JP2009283947A - Method and apparatus for data analysis - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and an apparatus for data analysis provided with an appropriate test system by configuring characteristics of a manufacturing process to automatically identify them in accordance with a component based on test data of the component. <P>SOLUTION: This method and this apparatus for data analysis are configured to automatically identify a characteristic of an assembly process in accordance with components based on the test data for the components. For instance, the apparatus is a test system provided with: a tester configured to test a set of components and generate test data for the set of components, wherein the components are manufactured based on a manufacturing process; and a diagnostic system configured to receive the test data from the tester, and identify the characteristic of the manufacturing process of the components by automatically analyzing the test data. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

(Cross-reference to related applications)
This application is entitled “METHODS AND APPARATUS FOR DATA SMOOTHING”, which claims the benefit of US Provisional Patent Application No. 60 / 293,577 (filed May 24, 2001) with the title “METHODS AND APPARATUS FOR DATA SMOOTHING”. US patent application Ser. No. 09 / 872,195 (filed May 31, 2001) entitled “METHODS AND APPARATUS FOR SEMICONDUCTOR TESTING” US patent application Ser. No. 10 / 154,627 (May 2002) Patent application No. 10 / 367,355 (February 2003) whose title is "METHODS AND APPARATUS FOR DATA ANALYSIS" 4), the title is “METHODS AND APPARATUS FOR TEST DATA CONTROL AND ANALYSIS”, US Provisional Patent Application No. 60 / 295,188 (filed May 31, 2001), and the title is “METHODS AND APPARATUS FOR TEST”. CIP with US Provisional Patent Application No. 60 / 374,328 (filed on April 21, 2002) of “PROGRAM ANALYSIS AND ENHANCEMENT” with title “DEVICE INDEPENDENT WAFERMAP”
Claims the benefit of ANALYSIS, US Provisional Patent Application No. 60 / 483,003, filed June 27, 2003. This application uses the disclosure of each of the above applications as a reference. However, this disclosure should be given priority to the extent that it conflicts with the referenced application.

(Field of Invention)
The present invention relates to data analysis.

  Semiconductor companies test components to make sure that they work properly. Test data not only measures whether a component functions properly, but can also indicate deficiencies in the manufacturing process. Therefore, many semiconductor companies can analyze data collected from a variety of different components to identify and correct problems. For example, a company can collect test data for composite chips on each wafer between various different lots. Test data can be collected from a variety of sources such as parametric electrical tests, optical inspection, scanning electron microscopy, energy dispersive x-ray spectroscopy, focused ion beam processes for defect analysis and fault isolation. Analyze this data to identify or classify user-defined “good parts” to identify patterns of common defects or defects, or to identify parts that may present quality and performance challenges be able to. Thereafter, steps may be taken to correct the problem. Tests are generally performed prior to device packaging (wafer level) and upon completion of assembly (final test).

  Collecting and analyzing test data is expensive and time consuming. The automatic tester gives a signal to the component and reads the corresponding output signal. The output signal may be analyzed to determine whether the component is operating properly. Each tester generates a large amount of data. For example, each tester can perform 200 tests on one component, and each of those tests can be repeated 10 times. As a result, testing on one component yields 2000 results. Each tester is testing more than 100 components per hour, and some testers get connected to the server, so a huge amount of data should be stored. In addition, for data processing, the server typically stores test data in a database so that the data can be easily manipulated and analyzed. However, storing in a conventional database requires additional storage capacity in addition to the time to organize and store the data. In addition, obtaining test data requires a complex and precise process. The test engineer prepares a test program that generates an output signal to the component and commands the tester to receive the output signal. Trying to ensure all and proper behavior in a component tends to make the program very complex. As a result, test programs for reasonably complex integrated circuits produce many tests and results. Program preparation requires extensive design and refinement to reach a satisfactory solution, and program optimization, such as eliminating duplicate testing, otherwise minimizing testing time, etc. For that, further effort is needed.

  Analyzing the collected data is also difficult. Depending on the amount of data, considerable processing power and processing time may be required. As a result, data is typically not analyzed during product runtime, but instead is typically analyzed between test runs or during other processing. To alleviate these burdens, some companies only sample data from testers and discard the rest. However, unless all data is analyzed, results analysis cannot be performed completely and cannot be accurate. As a result, the test results cannot be fully understood by sampling.

  Also, even if a series of all test data generated by a tester is retained, there is a huge amount of data, making it difficult to analyze data and extract important results. Data may include important information about devices, test processes, and manufacturing processes that can be used to improve products, reliability, and testing. However, considering the amount of data, it is difficult to isolate information and present it to users and other systems.

  In addition, many of the data interpretations are performed manually by engineers who review data based on experience and familiarity with the manufacturing and testing processes and make deductions for testing and manufacturing processes. Although manual analysis is often effective, technicians understand the manufacturing and testing systems separately and therefore tend to have different subjective results based on the same data. When skilled personnel leave the company or become unable to work for other reasons, there is a problem that it is not easy to transfer knowledge and understanding of the manufacturing and test systems and interpretation of test data to other personnel.

  Methods and apparatus for data analysis according to various aspects of the present invention are configured to automatically identify features of a manufacturing process to a component based on component test data.

  A more complete understanding of the invention can be obtained by reference to the detailed description and claims taken in conjunction with the accompanying exemplary drawings. The drawings do not have to be the same size. Similar reference numbers refer to similar elements in the figures.

The elements in the figures are simply and clearly illustrated and are not necessarily drawn to correspond to individual dimensions. For example, connections and steps performed by some elements in the figures may be exaggerated or omitted as compared to other elements that help enhance the understanding of embodiments of the present invention.
(Item 1)
A tester configured to test a set of components and generate test data for the set of components, wherein the component is manufactured based on a manufacturing process;
A diagnostic system configured to identify characteristics of the manufacturing process of the component by receiving the test data from the tester and automatically analyzing the test data;
A test system comprising:
(Item 2)
The test system according to item 1, wherein the test data includes at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and abnormality data. .
(Item 3)
2. The test system of item 1, wherein the diagnostic system is configured to provide a corrective action plan based on the identified characteristics.
(Item 4)
The test system of claim 1, wherein the diagnostic system includes a pattern recognition system configured to recognize a pattern in the test data.
(Item 5)
5. The test system of item 4, wherein the pattern recognition system is configured to compare the recognized pattern with a known pattern associated with the feature.
(Item 6)
5. The test system of item 4, wherein the pattern recognition system includes an intelligent system configured to automatically learn additional patterns based on the recognized pattern.
(Item 7)
5. The test system of item 4, wherein the pattern recognition system includes a classifier configured to classify the recognized pattern according to a known pattern.
(Item 8)
8. A test system according to item 7, wherein the classifier includes a neural network.
(Item 9)
9. The test system according to item 8, wherein the neural network includes a radial basis function network.
(Item 10)
5. The test system of item 4, wherein the pattern recognition system includes a feature extractor configured to extract features from the test data associated with the pattern.
(Item 11)
11. The test system of item 10, wherein the feature extractor calculates at least one of a mass, a center of gravity, a cross-sectional primary moment, and a Hu moment based on the test data.
(Item 12)
The feature extractor is configured to extract at least two features from the test data, and the pattern recognition system further includes a feature extractor configured to select fewer than all features for analysis. The test system according to Item 10.
(Item 13)
13. The test system of item 12, wherein the feature extractor operates in conjunction with a genetic algorithm.
(Item 14)
A test data analysis system that analyzes test data for a set of components that are manufactured and tested using a manufacturing process,
A memory for storing the test data;
A diagnostic system having access to the memory and configured to identify characteristics of the manufacturing process based on the test data;
A test data analysis system comprising:
(Item 15)
Item 15. The test data analysis according to Item 14, wherein the test data includes at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and abnormality data. system.
(Item 16)
15. The test data analysis system of item 14, wherein the diagnostic system is configured to provide a corrective action plan based on the identified characteristics.
(Item 17)
Item 15. The test data analysis system of item 14, wherein the diagnostic system includes a pattern recognition system configured to recognize a pattern of the test data.
(Item 18)
18. The test data analysis system of item 17, wherein the pattern recognition system is configured to compare the recognized pattern with a known pattern associated with the feature.
(Item 19)
18. The test data analysis system of item 17, wherein the pattern recognition system includes an intelligent system configured to automatically learn additional patterns based on the recognized patterns.
(Item 20)
18. The test data analysis system of item 17, wherein the pattern recognition system includes a classifier configured to classify recognized patterns according to a known pattern.
(Item 21)
21. The test data analysis system according to item 20, wherein the classifier includes a neural network.
(Item 22)
Item 22. The test data analysis system according to Item 21, wherein the neural network includes a radial basis function network.
(Item 23)
18. The test data analysis system of item 17, wherein the pattern recognition system includes a feature extractor configured to extract features from the test data associated with the pattern.
(Item 24)
24. The test data analysis system according to item 23, wherein the feature extractor calculates at least one of a mass, a center of gravity, a cross-sectional primary moment, and a Hu moment based on the test data.
(Item 25)
The feature extractor is configured to extract at least two features from the test data, and the pattern recognition system includes a feature extractor configured to select fewer than all features for analysis. 24. The test data analysis system according to item 23, further including:
(Item 26)
26. The test data analysis system of item 25, wherein the feature extractor operates in conjunction with a genetic algorithm.
(Item 27)
A computer-implemented test method for testing a component manufactured and tested according to a manufacturing process comprising:
Obtaining test data for the component;
Automatically identifying features of the manufacturing process based on the test data;
Including the method.
(Item 28)
28. The component of item 27, wherein the test data includes at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and abnormal data. Computer-implemented testing method for testing.
(Item 29)
28. A computer-implemented testing method for testing a component according to item 27, further comprising providing a corrective action plan based on the identified characteristics.
(Item 30)
28. The computer-implemented test method for testing a component according to item 27, wherein automatically identifying the feature comprises recognizing a pattern of the test data.
(Item 31)
The computer-implemented testing method for testing a component of claim 30, wherein automatically identifying the feature further comprises comparing the recognized pattern to a known pattern associated with the feature. .
(Item 32)
The computer-implemented testing method for testing a component according to item 30, further comprising automatically learning additional patterns based on the recognized pattern.
(Item 33)
The computer-implemented test method for testing a component according to item 30, wherein automatically identifying the feature comprises classifying a recognized pattern according to a known pattern.
(Item 34)
34. A computer-implemented test method for testing a component according to item 33, wherein classifying the recognized pattern is performed by a neural network.
(Item 35)
35. A computer-implemented test method for testing a component according to item 34, wherein the neural network includes a radial basis function network.
(Item 36)
28. A computer-implemented test method for testing a component according to item 27, wherein automatically identifying the feature comprises extracting a feature from the test data in association with the recognized pattern.
(Item 37)
The computer-implemented test method for testing a component according to item 36, wherein the features include at least one of a mass based on the test data, a center of gravity, a cross-sectional first moment, and a Hu moment.
(Item 38)
The computer-implemented testing method for testing a component according to item 36, wherein automatically identifying the feature further comprises selecting the feature from a plurality of features for analysis.
(Item 39)
A medium for storing instructions executable by a machine,
Obtaining test data for the component;
Automatically identifying features of the manufacturing process based on the test data;
A medium storing instructions for causing the machine to execute a test data analysis method including:
(Item 40)
The instruction according to item 39, wherein the test data includes at least one of electronic wafer sort data, data derived from electronic wafer sort data, electrical test data, bin map data, and abnormality data. Medium to store.
(Item 41)
40. The medium for storing instructions of item 39, wherein the method of analyzing further comprises providing a corrective action plan based on the identified characteristics.
(Item 42)
40. A medium for storing instructions according to item 39, wherein automatically identifying the feature comprises recognizing a pattern of the test data.
(Item 43)
45. The medium for storing instructions of item 42, wherein automatically identifying the feature further comprises comparing the recognized pattern to a known pattern associated with the feature.
(Item 44)
43. The medium for storing instructions of item 42, wherein the method of analyzing further comprises automatically learning additional patterns based on the recognized pattern.
(Item 45)
43. A medium for storing instructions according to item 42, wherein automatically identifying the feature comprises classifying a recognized pattern based on a known pattern.
(Item 46)
46. The medium for storing instructions according to item 45, wherein classifying the recognized pattern is performed by a neural network.
(Item 47)
47. A medium for storing instructions according to item 46, wherein the neural network includes a radial basis function network.
(Item 48)
40. The medium for storing instructions of item 39, wherein automatically identifying the feature comprises extracting a feature from the test data in association with the recognized pattern.
(Item 49)
49. A medium for storing instructions according to item 48, wherein the characteristics include at least one of a mass based on the test data, a center of gravity, a cross-sectional first moment, and a Hu moment.
(Item 50)
49. The medium for storing instructions according to item 48, wherein automatically identifying the feature further comprises selecting the feature from a plurality of features for analysis.

