KR101524917B1 - Tool optimizing tuning systems and associated methods - Google Patents

Abstract

The present disclosure provides various methods for tuning process parameters of a process tool and systems for implementing such tuning. An exemplary method for tuning process parameters of a process tool such that wafers processed by the process tool exhibit desired process monitor items includes defining an operation constraint criterion and a sensitivity adjustment criterion; Using the sensitivity constraint criterion and the sensitivity adjustment criterion to determine a potential tool tuning using process monitor item data associated with wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to each process parameter, Generating a set of process parameter combinations; Generating a set of optimal tool tuning process parameter combinations from the set of potential tool tuning process parameter combinations; And configuring the process tool according to one of the optimal tool tuning process parameter combinations.

Description

TOOL OPTIMIZING TUNING SYSTEMS AND ASSOCIATED METHODS [0001]

The present invention relates to various methods for tuning process parameters of a process tool and to systems for implementing such tuning.

An integrated circuit is fabricated by processing wafers with a series of wafer fabrication tools (referred to as processing tools). Each processing tool typically performs a wafer fabrication operation (called a process) on wafers in accordance with a predefined (or predetermined) process recipe that defines various parameters of the process. For example, IC manufacturing typically involves many processes associated with production and support, such as, for example, IC manufacturers often focus on monitoring the hardware and associated processes of process tools to ensure and maintain reliability, repeatability, and yield in IC manufacturing. Use a number of process steps that require tools. This monitoring involves periodically tuning the process parameters of the process tool to ensure that the process tool produces ICs with the desired characteristics.

Existing systems and related methods for tuning process tools were generally satisfactory for their intended purposes, but were not entirely satisfactory in all respects.

The present disclosure provides a number of different embodiments. An exemplary method for tuning process parameters of a process tool such that wafers processed by the process tool exhibit desired process monitor items includes defining an operation constraint criterion and a sensitivity adjustment criterion; Using the sensitivity constraint criterion and the sensitivity adjustment criterion to determine a potential tool tuning process using process monitor item data associated with wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to each process parameter, Generating a set of parameter combinations; Generating a set of optimal tool tuning process parameter combinations from the set of potential tool tuning process parameter combinations; And configuring the process tool according to one of the optimal tool tuning process parameter combinations. The method further includes processing the wafers with a process tool configured according to one of the optimal tool tuning process parameter combinations.

In the example, N process parameters are associated with a process tool, and the method further comprises determining a set of potential tool tuning process parameter combinations for each of the N processes for tuning to include various tool tuning combinations for n process parameters And selecting the number of parameters (n). In an example, generating the set of potential tool tuning process parameter combinations comprises: generating a first set of potential tool tuning process parameter combinations; Generating potential tool tuning process parameter combinations from a first number of potential tool tuning process parameter combinations to a second number, wherein the second number is greater than the first number; Generating potential tool tuning process parameter combinations of a third number from the second number of potential tool tuning process parameter combinations, the third number being less than the second number; And generating potential tool tuning process parameter combinations of a fourth number from the third number of potential tool tuning process parameter combinations, wherein the fourth number is less than the third number.

In the example, a first number of potential tool tuning process parameter combinations are generated using the process monitor item data and the sensitivity data to generate a set of process parameter values for each process parameter. Then, in the example, a second number of potential tool tuning process parameter combinations are generated by performing a growth process for each process parameter to extend the set of each process parameter value. For the enhancement of this example, the third number of potential tool tuning process parameter combinations determines whether the second number is greater than a predefined number of potential tool tuning process parameter combinations; And then performing a trimming process on at least one of the set of process parameter values until the number of potential tool tuning process parameter combinations is less than or equal to a predefined number. For a further enhancement of this example, a fourth number of potential tool tuning process parameter combinations are generated by applying a sensitivity adjustment criterion for a third number of potential tool tuning process parameter combinations. In the example, the action constraint criterion defines an optimization object function; Generating the set of optimal tool tuning process parameter combinations from the set of potential tool tuning process parameter combinations includes determining tool tuning process parameter combinations that minimize the value of the optimization object function. The action constraints criteria may also include correlations between process parameters that define tuning of process parameters, correlations between process monitor items that define tuning of process parameters, and process monitor items that define tuning of process parameters, Can be defined.