1 is a block diagram illustrating a test system with components of various aspects of the invention and associated functionality. FIG. FIG. 2 is a block diagram illustrating elements for operating a test system. Fig. 3 illustrates a flow chart of configuration elements. Fig. 4 illustrates a flowchart of supplemental data analysis elements. Fig. 4 illustrates a flowchart of supplemental data analysis elements. Fig. 4 illustrates a flowchart of supplemental data analysis elements. FIG. 3 illustrates various sections and sectioning techniques of a wafer. FIG. 6 further illustrates a flowchart of supplemental data analysis elements. FIG. 6 further illustrates a flowchart of supplemental data analysis elements. Fig. 4 illustrates a flowchart of output elements. 6 is a flowchart illustrating the operation of an exemplary data smoothing system in accordance with various aspects of the present invention. FIG. 6 is a plot showing test data for testing a plurality of components. FIG. 2 is a description of a wafer having a composite device and a resistance profile of the wafer. FIG. 11 is a graph of resistance versus population of resistors for various devices of the wafer shown in FIG. FIG. 11 is an overall plot showing original test data and anomaly detection brakes for the various devices shown in FIG. 10. FIG. 11 is a detailed plot showing original test data and anomaly detection brakes for the various devices shown in FIG. 10. 6 is a flowchart illustrating a combined analysis process according to various aspects of the present invention. FIG. 3 illustrates exemplary data point positions on three exemplary wafers. FIG. 5 is a flowchart associated with a cumulative squared complex data analysis process. FIG. FIG. 5 is a flowchart associated with a cumulative squared complex data analysis process. FIG. 4 is a table associated with a cumulative squared composite data analysis process. It is a figure which shows the exclusion zone prescribed | regulated on the wafer. Fig. 6 is a flowchart illustrating a proximity weighting process. Fig. 6 is a flowchart illustrating a proximity weighting process. It is a figure which shows a series of data points on the assumption of proximity weighting. It is a figure which shows a cluster detection and a filtering method. It is a figure which shows a series of clusters on a wafer on the assumption of detection and filtering. FIG. 6 illustrates a series of data points that are merged using an absolute merge process. FIG. 6 illustrates a series of data points that are merged using a duplicate merge process. FIG. 6 illustrates a series of data points that are merged using a percentage overlap merge process. FIG. 6 illustrates a series of data points that are merged using a percentage overlap merge process. FIG. 2 is a block diagram illustrating a system for identifying process characteristics using test data. It is a block diagram which shows a diagnostic system. It is a flowchart which shows a classification process. It is a figure which shows the pattern filtering method. It is a figure which shows a neutral network.

Detailed Description of Exemplary Embodiments
The present invention may be described in terms of functional block components and various process steps. Such functional blocks and processes can be realized by a plurality of hardware or software components configured to perform a specific function. For example, the present invention can use various testers, processors, storage systems, processes, and algorithms such as statistical engines, storage elements, signal processing elements, neutral networks, pattern analyzers, logic elements, programs, etc. Various functions can be performed under the control of one or more testers, microprocessors, or other control devices. Further, the present invention can be implemented in conjunction with any number of test environments, and each system described is only one exemplary application of the present invention. Furthermore, the present invention can use any number of conventional techniques such as data analysis, component linking, data processing, component handling, and the like.

  Referring to FIG. 1, methods and apparatus in accordance with various aspects of the present invention operate in conjunction with a test system 100 having a tester 102, such as an automated test equipment (ATE) for testing semiconductors. In the present embodiment, the test system 100 includes a tester 102 and a computer system 108. The test system 100 may be configured to test a component 106, such as a semiconductor device on a wafer, a circuit board, a package device, or other electrical or optical system. In this embodiment, component 106 includes a plurality of integrated circuit molds or packaged integrated circuits or devices formed on a wafer. The component 106 is created using a manufacturing process, which can include any manufacturing process that is compatible to create the component 106, and can include any process that is compatible to test the operation of the component 106. Possible testing processes may be included.

  Tester 102 suitably includes test equipment that tests component 106 and generates output data associated with the test, and may include multiple machines or other data sources. Tester 102 can include a conventional automatic tester, such as a tester made by Teradyne, and operates properly in conjunction with other equipment that facilitates testing. The tester 102 can be selected or set according to the particular component 106 to be tested and / or other suitable criteria.

  The tester 102 is a computer for programming the tester 102, loading and / or executing the test program, collecting data, providing instructions to the tester 102, analyzing test data, controlling tester parameters, etc. It can operate in conjunction with the system 108. In this embodiment, the computer system 108 receives tester data from the tester 102 and performs various data analysis functions of the tester 102 individually. The computer system 108 may implement a statistical institution that analyzes data from the tester 102 simultaneously with a diagnostic system 216 that identifies potential problems in the manufacturing and / or testing process based on the test data. The computer system 108 may include a separate computer such as a personal computer or workstation connected to or networked to the tester 102 so that signals can be exchanged with the tester 102. In alternative embodiments, the computer system 108 may be omitted from, or integrated with, other components of the test system 100, and various functions performed by other components, such as the tester 102 or network connected elements. Can be done.

  In the exemplary system, computer system 108 includes a processor 110 and a memory 112. The processor 110 includes a compatible processor that works in conjunction with a compatible operating system, such as Windows® XP, Unix®, Linux, or the like, such as a conventional Intel, Motorola, Advanced Micro Devices processor, or the like. Similarly, the memory 112 may include any suitable memory that is accessible to the processor 110, such as random access memory (RAM) or other storage system adapted to store data. In particular, the memory 112 of the present system includes a high speed access memory for storing and receiving information, and is appropriately configured with sufficient capacity to facilitate the operation of the computer 108.

  In the present embodiment, the memory 112 has a capacity for storing output results received from the tester 102 and facilitating analysis of output test data. Memory 112 is configured for high-speed storage and test data retrieval for analysis. In various embodiments, the memory 112 is configured to store elements of a dynamic data log that appropriately include a set of information selected by the test system 100 and / or an operator according to an analysis based on selected criteria and test results. Yes.

  For example, the memory 112 suitably stores a component identifier, such as an xy coordinate corresponding to the position on the wafer map of the wafer under test of the component 106, for each component 106. Each xy coordinate in memory 112 may be associated with a particular component 106 at the corresponding xy coordinate on the wafer map. Each component identifier has one or more fields, each field being a specific test performed on the component 106 at the corresponding xy coordinate on the wafer, statistics associated with the corresponding component 106, or others, for example. It corresponds to the relationship data of. The memory 112 may be configured to contain data identified as desired by the user according to any criteria or rules.

  Computer 108 in this embodiment also suitably includes access to a storage system, such as other memory (or a portion of memory 112), a hard drive array, an optical storage system, or other suitable storage system. The storage system may be local, such as a hard drive provided for computer 108 or tester 102, or remote, such as a hard drive array associated with a server to which test system 100 is connected. The storage system can store programs and / or data used by the computer 108 or other components of the test system 100. In this embodiment, the storage system includes a database 114 that is available via a remote server 116 including, for example, a main manufacturing server for a manufacturing facility. Database 114 stores tester information such as test system 100 and its components, test programs, tester data files for operating downloadable test system 100, etc., master data files. In addition, the storage system can include a complete tester data file, such as historical tester data retained for analysis.

  Test system 100 may include auxiliary equipment that facilitates testing of component 106. For example, the present test system 100 includes a device interface 104, such as a conventional device interface board and / or device handler or tester, for handling the component 106 and providing an interface between the component 106 and the tester 102. The test system 100 can include other components, equipment, software, etc. to facilitate testing of the component 106 in accordance with the particular configuration, application, environment, or other relevant factors of the test system 100. Or can be connected to them. For example, in this embodiment, the test system 100 is connected to a suitable communication medium such as a local area network, an intranet, or a global network such as the Internet, for communicating information to other systems, such as a remote server 116. ing.

  The test system 100 can include one or more testers 102 and one or more computers 108. For example, one computer 108 may be connected to an appropriate number, for example, 20 or more testers 102, according to various factors such as system throughput and computer 108 configuration. Further, the computer 108 can be separated from or integrated into the tester 102 using, for example, one or more processors, memory, clock circuitry, etc. provided in the tester 102 itself. Also, various functions can be performed by different computers. For example, a first computer may perform various pre-analysis tasks, several computers may receive the data and perform data analysis, and other computer suites may perform dynamic data logging and / or other output analysis And prepare reports.

  Test system 100 tests component 106 in accordance with various aspects of the present invention and provides advanced analysis and test results. For example, advanced analysis can identify false, suspicious or abnormal results, repeated tests, and / or tests with a relatively high probability of failure. The test system 100 can also analyze multiple sets of data, such as data taken from multiple wafers or multiple wafers, to generate composite data based on multiple data sets. Various data may be used in the test system 100 for manufacturing, testing, and / or other processes such as problems, inefficiencies, potential hazards, instabilities, or other aspects that may be identified by test data. Features can also be diagnosed. Operators, such as product design engineers, test engineers, manufacturing engineers, device engineers, or other personnel who use test data and analysis, then use the results to validate the test system 100 and / or manufacturing system. And / or improvements and component 106 can be classified.

  The test system 100 performs an advanced test process for testing the component 106 and collecting and analyzing test data in accordance with various aspects of the present invention. The test system 100 works in conjunction with a software application executed by the computer 108. Referring to FIG. 2, the software application of this embodiment includes a plurality of elements for implementing an advanced testing process, including a configuration element 202, a supplemental data analysis element 206, and an output element 208. The test system 100 can also include a composite analysis element 214 for performing data analysis from more than one data set. Further, the test system may include a diagnostic system 216 for identifying features and potential problems using test data.

  Each of the elements 202, 206, 208, 214, 216 suitably comprises software modules that operate on the computer 108 to perform various tasks. In general, configuration element 202 prepares test system 100 for testing and analysis. In supplemental data analysis element 206, the output test data from tester 102 is appropriately automatically analyzed during runtime to generate supplemental test data. The supplemental test data is then communicated to an operator or other system such as composite analysis element 214, diagnostic system 216, and / or output element 208.