An exemplary method for qualifying a process tool for production includes processing wafers with a process tool; And determining whether the process monitor item data associated with wafers processed by the process tool is within the specification, releasing a process tool for production if the process monitor item data is within the specification, And if not, tuning the process parameter tool set of the process tool. Wherein the tuning includes defining an operation constraint criterion and a sensitivity adjustment criterion; Using the sensitivity constraint criterion and the sensitivity adjustment criterion to determine a potential tool tuning process using process monitor item data associated with wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to each process parameter, Generating a set of parameter combinations; Generating a set of optimal tool tuning process parameter combinations from the set of potential tool tuning process parameter combinations; And configuring the process tool according to one of the optimal tool tuning process parameter combinations. The method includes repeating the steps of processing the wafers until the process tool is released for production and determining whether the process monitor item data associated with the wafers processed by the process tool is within specifications . In an example, the method further comprises performing maintenance on the process tool prior to determining whether the process monitor item data associated with the wafers processed by the processing and process tool is in the specification. In an example, the method further comprises processing the wafers with a process tool after ejecting the process tool for production.

In an example, generating the set of potential tool tuning process parameter combinations comprises: generating a first set of potential tool tuning process parameter combinations; Generating potential tool tuning process parameter combinations from a first number of potential tool tuning process parameter combinations to a second number, wherein the second number is greater than the first number; Generating potential tool tuning process parameter combinations of a third number from the second number of potential tool tuning process parameter combinations, the third number being less than the second number; And generating potential tool tuning process parameter combinations of a fourth number from the third number of potential tool tuning process parameter combinations, wherein the fourth number is less than the third number. In the example, a first number of potential tool tuning process parameter combinations are generated using the process monitor item data and the sensitivity data to generate a set of process parameter values for each process parameter; A second number of potential tool tuning process parameter combinations are generated by performing a growth process to expand a set of each of the process parameter values; Generating a third number of potential tool tuning process parameter combinations by performing a trimming process on the set of at least one process parameter values; A fourth number of potential tool tuning process parameter combinations are generated by applying a sensitivity adjustment criterion to a third number of potential tool tuning process parameter combinations. In the example, the action constraint criterion defines an optimization object function; Wherein generating a set of optimal tool tuning process parameter combinations from the set of potential tool tuning process parameter combinations includes selecting a fourth set of potential tool tuning process parameters to determine a tool tuning process parameter combinations that minimize a value of an optimization object function. And evaluating process parameter combinations. In the example, a fourth set of potential tool tuning process parameter combinations is ordered such that the combinations that minimize changes to process parameters are listed first.

An exemplary integrated circuit fabrication system for implementing the methods described herein includes a process tool tuning system configured to determine a set of process parameters for a process tool and a process tool configured to perform a process on wafers. The process tool tuning system includes process monitor item data associated with wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to each process parameter, predefined operating constraint criteria, and predefined sensitivity Generate a set of potential tool tuning process parameter combinations using the adjustment criteria; And a tool tuning solution module configured to generate a set of optimal tool tuning process parameter combinations from the set of potential tool tuning process parameter combinations. In the example, the tool tuning solution module includes various modules for determining a process parameter tool set. For example, a tool tuning solution module may include an engineering based knowledge module that defines operational constraints criteria; A combination calculator module configured to generate a set of potential tool tuning process parameter combinations; A statistical optimizer module configured to generate a set of optimal tool tuning process parameter combinations; And a robust buffer module configured to define a sensitivity adjustment criterion.

The tool tuning system 80 automatically determines (or calculates) the best combination of process parameters for the process tool 30. The tool tuning system 80 (and associated methods) provides convergent, feasible, and practical tool tuning process parameter solutions. Thus, the tool tuning system 80 provides a cost-effective and time-efficient way to make the process tool 30 eligible for production

The disclosure of the present invention is best understood by reading the following detailed description together with the accompanying drawings. In accordance with standard practice in the industry, emphasize that the various features are not drawn to scale and are used for illustrative purposes only. Indeed, the dimensions of the various features may be increased or decreased arbitrarily for clarity of explanation.
1 is a block diagram of an integrated circuit device manufacturing system in accordance with various aspects of the present disclosure.
2 is a schematic diagram of various tunable process parameters correlated with process monitor items in accordance with various aspects of the present disclosure.
Figure 3 is a block diagram of a tool tuning process flow that may be implemented by an integrated circuit manufacturing system to tune a process tool, such as the process tool of the integrated circuit manufacturing system of Figure 1, in accordance with various aspects of the present disclosure .
4 is a block diagram of a tool tuning optimization process flow that may be implemented in a tool tuning process flow, such as the tool tuning process flow of FIG. 3, in accordance with various aspects of the present disclosure.
5 is a flow diagram illustrating the tool tuning process flow of FIG. 3 to produce a set of optimal process parameters for tuning a process tool, such as a process tool in the integrated circuit fabrication system of FIG. 1, in accordance with various aspects of the present disclosure. An exemplary case in which the tool tuning optimization process flow of FIG. 4 is implemented is provided.
Figure 6 is a block diagram of a process flow that may be implemented to generate a set of potential process tool tuning process parameter combinations in accordance with various aspects of the present disclosure.
7 is a flow chart of a method for tuning a process tool according to various aspects of the present disclosure.
Figure 8 is a block diagram of a computer system for implementing a method for tuning a process tool, such as the methods described with reference to Figures 2 through 7, in accordance with various aspects of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS The following inventions provide many different embodiments or examples of implementing various aspects of the invention. Specific examples of components and devices are described below to simplify disclosure of the present invention. Of course, these are merely illustrative and not intended to be limiting. For example, in the following detailed description, the formation of the first feature on or on the second feature may include an embodiment in which the first and second features are formed in direct contact, and the first and second Additional features may be formed between the first and second features such that the features may not be in direct contact. Furthermore, the disclosure of the present invention may repeat the reference numerals and / or characters in various examples. Such repetition is for simplicity and clarity, and such repetition itself does not describe the relationship between the various embodiments and / or configurations disclosed.

1 is a block diagram of an integrated circuit manufacturing system 10 in accordance with various aspects of the present disclosure. In the example, the integrated circuit manufacturing system 10 is a virtual integrated circuit manufacturing system ("virtual fab"). The integrated circuit fabrication system 10 implements integrated circuit fabrication processes to fabricate integrated circuit devices. For example, the integrated circuit fabrication system 10 implements semiconductor fabrication processes for fabricating a substrate (or wafer). The substrate includes a semiconductor substrate, a mask (photomask or reticle, collectively referred to as a mask), or any underlying material on which processing is performed to produce material layers, pattern features, and / or integrated circuits. In FIG. 1, the integrated circuit manufacturing system 10 has been simplified for clarity, so as to better understand the inventive concepts of the present disclosure. Additional features may be added in the integrated circuit manufacturing system 10, and some of the features described below may be replaced or removed in other embodiments of the integrated circuit fabrication system 10.

The integrated circuit device manufacturing system 10 includes various entities (a database 25, a process tool 30, a metrology tool 40, an advanced process control (APC) system 50, (FDC) system 60, other entities 70, and tool tuning system 80) to communicate with each other. The integrated circuit manufacturing system 10 may include more than one respective entity in the illustrated embodiment, and may further include other entities not shown. In this example, each entity of the integrated circuit fabrication system 10 interacts with other entities through the network 20 to receive services from and / or provide services to other entities. The network 20 may be a variety of different networks or a single network, such as an intranet, the Internet, another network, or a combination thereof. Network 20 includes wired communication channels, wireless communication channels, or a combination thereof.

The database 25 stores data associated with the integrated circuit manufacturing system 10, and in particular data associated with integrated circuit manufacturing processes. In the illustrated embodiment, the database 25 includes a process tool 30, a metrology tool 40, an APC system 50, an FDC system 60, another entity 70, a tool tuning system 80, Lt; / RTI > For example, the database 25 may include data associated with wafer properties of wafers processed by the process tool 30 (such as those collected by the metrology tool 40 as further described below); Data associated with parameters implemented by the process tool 30 for processing such wafers; Data associated with the analysis of wafer characteristics and / or parameters by the APC system 50, the FDC system 60, and the tool tuning system 80; And other data associated with the integrated circuit fabrication system 10. In the example, each of the process tool 30, the metrology tool 40, the APC system 50, the FDC system 60, the other entity 70, and the tool tuning system 80 may each have an associated database.