  Configuration element 202 configures test system 100 to test component 106 and analyze test data. The test system 100 appropriately uses a set of predetermined initial parameters and information from the operator who sets up the test system 100 as needed. The test system 100 is initially properly configured with predetermined or default parameters to minimize operator intervention in the test system 100. Adjustment for setting by the operator can be performed using the computer 108 as necessary. Referring to FIG. 3, the exemplary configuration process 300 implemented by the configuration element 202 begins with an initialization procedure (step 302) that sets the computer 108 to an initial state. Configuration element 202 then obtains application configuration information for computer 108 and tester 102, eg, from database 114 (step 304). For example, the configuration element 202 can access a master configuration file for an advanced testing process and / or a tool configuration file associated with the tester 102. The master configuration file may include data relating to the proper configuration of the computer 108 and other components of the test system 100 to perform an advanced test process. Similarly, the tool configuration file contains data relating to the tester 102 configuration, eg, connection, directory, IP address, tester node identifier, manufacturer, flag, proper identifier, or any other accompanying information for the tester 102. Include appropriately.

  The configuration element 202 can then configure the test system 100 according to data contained in the master configuration file and / or the tool configuration file (step 306). Configuration element 202 also uses the configuration data to retrieve tester 102 identifiers for related data, such as a logistics instance representing tester data, using related information from database 114, such as tester 102. (Step 308). The information of the test system 100 also suitably includes one or more default parameters that can be accepted, rejected or adjusted by the operator. For example, the information of the test system 100 may include international statistical process management (SPC) rules that are presented to the operator at the time of installation, configuration, power enhancement, or other appropriate time that requires approval and / or modification. And may include purposes. The test system 100 information also includes a default wafer map or other files appropriately configured for each product, wafer, component 106, or other item that may or may be affected by the test system 100. be able to. Configuration algorithms, parameters, and other criteria can be stored in a recipe file for easy access, interaction with specific products and / or tests, and traceability.

  When the initial configuration process is completed, the test system 100 starts a test run according to a test program, for example, in conjunction with a conventional test series. The tester 102 properly executes the test program, provides a signal to the coupling portion of the component 106, and reads output test data from the component 106. The tester 102 can perform multiple tests on each component 106 on the wafer or the wafer itself, and each test can be repeated several times on the same component 106. The tests may include appropriate tests such as, but not limited to, continuity, supply current, leakage current, parametric static, parametric dynamic, and functional and durability tests. Test data from tester 102 is saved for quick access and supplemental analysis when the test data is obtained. Data can also be stored in long-term memory for subsequent analysis and use.

  Each test produces at least one result for at least one component. Referring to FIG. 9, an exemplary series of test results when performing a test on a plurality of components includes a first set of test results that have statistically similar values and values that deviate from the first set. A second set of test results characterized by: Each test result can be compared to the upper and lower limits of the test. If a particular result for a component exceeds any limit value, that component can be classified as a “bad part” or can be classified according to tests and / or test results.

  Some of the test results in the second set that deviate from the first set may exceed the control limits, but not others. For this purpose, those test results that are out of the first set but do not exceed the control limits or are not successfully detected are considered “abnormal”. Anomalies in test results can be identified and analyzed for any suitable purpose, such as identifying potentially unreliable components. Anomalies can also be used to identify various potential problems and / or improvements in testing and manufacturing processes.

  As tester 102 generates test results, output test data for each component, test, and iteration is stored by tester 102 in a tester data file. The output test data received from each component 106 is analyzed by the tester 102 to classify the performance of that component 106 into, for example, a specific bin classification by comparing the upper and lower test limits, and the classification results are also included. Saved in tester data file. The tester data file can also include additional information such as logic data and test program identification data. The tester data file is then provided to the computer 108 in an output file such as standard tester data format (STDF) and stored in memory. The tester data file can also be stored in a storage system, such as by composite analysis element 214, for long-term storage for later analysis.

  As the computer 108 receives the tester data file, the supplemental data analysis element 206 analyzes the data to provide advanced output results. The supplemental data analysis element 206 can provide an appropriate analysis of the tester data to achieve the fit purpose. For example, the supplemental data analysis element 206 can implement a statistical agency to analyze output test data at runtime and identify data and data characteristics relevant to the operator. The identified data and features can be preserved, but unidentified data can be processed such as abandonment.

  The supplemental data analysis element 206 may calculate statistical numbers according to, for example, the data and the set of statistical configuration data. Statistical configuration data may require any suitable type of analysis, such as statistical process control, anomaly identification and classification, signature analysis, and data correlation, as required by test system 100 and / or the operator. it can. In addition, supplemental data analysis element 206 suitably performs the analysis during runtime, ie, within seconds or minutes of test data generation. The supplemental data analysis element 206 can also perform automated analysis with minimal operator and / or test technician intervention.

  In the test system 100, after the computer 108 receives and stores the tester data file, the supplemental data analysis element 206 prepares the computer 108 for analysis of the output data to facilitate generation of supplemental data and preparation of the output report. To perform various preparatory tasks. 4A-C, in this embodiment, the supplemental data analysis element 206 first copies the tester data file to the tool input directory associated with the appropriate tester 102 (step 402). The supplemental data analysis element 206 searches the configuration data to arrange the computer 108 for supplemental analysis of the output test data.

  The configuration data suitably includes a series of logical data that can be retrieved from the tester data file (step 404). The supplemental data analysis element 206 also creates a logical reference (step 406). The logical reference can include tester 102 information, such as tester 102 information calculated from the tool configuration file. Logical references are also given an identification.

  The configuration data can also include an identifier indicating the test program that generated the output test data. The test program can be identified in any suitable manner, such as looking up in the database 114 in conjunction with the tester 102 (step 408) or reading from the master configuration file. If the test program identification cannot be established (step 410), a test program identification can be created and associated with the tester identification (step 412).

  The configuration data further identifies the wafer in the test run that is processed by the supplemental data analysis element 206, which is less than the entire wafer. In this embodiment, supplemental data analysis element 206 accesses a file indicating the wafer to be analyzed (step 414). If no instruction is given, the computer 108 suitably defaults to analyze all wafers in the test run.

  If the wafer for the test data file being performed is analyzed (step 416), the supplemental data analysis element 206 proceeds with supplemental data analysis on the test data file for the wafer. Otherwise, the supplemental data analysis element 206 waits for or accesses the next data file (step 418).

  The supplemental data analysis element 206 can establish one or more section groups to be analyzed for the various wafers being tested (step 420). To identify the appropriate section group that provides the output test data, the supplemental data analysis element 206 appropriately identifies the appropriate section group definition, for example according to the test program and / or tester identification. Each section group includes one or more section arrays, and each section array includes one or more sections of the same section type.

  Section types include various types of component 106 groups that are in place on a wafer. For example, referring to FIG. 5, a section type may include row 502, column 504, stepper field 506, annular band 508, radial zone 510, quadrant 512, or any other desired grouping of components. Different section types can be used according to component configuration, such as component processing order, tube sections, and the like. Such groups of components 106 are analyzed together, for example, to identify common defects or features that may be associated with the group. For example, if a particular portion of the wafer does not transfer heat to other portions of the wafer, test data for a particular group of components 106 may reflect common features or defects associated with uneven heating of the wafer.

  In identifying the section group for the tester data file in use, the supplemental data analysis element 206 further retrieves relevant configuration data such as control limits and flags for the test program and / or tester 102. Is enabled (step 422). In particular, the supplemental data analysis element 206 suitably retrieves a series of desirable statistics or calculations associated with each section arrangement within the section group (step 423). Desired statistics and calculations can be specified in any manner, such as by an operator or retrieved from a file. Further, supplemental data analysis element 206 identifies one or more signature analysis algorithms for each associated section type or other suitable variation associated with the wafer (step 424) and retrieves the signature algorithm from database 114. You can also.

  All configuration data can be provided by default or by automatic access by configuration element 202 or supplemental data analysis element 206. Furthermore, the configuration element 202 and the supplemental data analysis element 206 of this embodiment allow the operator to appropriately change the configuration data according to the operator's intention or the requirements of the test system 100. When configuration data is selected, the configuration data relates to relevant criteria and can be saved as default configuration data for future use. For example, if an operator selects a section group for a particular type of component 106, the computer 108 will automatically use the same section group for such component 106 unless otherwise ordered by the operator. Can do.

  The supplemental data analysis element 206 can also configure and store tester data files and additional data. The supplemental data analysis element 206 appropriately allocates memory for stored data, such as a portion of the memory 112 (step 426). Allocating appropriate memory for all data stored by supplemental data analysis element 206, including output test data from tester data files, statistical data generated by supplemental data analysis element 206, control parameters, etc. Provided to. The amount of memory allocated is calculated according to, for example, the number of tests performed on the component 106, the number of section group arrays, management limits, statistical calculations performed by the supplemental data analysis element 206, and the like.

  When all the configuration data is ready to perform supplemental analysis and output test data is received, supplemental data analysis element 206 loads the associated test data into memory (step 428), and for that output test data, To perform a supplementary analysis. The supplemental data analysis element 206 can perform any number and type of data analysis according to the components 106, the configuration of the test system 100, operator requirements, or other relevant criteria. Supplemental data analysis element 206 divides sections for selected features that identify potentially defective components 106 and patterns, trends, or other features in output test data that may indicate manufacturing concerns and shortcomings. It may be configured to analyze.

  The supplemental data analysis element 206, for example, smooths the output test data, calculates and analyzes various statistics based on the output test data, and identifies data and / or components 106 corresponding to various criteria. The supplemental data analysis element 206 can also categorize and correlate output test data to provide information to the operator 106 and / or test technician associated with the component 106 or test system 100. For example, this supplemental data analysis element 206 performs output data correlation, eg, to identify potentially related or overlapping tests, and an outbreak analysis to identify tests with frequent anomalies. Can do.

  The supplemental data analysis element 206 can include a smoothing system to perform initial processing of the tester data and to help smooth the data and identify anomalies (step 429). The smoothing system can also identify important changes, trends, etc. in the data, which can be provided to the operator by output element 208. The smoothing system is appropriately implemented as a program that runs on the computer system 108, for example. The smoothing system suitably comprises multiple steps for smoothing the data according to various criteria. The first stage can include a basic smoothing process. Ancillary steps conditionally provide advanced tracking and / or further smoothing of the test data. The smoothing system first adjusts the initial value of the selected test data according to the first smoothing technique, and if at least one of the initial values and the first adjusted value satisfy the threshold value, the value is supplementarily set. It works properly by adjusting according to the second smoothing technique. The first smoothing technique helps to smooth the data. The second smoothing technique is also useful for smoothing data and / or improving data tracking, but the method is different from the first smoothing technique. Further, the threshold can include any suitable criteria to determine whether supplemental smoothing is applied. The smoothing system appropriately compares a plurality of previously adjusted data and a plurality of previous original data to generate a comparison result, and adjusts the value of the selected data according to whether the comparison result satisfies a threshold. Therefore, the second smoothing technique is applied to the selected data. Further, the smoothing system appropriately calculates a predicted value of the selected data, and uses a third smoothing technique to adjust the value of the selected data according to whether the predicted value satisfies the second threshold. It can be applied to the selected data.

  Referring to FIG. 8, the first smoothed test data point is set appropriately as the first original test data point (step 802), and the smoothing system proceeds to the next original data point (step 804). ). Prior to performing a smoothing operation, the smoothing system first determines whether smoothing is appropriate for the data point, and if so, performs a basic smoothing operation on the data. Any criteria can be applied to determine whether smoothing is appropriate, such as following the number of data points received, the deviation of the data point value from the selected value, or comparing each data point value to a threshold. . In this embodiment, the smoothing system performs a threshold comparison. The threshold comparison determines whether data smoothing is appropriate. If appropriate, the initial smoothing process is suitably configured to advance the initial smoothing of the data.

More specifically, in this embodiment, the process starts with an initial original data point R ₀ , which is also designated as the first smoothed data point S ₀ . If further data points are received and analyzed, the difference between each original data point (R _n ) and the previous smoothed data point (S _n-1 ) is calculated and compared to a threshold (T ₁ ) (step 806). ). If the difference between the original data point R _n and the previously smoothed data point S _n−1 exceeds the threshold T ₁ , the exceeded threshold corresponds to a significant deviation from the smoothed data and the data changes Is considered to be commanding. Therefore, it is noted intersection occurrence threshold obtained data points S _n currently smoothing is set equal to the raw data points R _n (step 808). No smoothing is performed and processing proceeds to the next original data point.