The process tool 30 performs an integrated circuit manufacturing process. In this example, the process tool 30 is a chemical vapor deposition (CVD) tool used for epitaxial growth. Thus, the process tool 30 may be referred to as a CVD epitaxial tool. The wafer may be placed in a CVD epitaxial tool and subjected to an epitaxial process, such as vapor phase epitaxy, to form an epitaxial feature of the wafer. CVD epitaxial tools may include a chamber, a gas source, an exhaust system, a heat source, a cooling system, and other hardware. The chamber serves as a controlled environment for performing the epitaxial process. The gas source provides reactants and purging gasses during the epitaxial process and the exhaust system maintains the pressure in the chamber during the epitaxial process. The heat source includes lamp modules such as a bottom inside lamp module, a bottom outside lamp module, an inside top lamp module, and an top outside lamp module. Each lamp module includes an array of infrared lamps for heating the chamber to the desired chamber temperature during the epitaxial process and / or heating the wafer to the desired wafer temperature, by delivering energy to the chamber of the CVD epitaxial tool during the epitaxial process .

To ensure that the epitaxial features exhibit the target wafer properties (such as thickness, component concentration, and sheet resistance), the epitaxial process forms an epitaxial feature according to a predetermined (or predefined) epitaxial process recipe . The predetermined (or predefined) epitaxial process recipe defines various parameters implemented by the CVD epitaxial tool to achieve the target wafer properties. These parameters include process time, precursor gas type, precursor gas flow rate, chamber temperature, chamber pressure, wafer temperature, other parameters, or combinations thereof. During the epitaxial process, various hardware of the CVD epitaxial tool (such as chamber, gas source, exhaust system, heat source, and cooling system) are configured to achieve the prescribed parameters. Process tool 30 includes sensors that monitor these parameters during processing of wafers, such as during an epitaxial process. For example, the CVD epitaxial tool can be a CVD epitaxial tool lamp module, including chamber pressure, chamber temperature, wafer temperature, gas flow, deposition time (voltage, current, power, resistance, And other parameters, such as various characteristics of the CVD epitaxial tool, or combinations thereof.

The metrology tool 40 measures and collects data associated with the wafers during integrated circuit fabrication. For example, the metrology tool 40 may perform in-line measurements on the wafers being processed to determine critical dimensions of the features of the wafer (e.g., line widths of the features), thickness of the material layers of the wafers, Information about the various wafer properties of the wafers, such as the overlay accuracy between the features, the dopant profile (or concentration) of the features, the size and / or type of defect, electrical characteristics of features, other wafer characteristics, . In the illustrated embodiment, the metrology tool 40 measures wafer characteristics of the wafers being processed by the process tool 30. For example, the metrology tool 40 may determine the thickness of the epitaxial features of the wafers formed by the epitaxial process performed by the process tool 30, the electrical properties (such as sheet resistance), the surface roughness, the epitaxial stress, Other properties, or a combination thereof. The metrology tool 40 may include electrical tools, optical tools, analysis tools, other tools, or a combination thereof for measuring and collecting such data. These tools include, but are not limited to, microscopes (e.g., scanning electron microscopy and / or optical microscopy), microanalysis tools, line width measurement tools, mask and reticle defect tools, particle distribution tools, surface analysis tools, stress analysis tools, , A mobility and carrier concentration measurement tool, a junction depth measurement tool, a film thickness tool, a gate oxide integrity test tool, a capacitance voltage measurement tool, a focused ion beam (FIB) tool, a laser surface defect scanner, A process tool particle counter, a wafer evaluation test tool, another measurement tool, or a combination thereof.

The APC system 50 monitors the wafer characteristics of the wafers being processed and uses the inline metrology data, such as the data collected by the metrology tool 40, the process model, and various algorithms, Lt; RTI ID = 0.0 > intermediate < / RTI > The fine tuning of such process targets may be referred to as control operations, which compensate for tool problems and / or process problems that may generate wafer characteristic variations. The APC system 50 can transition control operations in real time, per wafer, every lot, or a combination thereof. In the illustrated embodiment, the APC system 50 implements control operations to modify the epitaxial process recipe performed by the process tool 30 to form the epitaxial features of the wafers. For example, the APC system 50 (based on in-line metrology data of the wafers to be processed, process models, and various algorithms) can be used to ensure that the epitaxial features of each processed wafer exhibit the target properties The parameters implemented by the process tool 30, such as the predetermined epitaxial process recipe (specifically, process time, gas flow rate, chamber temperature, chamber pressure, wafer temperature, or other process parameters) for each processed wafer ).