If the difference between the raw data point and the preceding smoothed data point does not exceed the threshold T _1, the process computes the data points S _n in the same time the current smoothed early a smoothing process (step 810). The initial smoothing process provides basic data smoothing. For example, in the present embodiment, the basic smoothing process includes a conventional exponential smoothing process according to the following equation. _{S n = (R n -S n} -1) * M 1 + S n-1. Where M ₁ is the selected smoothing factor, such as 0.2 or 0.3.

The initial smoothing process appropriately uses a relatively low coefficient M ₁ to provide significant smoothing on the data. However, the initial smoothing process and coefficients are in accordance with any criteria and set any method, and also the application of the smoothing system, the data being processed, the requirements and performance of the smoothing system, and / or others You may choose according to any criteria. For example, the initial smoothing process can be random, random walk, moving average, simple index, linear index, seasonal exponent, exponential weighted moving average, or any other suitable smoothing data at the beginning. Types of smoothing can be used.

  The data can be further analyzed for smoothing and / or according to smoothing. Supplemental smoothing can be performed on the data to enhance the smoothing of the data and / or to improve the tracking of the smoothed data to the original data. There are also a number of supplemental smoothing steps that can be applied if appropriate. The various stages can be independent, interdependent, or complementary. The data can also be analyzed to determine whether supplemental smoothing is appropriate.

  In this embodiment, the data is analyzed to determine whether one or more additional smoothing steps are performed. The data is analyzed according to any suitable criteria to determine whether supplemental smoothing may be applied (step 812). For example, the smoothing system compares a plurality of adjusted data points and the original data points with the previous data, and substantially all of the previous adjusted data is in general relation (smaller, larger, equal To identify trends in the data by generating comparison results according to whether they share substantially all of the corresponding original data.

Smoothing system of the present embodiment, a selected number P ₂ of the original data points, compared with the equivalent number of smoothed data points. It exceeds smoothed data point values of P ₂ raw data points correspond all (or equivalent), or if all of the original data points (a or equivalent) of the corresponding smoothed data points less than the If so, the smoothing system can determine that the data is trending and should be tracked in more detail. Therefore, it is possible to focus on this occurrence and the smoothing applied to the data can change by applying supplemental smoothing. Conversely, if neither criterion is met, the data points currently being smoothed will remain as originally calculated and no associated supplemental data smoothing will be applied.

In this embodiment, the criteria for comparing the smoothed data with the original data is selected to identify trends in the data that the smoothed data may be delayed. Therefore, the number of points P _2, in order to alter the trends in the original data, can be selected according to the desired sensitivity of the system.

  Supplemental smoothing changes the overall smoothing effect according to the data analysis. Any suitable auxiliary smoothing can be applied to the data in order to smooth the data more effectively or to track trends in the data. For example, in this embodiment, if the data analysis indicates trends in the data to be tracked in more detail, supplemental smoothing is first applied so that the smoothed data tracks the original data in more detail. Applied to reduce the degree of smoothing (step 814).

In this embodiment, the degree of smoothing is reduced by recalculating the data point values currently being smoothed using the reduced degree of smoothing. Any suitable smoothing system can be used to track the data in more detail, or otherwise address the results of the data analysis. In the present embodiment, the smoothing process of another conventional index is applied to the data using a higher coefficient M _{2.
S n = (R n -S n} -1) * M 2 + S n-1.

The coefficients M ₁ and M ₂ can be selected according to the desired detection sensitivity of the system, both in the lack of trend (M ₁ ) and presence (M ₂ ) in the raw data. For example, the value of M ₁ may be higher than the value of M ₂ in various applications.

  Supplemental data smoothing can also include additional steps. The additional stage of data smoothing can similarly analyze the data in several ways to determine whether further data smoothing should be applied. Any number of stages and types of data smoothing can be applied or considered according to the data analysis.

For example, in this embodiment, the data can be analyzed and potentially smoothed to prevent noise, such as using a prediction process based on the slope or trend of the smoothed data. Smoothing system is based on the number P ₃ selected from the smoothed data points preceding the data points currently in smoothing, linear regression to calculate the slope according to the appropriate processing such as N-point center (step 816). In this embodiment, the data smoothing system uses a “least-squares fit to straight line” process to establish a slope of P ₃ preceding smoothed data points.

The smoothing system predicts the value of the data point currently being smoothed according to the calculated slope. The system then compares the difference between the previously calculated smoothed data point (S _n ) and the predicted value of the current smoothed data point to the range (R ₃ ) (step 818). If difference is greater than the range R _3, obtained subsequently be noted that generation, data points currently in the smoothing is not adjusted. If the difference is within the range R ₃ , then the data points that are currently being smoothed are the data points that are currently being smoothed (S _n ) and the data points that are currently being smoothed (S _n-pred ). the difference of M ₃ is a third multiplier multiplying the a is set equal to the predicted value difference added to the original numerical value indicating the data points currently smoothed (step 820). _{Formula: S n = (S n-} pred -S n) * M 3 + S n.

Thus, the data point currently being smoothed is set according to the modified difference between the original smoothed data point and the predicted data point, but reduced by a certain amount (if M ₃ is less than 1). Is done. Application of predictive smoothing tends to help reduce point-to-point noise detection sensitivity between relatively flat (or otherwise tilted) portions of the signal. If there is a significant change in the original data due to the limited application of predictive smoothing on the smoothed data points, that is, if the original data signal is not relatively flat, the calculated average based on the slope is It is certain that it will not affect the smoothed data.

  After data smoothing, supplemental data analysis element 206 can continue to further analyze the tester data. For example, supplemental data analysis element 206 can perform statistical process control (SPC) calculations and analysis based on output test data. More specifically, referring again to FIGS. 4A-C, supplemental data analysis element 206 can calculate and store desired statistics for a particular component, test, and / or section (step 430). . The statistics are any statistics that are valid for the operator or test system 100, including SPC figures, which may include mean, standard deviation, minimum, maximum, sum, total, Cp, Cpk, or any other suitable statistic. May be included.

  The supplemental data analysis element 206 signs signatures according to sections based on combinations of test results against sections and / or other data, eg, historical data, etc. to dynamically and automatically identify trends and exceptions in the data. The analysis is performed appropriately (step 442). Signature analysis identifies the signature and applies a weighing system appropriately set by the operator according to any suitable data such as test data or defect identification. Signature analysis can cumulatively identify trends and exceptions that may correspond to problem areas or other features of the wafer or manufacturing process. Signature analysis may be performed for any desired signature, such as noise peaks, waveform changes, mode shifts, and noise. In this embodiment, computer 108 suitably performs signature analysis on the output test data for each desired test in each desired section.

In this embodiment, signature analysis can be performed in conjunction with the smoothing process. When the smoothing process analyzes tester data, analysis results that indicate trends and exceptions in the data are stored as indications of changes in the data or as anomalies that may be important to the operator and / or test technician. For example, if a trend is indicated by comparison of a series of data in the smoothing process, the occurrence of the trend can be noted and stored. Similarly, if the data points in the data smoothing process exceeds the threshold value T _1, it is noted this occurrence can be saved for inclusion in the analysis and / or output reports after.

  For example, referring to FIGS. 6A-B, the signature analysis process 600 may first calculate a total number for a particular test data series and control limits associated with a particular section and test (step 602). The signature analysis process then applies the appropriate signature analysis algorithm to the data points (step 604). Signature analysis is performed for each desired signature algorithm and then for each section tested and analyzed. Errors, trend results, and signature results identified by signature analysis are also saved (step 606). The process is repeated for each of the signature algorithm (step 608), test (step 610), and section (step 612). Upon completion, supplemental data analysis element 206 records errors (step 614), trend results (step 616), signature results (step 618), and other desired data in the storage system.

  Upon identification of each relevant data point, such as anomalies and other important data identified by ancillary analysis, each relevant data point may be associated with a value that identifies the relevant feature (step 444). For example, each associated component or data point may be appropriately represented as a hexadecimal number and may be associated with a set of values corresponding to the results of the auxiliary analysis associated with that data point. Each value can act as a flag or other instruction indicating a particular feature. For example, if a particular data point completely fails a particular test, the first flag can be set in the corresponding hexadecimal value. Other flags may be set if a particular data point is the beginning of a trend in the data. Other values in hexadecimal can include information related to trends, such as trend duration in the data.

  The supplemental data analysis element 206 may be configured to classify and correlate data (step 446). For example, the supplemental data analysis element 206 can utilize the information to identify faults, abnormalities, trends, or other data features in hexadecimal numbers associated with the data points. The supplemental data analysis element 206 also suitably applies conventional correlation techniques to the data, eg, to identify potentially overlapping or related tests.

  The computer 108 may perform additional analysis functions on the generated statistics and output test data, such as, for example, automatic identification and classification of anomalies (step 432). Identify anomalies by appropriately analyzing each duplicated data according to the selected algorithm. If a particular algorithm is inappropriate for a set of data, the supplemental data analysis element 206 can be configured to automatically interrupt the analysis and select another algorithm.

  The supplemental data analysis element 206 can operate in any suitable way to specify anomalies, for example, comparing to selected values and / or according to the handling of data during the data smoothing process. For example, the anomaly identifier according to various aspects of the present invention initially measures the detection sensitivity for the anomaly automatically based on the selected statistical relationship for each associated data (step 434). Some of the statistical relationships described above are then compared to thresholds or other reference points, such as data mode, average or median, or combinations thereof, to define the associated anomaly limits. In this embodiment, the statistical relationship is measured with, for example, 1, 2, 3, and 6 data standard deviations to define different abnormal amplitudes (step 436). The output test data can then be compared to an abnormal limit value, and the output test data can be identified and classified as abnormal (step 438).

  The supplemental data analysis element 206 stores the resulting statistics and anomalies in an identifier, such as xy wafer map coordinates, associated with the memory and any such statistics and anomalies (step 440). The selected statistics, anomalies, and / or faults may trigger notification events such as sending electronic messages to the operator, turning on the light tower, stopping the tester 102, or notifying the server.

  In this embodiment, supplemental data analysis element 206 includes a scaling element 210 and an anomaly classification element 212. The scaling element 210 is configured to dynamically scale selected coefficients and other values according to the output test data. Anomaly classification element 212 is configured to identify and / or classify various anomalies in the data according to a selected algorithm.

  More specifically, the scaling element of this embodiment suitably uses various statistical relationships to dynamically scale the anomaly detection sensitivity and smooth the exponent for the noise filtering detection sensitivity. The scaling index is calculated appropriately by the scaling factor and used to modify the selected anomaly detection sensitivity value and the smoothing index. Any suitable criterion can be used for scaling, such as a fitting statistical relationship. For example, a statistical relationship sample indicating anomaly detection sensitivity scaling is defined as follows:

異常検出感度および平滑化指数スケーリングを示す他の統計的関係サンプルは以下のように定義される。

Other statistical relationship samples showing anomaly detection sensitivity and smoothing exponent scaling are defined as follows:

異常検出感度および平滑化指数スケーリングを示す他の統計的関係サンプルは以下のように定義される。

Other statistical relationship samples showing anomaly detection sensitivity and smoothing exponent scaling are defined as follows:

ここでσ＝データ標準偏差である。

Here, σ = data standard deviation.

  A statistical relationship sample showing smoothing exponential scaling used in multiple algorithms is

であり、ここでσ＝データ標準偏差、およびμ＝データ平均値である。

Where σ = data standard deviation and μ = data average.