The FDC system 60 monitors the wafer characteristics achieved by the parameters implemented by the process tool 30 during the integrated circuit fabrication process and the parameters implemented by the process tool 30 during the integrated circuit fabrication process, And evaluates the states of the process tool 30 to detect tool problems, such as tool state degradation. In general, the FDC system 60 implements statistical process control (SPC) to track and analyze the state of the process tool 30. For example, the FDC system 60 may implement SPC charts that document the historical process performance of the process tool 30 by charting the SPC data associated with the process over time. This SPC data includes parameters and / or wafer characteristics associated with multiple wafers processed by the process tool 30. [ If the SPC data indicates that the parameters and / or wafer characteristics have deviated from the acceptable targets (i.e., if the FDC system 60 detects a fault or an abnormality), the FDC system 60 will generate an alarm Any problems with the process tool 30 can be identified by triggering and notifying the operator of the process tool 30, interrupting the process performed by the process tool 30, taking another action, or a combination thereof And can be resolved.

In this example, to detect problems with the CVD epitaxial tool, the FDC system 60 monitors the parameters implemented by the process tool 30 to form the epitaxial features of the wafers. The FDC system 60 evaluates these parameters and wafer characteristics to detect any anomalies or defects during operation of the CVD epitaxial tool. In the example, the anomalies are displayed during the epitaxial process when the chamber pressure or chamber temperature changes significantly (higher or lower) from a prescribed chamber pressure or chamber temperature, such as a predetermined epitaxial process recipe. In another example, the abnormality is indicated during the epitaxial process when the flow rate of the precursor gas changes significantly (higher or lower) from the defined flow rate of the precursor gas, such as the predetermined epitaxial process recipe. In another example, the anomalies are displayed when the properties (such as sheet resistance, thickness, and / or stress) of the epitaxial features of the wafers formed by the CVD epitaxial tool vary significantly from the target properties of the epitaxial features. These anomalies may indicate problems with the process tool 30. For example, damaged or aged hardware of the CVD epitaxial tool can cause the flow rate of the precursor gas, the chamber pressure, and / or the chamber temperature to vary from the expected flow rate of the precursor gas, the chamber pressure, and / have.

The tool tuning system 80 tunes the process tool 30 (in this example, a CVD epitaxial tool) for manufacturing integrated circuit devices. The process tool 30 requires periodic and / or irregular maintenance due to process drift or failure / aging of its hardware. Regular maintenance occurs after a certain amount of operating time or when a certain amount of wafers have been processed and then removed from production for maintenance (offline processing). Non-periodic maintenance is performed by the FDC system 60 to detect anomalies or defects during operation of the process tool 30 and to trigger an alarm to notify engineers of the process tool 30 (also referred to as operators, technicians, or technicians) . The process tool 30 is then removed from production for maintenance (offline processing). Regular and / or non-periodic maintenance may include cleaning these inner walls and / or parts to remove residues or deposited films on the inner walls and / or parts of the processing chamber of the process tool 30 have. Maintenance may further include removing parts from the process tool 30, these parts being replaced or replaced during service provisioning. Various maintenance (service provisioning) processes performed on the process tool 30 may cause the process tool 30 to perform differently after maintenance (service provisioning) processes. In the example, processing chamber cleaning results in a chamber interior that operates differently compared to before cleaning. In another example, the processing of the parts of the process tool 30 alters the operating characteristics of the process tool 30 (regardless of whether it is removed and uninstalled or removed and replaced). Thus, setting the processing parameters of the process tool 30 to the same processing parameters as those used prior to maintenance may not produce the same desired wafer characteristics. As a result, after the process tool 30 is serviced (or maintained), the process tool 30 is allowed to qualify for additional wafer production, and the process tool 30 is guaranteed to produce wafers meeting the specifications The tuning process is performed.

The various process parameters of the process tool 30 are tuned during the tuning process. The process parameters of the process tool 30 are suitably tuned to ensure that the process tool 30 produces wafers with the desired wafer characteristics. Thus, the tunable process parameters of the process tool 30 are correlated with various wafer characteristics. Figure 2 shows a schematic diagram of various tunable process parameters correlated with process monitor items (wafer characteristics) according to various aspects of the present disclosure. For example, the process tool 30 may be configured to process X ₁ , X ₂ , X ₃ ,. . . X _N < / RTI > When the process tool 30 is a CVD epitaxial tool, tunable process parameters include heater power (to adjust the chamber temperature and / or wafer temperature), vacuum power settings (to adjust the chamber pressure), radio frequency power, Bias power, distance between the topmost electrode of the CVD epitaxial tool and the processed wafers, gas flow rate, and other tunable process parameters. X ₁ , X ₂ , X ₃ ,. . . , Each of the tunable parameters, such as process X is _{N Y 1, Y 2, Y 3} , ... Y M and the like, indirectly or directly to the output of each process of the wafer processing monitor item (wafer characteristics) It affects. For example, if the process tool 30 is a CVD epitaxial tool, the process monitor items (wafer characteristics for process monitoring) may be selected based on the thickness of the epitaxial features, the sheet resistance of the epitaxial features, the stress of the epitaxial features, Critical dimensions of the faceted features, profiles of the epitaxial features, and other wafer characteristics. In this example, in order to facilitate the following discussion, the CVD epitaxial tool comprises four tunable process parameters X ₁ , X ₂ , X ₃ , X ₄ and three process monitor items Y ₁ , Y ₂ , Y ₃ ). The four tunable process parameters (silicon-containing precursor gas (e.g., SiH ₄₎ and the like) gas flow rate of the precursor gas, a high frequency radio frequency (high frequency radio frequency; HFRF) power, deposition time (DepTime), and the distance ( The distance between the top electrode of the CVD epitaxial tool and the wafer during processing). The four tunable process parameters are based on three process monitor items (wafer characteristics): stress S of the epitaxial features, sheet resistance RI of the epitaxial features, and thickness THK of the epitaxial features Directly or indirectly. Thus, during the tuning process, the gas flow rate, HFRF power, deposition time, and spacing of the precursor gases are tuned to achieve the desired thickness, sheet resistance, and stress of the epitaxial features. The tuned gas flow rate, HFRF power, deposition time, and spacing are referred to as process parameter tool sets.

The process tool 30 is often manually tuned. For example, an engineer familiar with the process tool 30 and its associated processes performs trial and error techniques, in which case various combinations of process parameters (different process parameter tool settings) to achieve desired wafer characteristics, (I. E., The process monitors whether these parameters meet the specification). In the case of outdated process technologies, this passive tuning is inadequate because the process windows for outdated process techniques are often relatively wide so that passive tuning can be achieved in a reasonable time frame. However, in the case of advanced processing techniques, as the technology nodes decrease, the number of process monitor items (Y _M ) increases as the process windows are narrowed (i. E., Narrower ranges between the upper and lower limits). Thus, passive tuning is very time consuming. The engineer may spend several hours or even days to make the process tool 30 eligible for production. Further, because the engineer generally implements the tuning method based on the individual experience of the engineer when qualifying the process tools, the process tool 30 may be differently qualified (or tuned) by different engineers, The processor 30 implements the different process parameter tool settings to achieve the desired wafer characteristics. Over time, the operation of the process tool 30 will drift differently depending on the process parameter tool settings implemented by different engineers. Since the tuned process parameter tool settings are not standardized, the performance of the process tool 30 accordingly depends on the engineer who tuned the process tool 30. [ More recently, complex statistical tuning for tuning the process tool 30 has been implemented. Statistical tuning methods generally require high computational power and time. Furthermore, the tuned process parameter tool settings are generally random, which creates different process parameter tool tuning solutions each time the statistical tuning method is driven, and tuned process parameter tool settings are used to determine the actual It does not generally consider whether it is feasible or practical in action.

The tool tuning system 80 solves many of the problems experienced by current tool tuning methodologies. In particular, as will be described in greater detail below, the tool tuning system 80 automatically determines (or calculates) the best combination of process parameters for the process tool 30. The tool tuning system 80 (and associated methods) provides convergent, feasible, and practical tool tuning process parameter solutions. The tool tuning system 80 thus provides a cost-effective and time-efficient way to make the process tool 30 eligible for production. FIG. 3 is a block diagram of a tool tuning process flow 100 implemented by the integrated circuit manufacturing system 10 of FIG. 1 in accordance with various aspects of the present disclosure, and FIG. 4 is a block diagram of a tool tuning process flow 100 according to various aspects of the present disclosure Figure 2 is a block diagram of a tool tuning optimization process flow 200 that may be implemented in the tool tuning process flow 100. In this example, the tool tuning system 80 includes a tool tuning optimization process flow 200 for optimizing tuning of a process tool 30 (in this example, a CVD epitaxial tool) for manufacturing integrated circuit devices, Flow < / RTI > 5 illustrates a tool tuning process flow 100 of FIG. 3 for generating optimal sets of process parameter tools for tuning a process tool 30, such as a CVD epitaxial tool, in accordance with various aspects of the present disclosure. And tool tuning optimization process flow 200 of FIG. 4 are implemented by tool tuning system 80. FIG. Figures 3-5 will be discussed simultaneously, and Figures 3-5 have been simplified for clarity so as to better understand the inventive concepts of the present disclosure. Additional steps and / or features may be provided in the tool tuning process flow 100 and the tool tuning optimization process flow 200, and some of the described steps and / or features may be provided by the tool tuning process flow 100 and tool And may be replaced or removed for additional embodiments of the tuning optimization process flow 200. Further, the discussion below with respect to tuning the CVD epitaxial tool is for illustrative purposes only and may be applied to any type of process tool 30 and tool tuning process < RTI ID = 0.0 > It is contemplated that flow 100 and tool tuning optimization process flow 200 may be implemented by tool tuning system 80.