  Other statistical relationship samples that show smoothing exponent scaling used in multiple algorithms are:

ここでσ＝データ標準偏差、およびμ＝データ平均値である。

Here, σ = data standard deviation and μ = data average value.

  The anomaly classification element 212 is suitably configured to identify and / or classify components 106, output test data, and analysis results, anomalies in the output test data according to any suitable algorithm. Anomaly classification element 212 can also identify and classify selected anomalies and components 106 according to test output test results and information generated by supplemental data analysis element 206. For example, anomaly classification element 212 may include user-defined criteria, user-defined good / bad distribution style evaluation, data classification related to tester data comparison, test set in-situ detection sensitivity requirements and analysis, tester output level analysis, Appropriately configured to categorize components 106 into critical / limit / good part categories in conjunction with dynamic wafer maps and / or test strip mapping for placement and dynamic retesting, or test program optimization analysis, etc. Yes. The anomaly classification element 212 can classify and characterize data according to conventional SPC control rules, such as Western Electric rules or Nelson rules.

  The abnormal classification element 212 appropriately classifies the data using a series of selected classification restriction calculation methods. Any suitable classification restriction calculation scheme can be used to characterize the data as required by the operator. The anomaly classifier 212 may, for example, output test data from 1, 2, 3, and 6 data standards that are statistically measured from selected thresholds, eg, thresholds such as data mean, mode, and / or median Classify abnormalities by comparing with values corresponding to deviations. Anomaly identification by this method helps to normalize any anomaly identified in any test, regardless of data amplitude and associated noise.

  Anomaly classification element 212 analyzes and correlates normalized anomalies and / or original data points based on user-defined rules. A sample user selectable method for part and pattern classification based on identified anomalies is as follows:

  Cumulative amplitude, cumulative total method:

。

.

  Classification rules:

。

.

  Cumulative amplitude square, cumulative total square method:

。

.

  Classification rules:

。

.

N-point method:
The actual numbers and logic rules used in the examples below can be customized by the end user for each scenario (test program, test node, tester, prober, handler, test settings, etc.). Σ in these examples is the σ associated with the data mean, mode and / or median based on the data standard deviation measured by the main statistical relationship.

補足のデータ分析要素２０６は、出力試験データおよび補足のデータ分析要素２０６により生成された情報の追加分析を実施するように構成されることができる。例えば、補足のデータ分析要素２０６は、高確率の故障または異常を有する試験を、例えば故障、異常、または特定の分類における異常の合計数または平均数を１つ以上の閾値と比較することによって、識別することができる。

The supplemental data analysis element 206 can be configured to perform additional analysis of the output test data and information generated by the supplemental data analysis element 206. For example, supplemental data analysis element 206 can compare a test with a high probability of failure or anomaly, for example, by comparing the total number or average number of failures, anomalies, or anomalies in a particular classification with one or more thresholds. Can be identified.

  The supplemental data analysis element 206 may identify similar or dissimilar trends, eg, compare cumulative totals, anomalies, and / or correlate anomalies between wafers or other data sets. Can be configured to correlate data from different tests. The supplemental data analysis element 206 can also analyze and correlate data from different tests to identify and classify potentially critical and / or critical and / or good parts on the wafer. Supplemental data analysis element 206 analyzes and correlates data from different tests to identify user-defined good and / or bad part patterns on a series of wafers for the purpose of reducing dynamic test times You can also.

  The supplemental data analysis element 206 is suitable to analyze and correlate data from different tests to identify user-defined related raw data for the purpose of dynamically compressing test data in memory. Configured. The supplemental data analysis element 206 can also analyze and correlate statistical exceptions and test node in-situation requirements and test data results of detection sensitivity analysis. Further, the supplemental data analysis element 206 can guide the test node output level analysis, for example, by identifying whether a particular test node can be measured improperly, or by creating an inappropriate result. Supplemental data analysis element 206 uses interrelated results and anomaly analysis to create additional data for use in the analysis to facilitate test program optimization, including but not limited to automatic identification of duplicate tests. The data can be further analyzed and correlated for purposes. The supplemental data analysis element 206 may identify critical experiments by periodically identifying, for example, failed or nearly failed tests, tests with little failure, and / or tests that exhibit very low Cpk. Is also configured.

  Supplemental data analysis may also provide for the identification of test sampling candidates, such as tests that rarely or never fail, or tests in which anomalies are never detected. Supplemental data analysis element 206 relates to correlation techniques, such as analysis and correlation of identified anomalies and / or other statistical exceptions, number of failures, criticality experiments, longest / shortest tests, or test failures Based on conventional correlation techniques with basic functionality issues, the best order test sequence identification may also be provided.

  Supplemental data analysis can also provide identification of parts that are critical, critical, and good, as defined by detection sensitivity parameters in the recipe configuration file. Part identification involves placement / classification and / or dynamic prober mapping of defective and good parts in a wafer prober prior to packaging and / or shipping of parts that may indicate reliability risks. Test time reduction can be provided. These parts can be identified in any suitable way, eg dynamically generated prober control maps (for dynamic mapping), wafer maps used for offline inking, test strip maps used for final test strip tests Can be shown and output as good and bad parts in the result file and / or database result table.

  Supplemental data analysis at the cell controller level helps to improve quality control in the prober, and as a result also improves final test output. Quality issues can also be identified during or after product runtime. In addition, supplemental data analysis and signature analysis help to improve the data quality provided to downstream and offline analysis tools by identifying anomalies, as well as test technicians or other personnel. For example, the computer 108 may include information on a wafer map that identifies a group of components with signature analysis indicating a failure in the manufacturing process. Thus, the signature analysis system can identify potential defective products that may not be detected using conventional test analysis.

  Referring now to FIG. 10, the array of semiconductor devices is located on the wafer. In this wafer, the general resistance of resistive components in semiconductor devices varies from wafer to wafer, for example due to uneven material placement or wafer handling. However, the resistance of any particular component may be within the administrative control of the test. For example, the target resistance for a particular resistive component may be 1000Ω +/− 10%. Near the end of the wafer, the resistance of most resistors approaches, but does not exceed, the standard distribution range of 900Ω to 1100Ω (FIG. 11).

  Components on the wafer may contain defects due to, for example, contaminants or imperfections in the manufacturing process. This defect increases the resistance of the resistor located near the low resistance wafer end to 1080 Ω. This resistance is well above the expected 1000 Ω for devices near the center of the wafer, but is well within the standard distribution range.

  Referring to FIGS. 12A-B, the original test data for each component can be represented graphically. The test data, in part, shows considerable variation due to resistivity changes between components on the wafer as the probe indexes the device rows or columns. Devices affected by defects are not easy to identify based on visual inspection of test data or comparison with test limits.

  When processing test data according to various aspects of the present invention, a device affected by a defect may be associated with an anomaly in the test data. Test data is mainly limited to a certain range of values. However, the data associated with the defect is different from the data of the peripheral components. Thus, the data shows deviations from values associated with peripheral components that are not defective. The anomaly classification element 212 can identify and classify anomalies according to the magnitude of deviations from the anomaly's surrounding data.

  The output element 208 collects data from the test system 100 as appropriate during runtime and provides output reports to a printer, database, operator interface, or other desired destination. Any format may be used to present an output report for use or later analysis, for example, image, numeric, text, print, or electronic format. The output element 208 can provide any selected content including output test data selected from the tester 102 and the results of supplemental data analysis.

  In this embodiment, the output element 208 suitably provides a data selection from the output test data specified by the operator through a dynamic dialog as well as supplemental data at the product runtime. Referring to FIG. 7, the output element 208 first reads the sampling range from the database 114 (step 702). This sampling range identifies predetermined information to be included in the output report. In this embodiment, the sampling range identifies the component 106 on the wafer that has been selected for inspection by the operator. The predetermined component can be selected according to any criteria, such as a circumferential zone, a radial zone, a random component, or an individual stepper field. The sampling range includes a set of xy coordinates corresponding to the location of a given component on the wafer or an identified portion of the component available in a batch.

  The output element 208 may also be configured to include information in the dynamic dialog related to the anomaly or other information generated or identified by the supplemental data analysis element (step 704). If so configured, an identifier representing each anomaly such as an xy coordinate is also assembled. The operator selected component and the coordinates representing the anomaly are merged into the dynamic dialog (step 706), which in this embodiment is in the form of the original tester data output format. The resulting merge of data into a dynamic dialog reduces the data storage requirements without compromising data integrity for the next customer analysis, thereby reducing the original data to basic statistics and reducing the original critical data value. Makes it easy to compress into tester data files. The output element 208 retrieves selected information, such as the original data and one or more data from the next data analysis element 206, each time it enters within the merged xy coordinate array of the dynamic dialog (steps). 708).

  The retrieved information is then stored appropriately in a suitable output report (step 710). Reports can be prepared in any suitable format and method. In this embodiment, the output report suitably includes a dynamic dialog having a wafer map showing selected components on the wafer and their classification. Further, the output element 208 can add wafer map data corresponding to anomalies in the wafer map of preselected components. The output element can also include only anomalies from the wafer map or batch as sampled output. The output report can also include a series of graphical representations of the data to highlight the occurrence of anomalies and correlations in the data. The output report may further include advice and attached data for the advice. For example, if two tests appear to generate the same set of faults and / or anomalies, the output report will include a suggestion that the tests are duplicates and omit one test from the test program I can advise you. The advice can include a graphical representation of data showing the same test results.

  The output report can be provided in any suitable manner, such as output to a local workstation, sent to a server, triggering an alarm, or any other suitable method (step 712). In one embodiment, the output report can be provided offline so that the output does not affect system operation or move to the main server. In this configuration, the computer 108 copies data files, performs analysis, and generates results, for example for demonstration or verification purposes.

  In addition to the supplemental analysis of data for each wafer, the test system 100 identifies patterns and trends for multiple data sets, such as using multiple wafers and / or lots, in accordance with various aspects of the present invention. Data analysis can also be performed to generate additional data. Composite analysis is suitably performed to identify selected features such as patterns or irregularities among multiple data sets. For example, multiple data sets can be analyzed to determine common features that can indicate patterns, trends, or irregularities for two or more data sets.

  Composite analysis can include any suitable analysis for the purpose of analyzing test data to determine common features between data sets and can be implemented in any suitable manner. For example, in the test system 100, the composite analysis element 214 performs a composite analysis of data derived from multiple wafers and / or lots. The test data for each wafer and lot or other group forms a data set. Composite analysis element 214 is suitably implemented as a software module that runs on computer 108. However, the composite analysis element 214 can be implemented in any suitable configuration, such as a hardware solution or a combined hardware and software solution. Further, the composite analysis element 214 can be performed on the test system computer 108 or a remote computer such as an independent workstation or a third party separate computer system. The composite analysis can be performed during runtime, following the generation of one or more complete data sets, or at the time of data collection including historical data generated long before the composite analysis.

  A composite analysis can use any data from two or more data sets. Composite analysis can receive multiple sets of input data for each data set, including raw data and filtered or smoothed data, such as by performing multiple configurations by a classification agency. Once received, the input data is appropriately filtered using a series of user-defined rules that can be defined as mathematical formulas, formulas, or any other criteria. The data is then analyzed to identify patterns or irregularities in the data. The composite data can also be merged with other data, such as the original data or the analyzed data, to produce an overall sophisticated data set. Composite analysis element 214 can then provide an appropriate output report that can be used to improve the testing process. For example, the output report can provide information related to issues in the manufacturing and / or testing process.

  In this system, the composite analysis element 214 analyzes the set of wafer data in conjunction with a user-based or other suitable process and spatial analysis to build and establish a composite map that shows significant patterns or trends. Composite analysis can receive multiple different data sets and / or composite maps per wafer set by performing multi-user configuration on the set of wafers.