At block 105 of FIG. 3, maintenance (service provisioning) is performed on the process tool 30. At block 110, a process tool performance check 110 is performed to evaluate the performance of the process tool 30. For example, various process parameters of the process tool 30 are set according to a process recipe (a set of process parameter tools intended to achieve the desired wafer characteristics), and the process tool 30 sets a set of wafers And the wafer characteristics of the set of processed wafers are collected, compiled and evaluated to determine whether a set of processed wafers represents the desired wafer characteristics. A set of wafers may be referred to as a test wafer set. In the example, a set of process parameter tools intended to achieve the desired wafer characteristics is a set of process parameter tools that were implemented by the process tool 30 prior to maintenance. In this example, the process recipe includes a set of process parameter tools defining the interval, gas flow rate, HFRF power, and deposition time for processing a set of test wafers; The spacing, the gas flow rate, the HFRF power, and the deposition time of the CVD epitaxial tool are set according to the process recipe; A CVD epitaxial tool processes a set of wafers to form epitaxial features.

The data associated with the processed test wafer set is collected and compiled in time series charts (T charts). This data thus includes process parameter data associated with the process parameters implemented by the process tool 30 to form the epitaxial features of each wafer. As mentioned above, the parameters may be selected from the group consisting of chamber pressure, chamber temperature, wafer temperature, gas flow, deposition time (voltage, current, power, resistance, Such as the various characteristics of the < / RTI > The data also includes process monitor item (wafer) data associated with the processed wafers, such as sheet resistance, thickness, and stress of the epitaxial features of the processed wafers, other wafer characteristics, or combinations thereof. Such process monitor item data and process parameter data may be collected and stored in a database 25 or other database associated with the process tool 30. The statistical analysis is then performed on time series data (T chart), thereby reducing the amount of data for evaluation, and the performance of the process tool 30 is evaluated based on statistically analyzed time series data. For example, the statistical process control can be used to control time-series process parameter data and process monitor item (wafer) data to control charts (e.g., Xbar-R control chart, Xbar-S control chart, I- A control chart, a Z control chart, other control charts, or a combination thereof), and these control charts can be used to evaluate whether wafer characteristics are within specification. Control charts analyze time series data according to statistics and process limits are defined by statistical analysis (such as standard deviation of analyzed data). For example, the control charts include a centreline representing the average value of the analyzed data and a control upper limit (maximum value) and a lower control limit (minimum value) defined by the statistical analysis, particularly within a plurality of standard deviations of the analyzed data do. In this example, in FIG. 5, an exemplary table 300 of analyzed process monitor item (wafer) data provides information relating to the stress, sheet resistance, and thickness of the epitaxial features of the test wafer set. The table 300 defines target wafer characteristics with target stress (S _target ) -3 GPa, target sheet resistance (RI _target ) 2 ohms / square, and target thickness (T _{target )} 510 nm. The wafer characteristics of the test wafer set are included in the table 300 and the test stress (S _test ) is -3 GPa, the test sheet resistance (RI _test ) is 2 ohms / square and the test thickness (T _test ) to be. The bias (or difference) between the target stress and the test stress is zero, the bias (or difference) between the target sheet resistance and the test sheet resistance is 1, and the bias (or difference) between the target thickness and the test thickness is two. Table 300 also defines control limits for each of the wafer characteristics. For example, the stress within ± 5 of S _target is within specification, the sheet resistance within ± 5 of RI _target is within specification, and the thickness within ± 10 of T _target is within specification It is.