  Referring to FIG. 13, in this embodiment operating in a semiconductor test environment, composite analysis element 214 receives data from a plurality of data sets, such as data from a plurality of wafers or lots (1310). The data can include any data that fits the analysis, such as raw data, filtered data, smoothed data, historical data from a previous test run, or data received from a tester during runtime. . In this embodiment, the composite analysis element 214 receives raw data and filtered data during runtime. The filtered data can include any data that fits the analysis, such as smoothed data and / or signature analysis data. In this embodiment, the composite analysis element 214 may be smoothed data, fault identification, anomaly identification, signature analysis data, and / or other data generated by the original data set and supplemental data analysis element 206, etc. Receive supplementary data.

  After receiving the original data and supplemental data, the composite analysis element 214 generates composite data for use in the analysis (1312). Composite data includes data representing information from one or more data sets. For example, the composite data may be the number of failures and / or anomalies for a particular test for which corresponding test data occurs in different data sets, such as data for components at the same or similar locations on different wafers or in different lots. Relevant summary information can be included. However, the composite data may be any suitable data, such as zones with anomalies or fault concentrations, data related to wafer locations that are generating multiple anomalies or faults, or other data derived from two or more data sets. Data can be included.

  Composite data is suitably generated by comparing data from different data sets to identify patterns and irregularities between data sets. For example, the composite data can be generated by an analysis engine configured to provide and analyze the composite data according to any suitable algorithm or process. In this embodiment, the composite analysis element 214 includes a proximity engine configured to generate one or more composite masks according to the data set. The composite analysis element 214 can also process the composite mask data, for example, to refine or enhance information in the composite mask data.

  In this embodiment, the proximity engine receives a plurality of data sets and performs spatial analysis on the data sets described above using any of files, memory structures, databases, or other data stores (1320), Output the result in the form of a composite mask. The proximity engine can generate composite mask data, such as a composite image for the entire data set, according to any suitable process or technique using any suitable method. In particular, the proximity engine may properly merge the composite data with the original data (1322) and generate an output report (1324) for use by a user or other system. The proximity engine must be configured to refine or enhance the complex mask data to be analyzed, such as by spatial analysis, data analysis to obtain repeated features in the data set, or removal of data that does not meet the selected criteria. You can also.

  In this embodiment, the proximity engine is configured to perform composite mask generation (1312), determine an exclusion zone (1314), perform proximity weighting (1316), and detect filter clusters (1318). You can also. The proximity engine may also provide proximity adjustments or other operations that use user-defined rules, criteria, thresholds, and priorities. The result of the analysis is a composite mask of the input data set that indicates the spatial trends and / or patterns that are found in a given data set. The proximity engine can use any suitable output method and medium including a file-based data store, such as memory structures, databases, other applications, and text or XML files for outputting composite masks. .

  The proximity engine can use any suitable technique for generating composite mask data including cumulative squares, N-point formulas, Western electrical rules, or other user-defined criteria or rules. In this embodiment, the composite mask data may be considered a comprehensive or “stacked” representation of multiple data sets. The proximity engine accumulates and analyzes data for corresponding data from multiple data sets to identify potential relationships or common features in the data for a particular set of corresponding data. The analyzed data may be any suitable data including original data, smoothed data, signature analysis data, and / or other suitable data.

  In this embodiment, the proximity engine analyzes data at corresponding positions on a plurality of wafers. Referring to FIG. 14, each wafer has a device at a corresponding location that can be specified using an appropriate identification system such as an x, y coordinate system. Thus, the proximity engine compares device data at corresponding locations or data points, such as locations 10, 12 shown in FIG. 14, to identify patterns in the composite set of data.

  The proximity engine of this embodiment uses at least one of two different techniques for generating composite mask data, cumulative squares, and formula-based methods. The proximity engine properly identifies the data of interest by comparing the data with a selected or calculated threshold. In one embodiment, the proximity engine compares data points at corresponding locations on various wafers and / or lots with thresholds to determine whether the data represents a potential pattern across the entire data set. The proximity engine compares each data with one or more threshold values selected in any suitable way, such as a predefined value, a value calculated based on current data, or a value calculated from historical data. .

  For example, the first embodiment of the proximity engine implements a cumulative square method to compare data with a threshold. In particular, referring to FIG. 15, the proximity engine properly selects the first data point (1510) in the first data set, such as the result of a specific test for a specific device on a specific wafer in a specific lot. (1512), the data point value is compared with a count threshold (1514). The threshold can include any suitable value and any type of threshold, such as a range, a lower limit, an upper limit, etc., and can be selected according to any suitable criteria. If the data point value exceeds the threshold, i.e. lower than the reference value, higher than the threshold, within the threshold, or a specific conditional relationship may exist, to generate a summary value corresponding to the data point The absolute count is increased (1516).

  The data point value can also be compared (1518) to a cumulative threshold. If the data point value exceeds the cumulative threshold, the data point value is added (1520) to the cumulative value of the data point to generate another summary value for that data point. The proximity engine repeats the process (1522) for all corresponding data points in all relevant data sets (1524), such as all wafers in the lot or other selected group of wafers. Other desirable tests or comparisons may also be performed on the data points and data sets.

  Once all of the relevant data points in the population have been processed, the proximity engine can calculate a value based on the selected data, such as data that exceeds a certain threshold. For example, the proximity engine may calculate (1526) a total cumulative threshold for each corresponding set of data based on the cumulative value of the associated data points. The total cumulative threshold can be calculated in any suitable manner, such as identifying corresponding data points associated with the threshold to identify desirable or related features. For example, the total cumulative threshold (Limit) can be defined according to the following equation:

ここでＡｖｅｒａｇｅはデータの複合集団におけるデータの平均値であり、Ｓｃａｌｅ
Ｆａｃｔｏｒは累積２乗法の検出感度を調整するために選択された値または変数であり、Ｓｔａｎｄａｒｄ Ｄｅｖｉａｔｉｏｎはデータの複合集団におけるデータポイント値の標準偏差であり、（Ｍａｘ−Ｍｉｎ）はデータの完全集団における最高データポイント値と最低データポイント値の差異である。一般に、総累積閾値は、特定のデータセット内で着目データポイントを識別するための比較値を確立するために定義される。

Here, Average is an average value of data in a composite group of data, and Scale.
Factor is the value or variable selected to adjust the detection sensitivity of the cumulative square method, Standard Deviation is the standard deviation of data point values in the composite population of data, and (Max−Min) is in the full population of data. The difference between the highest data point value and the lowest data point value. In general, the total cumulative threshold is defined to establish a comparison value for identifying the data point of interest within a particular data set.

  When calculating the total cumulative threshold, the proximity engine determines whether to designate each data point for inclusion in the composite data, for example, by comparing the total number and cumulative value to the threshold. The proximity engine of the present embodiment appropriately selects the first data point (1528), squares the total accumulated value of the data point (1530), and dynamically generates the squared accumulated value of the data point. The total cumulative threshold is compared (1532). If the squared cumulative value exceeds the total cumulative threshold, the data point is designated to be included in the composite data (1534).

  The absolute counter value of the data points is compared (1536) to a pre-selected threshold or a total count threshold, such as a threshold calculated based on, for example, a percentage of the number of wafers or other data sets in the population. You can also. If the absolute counter value exceeds the total count threshold, it can be specified (1538) that the data point is again included in the composite data. This process is performed appropriately for each data point (1540).

  The proximity engine can also generate composite mask data using other additional or alternative techniques. The proximity engine can also use a formula-based system for composite mask data generation. Formula-based systems utilize variables, formulas or formulas to define a composite wafer mask in accordance with various aspects of the present invention.

  For example, in an exemplary formula-based system, one or more variables may be user defined according to any suitable criteria. Variables are appropriately defined for each data point in the associated group. For example, the proximity engine can analyze each value in the data population for each data point, for example, to calculate the value of the data point, or to count the number of times the calculation provides a particular result. A variable can be computed for each data point in the data set that represents each defined variable.

  After the variables are calculated, the data points can be analyzed, such as to determine whether the data points meet user-defined criteria. For example, a user-defined formula can be solved using a calculated variable value, and if that formula is equivalent to a specific value or range of values, the data point is included in the composite mask data. Can be specified.

  Thus, the proximity engine can generate a series of composite mask data according to any suitable process or technique. The resulting composite mask data includes a series of data corresponding to the results of the data population for each data point. As a result, data point features can be identified in multiple data sets. For example, in this embodiment, the composite mask data may indicate specific device locations that share features on multiple wafers, such as test results that vary over a wide range or high failure rates. Such information can be used to refine and control manufacturing and testing because it can point out challenges or features in the manufacturing or design process.

  The composite mask data can also be analyzed to generate additional information. For example, filtering to reduce clutter from relatively separate data points, to show spatial trends and / or patterns in a data set, or to identify or filter important patterns, enhancing zones with specific characteristics Alternatively, the composite mask data can be analyzed, such as by filtering or filtering data having known characteristics. The composite mask data of this embodiment can be subjected to spatial analysis, for example, to smooth the composite mask data and complete a pattern in the composite mask data. The selected exclusion zone can receive a specific treatment so that the composite mask data is removed, ignored, enhanced, reduced in importance, or distinguished from other composite mask data. The cluster discovery process can also remove or reduce the importance of relatively unimportant or unreliable data point clusters.

  In this embodiment, the proximity engine is used to specify a specific designated zone in the composite mask data so that data points from the designated zone are given a specific designated treatment or are ignored in various analyses. Can be configured to identify. For example, referring to FIG. 16, the proximity engine can establish exclusion zones at selected locations on the wafer, such as individual devices, groups of devices, groups of devices around the parameters of the wafer, and the like. An exclusion zone can provide the effect of excluding certain data points from the influence and / or metric on other data points in proximity analysis. Data points are designated to be excluded in any suitable way, such as assigning values that are outside the scope of the next process.

  Related zones are identified in any suitable way. For example, excluded data points use device identification or coordinate file listings, such as x, y coordinates, selection of specific groups around parameters, or other processes adapted to define related zones in composite data. Can be specified. In this embodiment, the proximity engine is a group of devices that are eliminated on the wafer using simple calculations that cause the proximity engine to ignore or specially treat data points within the user-defined width at the end of the data set. Can be defined. For example, all devices listed in this range or in the file are then subject to the selected exclusion criteria. If the exclusion criteria are met, the data points or devices in the exclusion zone that meet the exclusion criteria are excluded from one or more analyses.

  The proximity engine of this embodiment is suitably configured to perform additional analysis on the composite mask data. Additional analysis can be set for any suitable purpose, such as enhancing the desired data, removing unwanted data, or identifying selected features in the composite mask data. For example, the proximity engine is suitably configured to perform a proximity weighting process to smooth and complete the pattern in the composite mask data, eg, based on a point metric system.

  With reference to FIGS. 17A-B and 18, the proximity engine retrieves all data points in the data set. The proximity engine selects the first point (1710) and matches the value of the data point with criteria such as a threshold or a range of values (1712). If the data point exceeds the selected threshold or is found to be within the selected range, the proximity engine searches for data points around the main data point to obtain a value (1714). The number of data points around the main data point may be any selected number and may be selected according to any suitable criteria.

  The proximity engine searches for surrounding data points (1716) to obtain data points that meet another relevant criterion that exceeds the influence value or indicates that the data points should be weighted. If the data point exceeds the influence value, the proximity engine assigns an appropriate weight to the main data point according to the value of the surrounding data point. The proximity engine can also adjust its weight according to the relative position of the surrounding data points. For example, the amount of weight imparted to a peripheral data point can be determined according to whether the peripheral data point is adjacent (1718) or diagonal (1720) to the main data point. The total weight may be adjusted if the data point is at the end of the wafer (1722). Once all peripheral data points around the main data point have been matched (1724), the main data point is assigned a weight overall, for example by adding a weight element from the peripheral data point. The weight for the main data point can then be compared 1726 to a threshold, such as a user-defined threshold. If the weight meets or exceeds the threshold, the data point is designated as such (1728).

  The composite mask data may be further analyzed to filter the data. For example, in this embodiment, the proximity engine can be configured to identify and appropriately remove groups of data points that are less than a threshold, such as a user specific threshold.

  Referring to FIGS. 19 and 20, the proximity engine of the present embodiment may be configured to define groups, classify by size, and exclude smaller groups. To define a group, the proximity engine searches all data points in the composite mask data for data points that meet the criteria. For example, data points in the composite mask data can be separated into value ranges and assigned index numbers. The proximity engine first searches the composite mask data for data points that match a certain index (1910). When a data point matching the specified index is encountered (1912), the proximity engine designates that detected point as the main data point and has substantially the same value in or instead of the same index. Then, in order to obtain other data points that also exceed the threshold or meet other desirable criteria, a recursive program that searches from the main data points in all directions is started (1914).

  As an example of the recursive function in the present embodiment, the proximity engine may start searching for a data point having a specific value of 5, for example. When a data point with a value of 5 is detected, the recursive program searches all data points around the main device until another data point with a value of 5 is detected. When another eligible data point is detected, the recursive program selects the detected data point as the main data point and repeats the process. Thus, the recursive process analyzes and marks all data points that match values that are adjacent or diagonal to each other to form a group. When the recursive program detects all devices in a group with a specific value, the group is assigned a unique group index and the proximity engine searches the entire composite mask data again. When all data values are retrieved, the composite mask data is completely classified into adjacent data points having the same group index.

  The proximity engine may determine the size of each group. For example, the proximity engine may count (1916) the number of data points in the group. The proximity engine may then compare the number of data points in each group to a threshold (1918) and remove groups that do not meet the threshold. The group can be removed from the classification analysis in any suitable manner, such as resetting the index value of the related group to a default value (1920). For example, if the threshold of data points is 5, the proximity engine changes the group index value of each group having less than 5 data points to a default value. Accordingly, only the groups classified by different group indices are groups having five or more data points.

  The proximity engine may perform any suitable additional operations to generate and refine composite mask data. For example, information related to a plurality of data sets and information of the data using the composite mask data including the result of further sorting, processing and analyzing the original composite mask data, for example, an apparatus or a manufacturing process on a wafer Can provide a source. The data can be provided to the user or used in any suitable manner. For example, the data can be provided to another system for further analysis or combination with other data. For example, a user-defined rule may be executed in conjunction with the merge process for the composite mask data, the original data, and any other suitable data to generate a data set that shows trends and patterns in the original data. Further, the data can be provided to the user via a suitable output system such as a printer or visual interface.

  In this embodiment, for example, the composite mask data is combined with other data and provided to the user for review. The composite mask data can be combined with any other suitable data in any suitable manner. For example, the composite mask data is merged with signature data, original data, hardware bin data, and / or other composite data. The data sets can be merged in any suitable manner, such as using various user-defined rules including formulas, thresholds, and rankings.

  In this system, the composite analysis element 214 performs the merging process using an appropriate process, and converts the composite mask data into composite original data such as composite original data, composite signature data, or composite bin data map. And merge. For example, referring to FIG. 21, the composite analysis element 214 may merge the composite mask data with the original individual wafer data using a complete merge system. Here, the composite mask data is completely merged with the original data map. Thus, the composite mask data is completely merged with the original data map regardless of the overlap or inclusion of existing patterns. When only one composite mask shows one pattern among a plurality of composite masks, the pattern is included in the entire composite mask.

  Alternatively, the composite analysis element 214 may merge data in conjunction with additional analysis. The composite analysis element 214 may screen data that may be irrelevant or unimportant. For example, referring to FIG. 22, the composite analysis element 214 can only merge data present in composite mask data that overlaps data in the original data map or another composite mask. This highlights potentially relevant information.

  Alternatively, the composite analysis element 214 may evaluate the composite mask data and the original data to determine whether certain thresholds, percentages, or other data point values overlap between maps. Depending on the configuration, the merge data matches the threshold required for data point duplication, in this case only including regions where the data points corresponding to the device significantly overlap between the composite mask data and the original data. Can be. Referring to FIG. 23, the composite analysis element includes only composite data patterns that significantly overlap with a test turbine failure, such as a defective device in the original data, for example, 50% of the duplicate data that overlaps with test turbine failure data. 214 may be configured. Therefore, the composite data pattern can be ignored when the minimum amount of the composite data overlaps with the original data. Similarly, referring to FIG. 24, the composite analysis element 214 compares two different composite data sets, eg, data from two different recipes, and whether the overlap between the two recipes meets the selected criteria. Can be determined. Only duplicate data and / or data that meets the minimum criteria are merged.

  The merge data may be provided to output element 208 and output to a user or other system. The merge data can be taken as input to another process, such as a generation error identification process or a key trend identification process. The merge data can also be output in any type or format, such as a memory structure, database table, flat text file or XML file.

  In this embodiment, the merge data and / or wafer map is provided to an ink map generation engine. The ink map engine generates a map of the off-line inking device. In addition to the off-line ink map, the merge data results may be used in the generation of binning results for partless production of parts, or in any other process or application that utilizes those types of results.

  Test system 100 may also be configured to use the test data to identify features and / or problems associated with manufacturing processes, including manufacturing and / or testing processes. For example, the test system 100 may analyze test data from one or more sources to match the characteristics of the test data with known problems, challenges, or features in the manufacturing and testing process. If the test data features do not correspond to a known problem, the test system 100 is updated so that the new test data features are diagnosed as new test data features are discovered. Can receive and store information related to

  In particular, the diagnostic system 216 is suitably configured to automatically identify and classify test data features. The test system 100 may also automatically provide notifications such as emergency alerts and / or subsequent output reports. The classification criteria and procedures are configurable, and if different test data features are associated with different problems, the information is suitably provided to the diagnostic system 216 for use in later analysis. Automatic classification and updating of test features facilitates consistent analysis of test data and maintains information in the diagnostic system 216 to continually improve test data analysis.

  For example, the test data diagnostic system 216 can be configured to diagnose a problem based at least in part on the test data. Data may be received at run time, extracted from the storage system after completion of one or more test runs, and / or may include historical data. The diagnostic system 216 can receive test data from any suitable source, such as parametric tests, metrology, process control, microscopes, spectrometers, and defect analysis and fault isolation data. The diagnostic system 216 includes smoothed data, sorted composite data, and additional data generated based on test data such as bin results, SPC data, spatial analysis data, anomaly data, composite data, and data signatures, etc. Receive processed data.

  For example, referring to FIG. 25, the diagnostic system 216 of the present embodiment is configured to analyze two or more types of data. The diagnostic system 216 analyzes the electronic wafer sort (EWS) original data 2512 as well as the EWS bin signature data 2514 of each wafer. The EWS bin signature data 2514 may include any suitable classification data based on EWS results that may be generated, for example, by supplemental data analysis element 206. In this embodiment, the EWS bin signature data 2514 corresponds to each device on the wafer indicating the size of the device defect classified as gross, important, or duplicate, as determined by the supplemental data analysis element 206. Data to be included.

  The diagnostic system 216 also receives 2516 process control or electrical test (ET) data, such as data relating to electrical characteristics for various points on the wafer and components 106. Further, the diagnostic system 216 may receive 2518 bin map data for the wafer indicating a pass / fail binning classification for the component 106. Further, the diagnostic system 216 receives 2520 an anomalous signature bin map for the wafer, such as data generated by the anomaly classification element 212. For example, each outlier in the data is classified as micro, mild, moderate, or severe according to selected criteria.

  The diagnostic system 216 may be configured in any suitable manner to analyze the received data and identify process features, such as problems or issues in the manufacturing or testing process. Process characteristics may be identified according to any criteria or process. For example, referring to FIG. 26, the diagnostic system 216 of this embodiment includes a rule-based analyzer 2610 that identifies process characteristics according to predefined criteria. Additionally or alternatively, the diagnostic system 216 may include a pattern recognition system 2612 that identifies process features based on patterns recognized in the test data.

  In particular, the rule-based analyzer 2610 can analyze test data for specific features corresponding to specific problems. Specific features suitably include any known data set corresponding to a specific test or manufacturing challenge. The rule-based analyzer 2610 is suitably configured to analyze data relating to selected types of data and generate corresponding signals. For example, if a test corresponding to a particular output node on multiple components does not produce any results, the diagnostic system 216 may either (a) the output node is not functioning at all, or (b) the test probe is appropriate with the output node. Any notification of not touching may be generated.

  The pattern recognition system 2612 is suitably configured to receive the data from various sources and identify patterns in the data. The pattern recognition system 2612 is also suitably configured to match the identified pattern with a known problem associated with such a pattern, for example, by assigning a special task likelihood based on the identified pattern. Is done. For example, a cluster of devices with similar reject bin results or outliers at the same location on different wafers may indicate a particular problem in the manufacturing process. The pattern recognition system 2612 identifies and analyzes patterns of data that may indicate such challenges in the manufacturing and / or testing process.

  The pattern recognition system 2612 can be configured in any suitable manner to identify patterns in various test data and analyze patterns corresponding to potential manufacturing or test challenges. In this embodiment, the pattern recognition system 2612 includes an intelligent system configured to recognize a spatial pattern of clustered defects in test data. In particular, the pattern recognition system 2612 of this embodiment includes a pattern identifier 2614 and a classifier 2616. The pattern identifier 2614 identifies a pattern in the received data that can correspond to the task. The classifier 2616 classifies the identified patterns into different known categories or new categories.

  The pattern identifier 2614 may be configured in any suitable manner and identifies a pattern in the test data. For example, the pattern identifier 2614 is suitably configured to select a data set that does not show a pattern, generate data related to the pattern in the data, and select a specific pattern to classify. In the present embodiment, the pattern identifier 2614 includes a pattern filter 2618, a feature extractor 2620, and a feature selector 2622. The pattern filter 2618 determines whether a data set such as data relating to a specific wafer includes a pattern. The feature extractor 2620 generates features that display information from the data set specified by the pattern filter 2618 and is suitable for analysis by the classifier 2616. The feature selector 2622 analyzes the feature generated according to the selected criterion and selects the feature of the classification.

  For example, the pattern filter 2618 suitably includes a software module configured to process the received data and detect whether any pattern is present in the data. In order to maintain the uniformity of the test data, the pattern filter appropriately processes the data without losing information from the original data. The pattern filter 2618 can exclude data sets that do not have a pattern, leaving only the data set in which the pattern is detected. The pattern filter may be configured to analyze various types of data individually or in combination.

  In the present embodiment, the pattern filter 2618 reduces noise in the data set. For example, intermittent noise from test data is removed. The pattern filter 2618 may use any suitable system for filtering noise, such as spatial filtering, median filtering, or convolution processing on test data. The pattern filter 2618 analyzes the test data to identify patterns in the test data. The pattern filter 2618 may be configured to identify patterns in the data set in any suitable manner.

  In this embodiment, the pattern filter 2618 analyzes data in conjunction with one or more masks associated with known or theoretical patterns according to a pattern search algorithm. In one exemplary embodiment, the pattern filter 2618 uses spatial filtering such as median filtering to reduce noise such as “sesame salt” noise caused by outliers in the data. For example, referring to FIG. 28, the pattern filter may be configured to perform median filtering of test data using a two-dimensional 2-bin map in conjunction with a pattern mask. The pattern mask may include any suitable mask that selects the e-bin map to determine a device that performs the median filtering. The pattern mask is suitably similar to the pattern identified by the classifier. For example, the pattern mask utilizes information generated by the composite analysis element 214, such as the composite mask data from various data sets or merged composite mask data, but using any suitable simulated theoretical pattern. obtain.

  The median filtering is performed around each value selected by a mask in the original e-bin map data. In particular, each data point in the data set and selected data points surrounding each data point are compared with each selection mask. For example, around each value selected by the mask in the test data, a median value is calculated taking into account an n × n size such as a neighboring 3 × 3 window. Those datasets that do not show a pattern are ignored. A data set containing the pattern is provided to feature extractor 2620.

  Analyzing a data set having a pattern, the identified pattern can be matched to a particular matter. However, the raw data used by the pattern filter 2618 under certain circumstances may not be suitable for analysis by the classifier 2616. Accordingly, pattern identifier 2614 includes a system that creates data that can be used by classifier 2616 based on test data.

  In the present embodiment, the feature extractor 2620 generates a feature indicating information from the data set specified by the pattern filter 2618. The feature may be particularly effective in situations where the original data is difficult or unusable. The features can then be analyzed by classifier 2616 to identify the type of pattern identified by pattern filter 2618. For example, the feature extractor 2620 can be configured to calculate a series of variables based on a data set. The features are suitably configured to efficiently encode the relevant information residing in the original data, and classifier 2616 is used to classify corresponding patterns in the data set into defect classes.

  The features include any suitable information extracted from the data. In this embodiment, the feature extractor 2620 calculates several features such as mass, center of gravity, cross-sectional primary moment, and seven Hu moments obtained from test data. The various features described above can be determined for any data set. In this embodiment, the features are calculated based on bin data or other test data on the wafer. As such, the mass generally corresponds to the distribution scale of the fault in the bin or other value in question, but may not provide any information regarding the location of the distribution. When the test value of the coordinates x and y is f (x, y), the mass M is appropriately calculated according to the following equation.

ここでＮは、試験データセットにおけるデータポイントの合計数である。質量は、ウエハ上の障害数など、異なるデータポイント数を持つデータセット間に一貫性が存在するように正規化される。

Where N is the total number of data points in the test data set. The mass is normalized so that there is consistency between data sets with different numbers of data points, such as the number of defects on the wafer.

The median may be defined by the spatial coordinates, such as x _c and y _c. The median provides position information by measuring the center of the obstacle's distributed mass. The median value of the data can be calculated in any suitable way, for example according to the following equation:

断面一次モーメントの順序（ｐ＝０．．．３，ｑ＝０．．．３）は、以下の式に従って計算され得る。

The order of the cross-sectional first moments (p = 0 ... 3, q = 0 ... 3) can be calculated according to the following equation:

このモーメントセットにより供給される情報は、ビンマップがそのモーメントのすべての順序で構成できるという意味において、データセットに対応する表示を提供する。そのため、各モーメントの係数は、ビンマップに常駐する一定量の情報を伝える。

The information provided by this moment set provides a display corresponding to the data set in the sense that the bin map can be constructed in any order of that moment. Thus, the coefficient of each moment conveys a certain amount of information resident in the bin map.

  In this embodiment, seven Hu moments are also considered (Hu, M. K. “Visual Pattern recognition by moments inventors”, IRE Transaction on Information Theory, Vol. 8 (2), pp. 179-187, 19 years. ). The seven Hu moments are invariant under translation, scaling, and rotation. The Hu moment can be calculated according to the following equation:

ここで、η_ｐｑがすべてのｐ、ｑの中央モーメントであり、
η_ｐｑ＝ΣΣ（ｘ−ｘ_ｃ）^ｐ（ｙ−ｙ_ｃ）^ｑｆ（ｘ，ｙ）
と定義される。

Where η _pq is the central moment of all p and q,
η _pq = ΣΣ (x− _{c c} ) ^p (y−y _c ) ^q f (x, y)
Is defined.

  The first six of these moments are invariant under reflective action, but the last moment changes the sine. These quantity values are non-abnormally large and vary widely. To avoid precise problems, the logarithm of the absolute value can be taken and passed to the classifier 2616 as a feature. The invariance of these features is advantageous when analyzing binmaps or other data sets with signature classes that are independent of scale, position, or angular position.

  The representative 26 feature sets are appropriately extracted from each bin map or other data set specified by the pattern filter 2618. All or some features may be provided directly to the classifier 2616 for classification. In this embodiment, fewer than all features may be provided to the classifier 2616. For example, reducing the number of features to be analyzed, thereby reducing the dimension of the analysis process. Reducing the number of features also tends to reduce computational complexity and redundancy. Furthermore, the required generalization characteristics of the classifier 2616 may require a limited number of features. For example, in the case of this classifier 2616, the generalization property of the classification 2616 may correspond to the ratio of the number of training parameters N to the number of free classifier parameters. More feature numbers correspond to more classifier parameters, such as synaptic weights. For the number N of finite and normally limited training parameters, a smaller number of features tends to improve the generalization of the classifier 2616.

  The system's feature selector 2622 analyzes the generalized features according to the selected criteria and selects the features to classify. In particular, feature selector 2622 is suitably configured to select a particular feature to analyze and minimizes the errors induced by providing some features to classifier 2616. The feature selector 2622 is configured in any suitable manner and selects features to transfer to the classifier 2616.

  In this embodiment, the feature selector 2622 implements a genetic algorithm and selects a feature. The genetic algorithm appropriately includes a parallel search process, which maintains multiple solutions, eliminates suspicious solutions, and improves good solutions. Genetic algorithm analysis is properly applied to various features for many iterations, and the output of the algorithm is the best solution detected during the evolution process.

  Referring to FIG. 27, when the genetic algorithm in the present embodiment is implemented, feature selector 2622 first defines the value of the GA parameter (2710), activates a generation counter (2712), and randomly sets the initial population. (2714). The population includes a group of coded individuals, each individual representing a group of selected features. The array of solids in the initial population is randomly generated by, for example, an automatic computer program. Any suitable parameter may be used, such as parameters corresponding to the number of epochs, the number of individuals in the population, the size of the chromosome, cost function, selectivity, crossover / replication rate, and mutation rate. In this system, each population contains ten different solids, which indicate whether a particular feature exists in the optimal solution. That is, each individual is binary coded into the chromosome and represents a group of features such as a 26 bit (feature number) string. Here, “1” indicates that a particular feature is considered for classification, and “0” means that no feature is used at that position.

  The feature selector 2622 then evaluates (2716) the initial population, applies crossovers and mutations to the population (2718, 2720), and increments the generation counter (2722). When the generation counter reaches a preselected limit (2724), eg, the maximum number of generations, the special selector 2622 terminates the analysis and provides the selected feature to the classifier 2616 (2726). Unless the limit is reached, the feature selector 2622 repeats evaluation of the child population (2728) and applies the crossover and mutation to the population until the limit is reached.

  The classifier 2616 classifies the identified patterns into different known categories or new categories. The classifier 2616 can optionally classify patterns identified by pattern identifiers 2614 such as base or maximum likelihood identifiers, supervised non-parameter classifiers, and / or supervised or unsupervised rule-based classifiers. An appropriate classification system can be included. The classifier 2616 of the present embodiment is configured to classify patterns based on analysis of features selected by the feature selector 2622.

In this embodiment, the classifier 2616 is a neural network such as a linear neural network, a radial basis function (RBF) neural network, or a feedforward network configured to analyze features selected by a feature selector 2622. including. Referring to FIG. 29, an RBF neural network 2910 according to various aspects of the present embodiment suitably includes three layers of different roles. The input layer 2912 includes a source node that connects the RBF network to the feature selector 2622 and receives selected features. A second layer 2914 suitably including a hidden layer applies a non-linear transformation from the input space to the hidden layer. Here, the activation function of the neuron (h _i (x)) is a radial basis function (RBF Ф). A Gaussian function is generally used, but Cauchy, multi-order functions and inverse multi-order functions can also be used. In this embodiment, each hidden neuron calculates a distance c from the input layer to the neuron center point, and applies the distance to the RBF. The neurons of the output layer 2916 (o _i (x)) calculate the weighted sum between the outputs of the hidden layer and the weight of the link connecting the outputs and the hidden layer, for example according to the following equation:
_{h i (x) = Ф (} ‖x-c i ‖ ² / _r ^{i 2)}
o _j (x) = Σw _ij h _i (x) + w _o
Here, x is an input, Ф is RBF, c _i is the center of the i-th hidden neuron, r _i is its radius, w _ij is a heavy link connecting the number of hidden neurons i and the number of output neurons j, and w _o Is the bias of the output neuron.

  That is, the neurons in the hidden layer 2914 apply a non-linear transformation from the input space to the higher dimensional hidden space, and the input layer 2916 performs a linear transformation from the hidden unit space to the output space. The correctness of this arrangement is likely that the problem of pattern classification nonlinearly cast in a high-dimensional space can be separated more linearly than in the case of a low-dimensional space.

  The classifier 2616 receives data for analysis such as the features obtained by the feature extractor 2620 and the features selected by the feature selector 2622. The data is processed by an RBF network or the like, and data such as features is compared with data relating to a known pattern. If a known pattern matches the data, a corresponding matter or feature in the known pattern is notified. The classifier 2616 may assign a likelihood of a particular item or feature corresponding to the pattern. If there is no known pattern that matches the data, a mismatch is similarly notified. The resulting information can then be provided to output element 208.

  The classifier 2616 may also provide a corrective action plan based on the corresponding feature. In particular, the classifier 2616 may be configured to access a memory, such as the database 114, to identify a set of corrective action candidates corresponding to various manufacturing and / or testing process features. For example, if a feature that matches the identified pattern indicates that the component has been exposed to excessive heat at a particular point in the manufacturing process, the classifier 2616 may use the database for possible corrective actions corresponding to that feature. 114 may be checked to solve the problem, such as by reducing the exposure temperature or duration of the wafer in a particular manufacturing process.

  The pattern recognition system 2612 can also be configured to learn additional information regarding the identified pattern and the corresponding task. For example, the pattern recognition system 2612 can be configured to receive diagnostic feedback information after identifying a pattern. The diagnostic feedback information appropriately corresponds to the actual matter identified in the manufacturing or testing process that produced the identified pattern. The pattern recognition system may then further analyze the data using diagnostic feedback information to identify a recurrence of the problem.

  In operation, data is received from various sources. Such data, such as data that accurately corresponds to known problems, is first analyzed for rule-based diagnosis. The diagnostic system 216 generates an output that indicates a particular problem that is identified using rule-based analysis. The data is then provided to a pattern recognition system 2612 that analyzes the data and identifies the pattern. Thereafter, the pattern recognition system 2612 may analyze an identification pattern corresponding to a particular manufacturing or test task. The pattern identification system 2612 appropriately assigns a likelihood associated with a specific task based on the pattern. The diagnostic system 216 may recommend corrective action based on the patterns identified in the data. The diagnostic system 216 may then generate an output report showing various identified issues and corrective action plans. Once the reported problem is resolved, the pattern recognition system 2612 may receive diagnostic feedback information. The diagnostic feedback information is stored in the pattern recognition system 2612 and used for later analysis.

  The specific implementations shown and described herein are merely intended to illustrate the present invention and the best mode thereof, and are not intended to limit the scope of the invention in any way. For the purpose of omission, conventional signal processing, data transfer, and other functional aspects of the system (and components of the individual operating elements of the system) may not be described in detail. Further, the associated lines shown in the various figures indicate exemplary functional relationships and / or physical coupling between various elements. There are many alternative or additional functional relationships or physical connections in actual systems. Although the present invention refers to the preferred embodiment as described above, changes and modifications may be made without departing from the scope of the invention. These and other changes or modifications are intended to be included within the scope of the present invention as set forth in the appended claims.

Claims (1)

A test system as described herein.

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