Referring again to FIG. 3, at block 115, process monitor item (wafer) data is evaluated to determine if this data is out of specification. For example, the test stress is evaluated to determine whether the test stress is within an acceptable range from the target stress, and the test sheet resistance is evaluated to determine whether the test sheet resistance is within an acceptable range for the target test sheet resistance , The test thickness is evaluated to determine whether the test thickness is within an acceptable range from the target thickness. If the process monitor item data is not out of specification (i.e., if it is in the specification), at block 120, the process tool 30 is released for production. If the process monitor item data is out of specification (i.e., outside of an acceptable range), the tool tuning process flow 100 may include an optimal process parameter for tuning the process tool 30 to achieve desired wafer characteristics The tool tuning optimization module 130 (also referred to as block 130) that implements the tool tuning optimization process flow 200 for determining the tool set. In this example, the tool tuning system 80 includes a tool tuning optimization model 130. The tool tuning optimization model 130 includes a tool tuning solution module 140 that generates various sets of process parameter tools for tuning the process tool 30 to achieve desired wafer characteristics. As noted above, for clarity, in the present example, for purposes of clarity, the inventive concepts of the present disclosure are better understood, the following discussion will be directed to forming epitaxial features indicative of the desired stress, sheet resistance, , The spacing for tuning the CVD epitaxial tool, the gas flow rate of the precursor gas (specifically the silicon-containing precursor gas (SiH ₄ )), the HFRF power, and the deposition time . Thus, at block 210 in Figure 4, the process tool 30 has N number of process parameters _{(X 1, X 2, X} 3,... X N) has the set of potential combinations C (N, n) is selected by the tool tuning solution module 140 so that the number n of these process parameters for tuning is selected. This discussion is not intended to be limiting, and disclosure of the present invention is not intended to be limited by the fact that the tool tuning optimization process flow 200 can determine optimal process parameter tool sets, including some process parameters, to achieve some desired wafer characteristics It can be conceived.

In the embodiment shown in Figures 3 and 4, the tool tuning solution module 140 includes an engineering-based knowledge module 142, a combination calculator module 144, a statistical optimizer 142, A module 146, and a robust buffer module 148. 4, the engineering-based knowledge module 142 may be used by the combination calculator module 144 and the statistical optimizer module 146 to apply the historical data and / (Defined by the engineer or tuning system 80 based on experience). These constraints define rules that can not be violated to ensure the desired wafer quality (desired wafer characteristics), based on the knowledge and experience of the engineer. The engineering-based knowledge module 142 is configured to determine how the process parameters are correlated with each other, how the process parameters are correlated with the process monitor items, and how constraints on the tuning of process parameters .

(1) constraints between process parameters define constraints on how process parameters are tuned with respect to each other (X to X constraints). For example, previous experience indicates that any gas flow rate change should be limited by the upper and lower limits to ensure that the etch of epitaxial features by the precursor gas remains within the specification. Thus, exemplary process parameter constraints for avoiding unwanted etching are < RTI ID = 0.0 >

-5?? SiH ₄ (Side) +? SiH ₄ (top)? 5

Where SiH ₄ (side) is the unit change in the gas flow rate of the silicon-containing precursor gas from the side of the process chamber, and SiH ₄ (top) is the temperature of the process chamber Is a unit change in the gas flow rate of the silicon-containing precursor gas from the uppermost stage.

(2) constraints between process parameters and process monitor items must be tuned based on the wafer characteristics of the set of processed wafers (X to Y constraints). For example, if the difference between the wafer characteristics exhibited by the set of processed wafers is within a defined range from the desired wafer characteristics, the process parameters affecting such wafer characteristics are disabled in the tuning. In the present example, previous experience indicates that the gas flow rate affects the sheet resistance of the epitaxial features and the RF power affects the stresses of the epitaxial features. Therefore, an exemplary process parameter / process monitor entry restriction sheet between the sheet resistance of the epitaxial feature of the set of the processed wafer (RI _test) and the desired sheet resistance (RI _target) to resist car-is (ΔRI = RI _test RI _target) If it is within the specified range, it can be specified that the tuning of the gas flow rate is disabled. E.g,

-0.001 < DELTA RI < 0.01, SiH ₄ is disabled in tuning.

Another exemplary process parameter / process monitor item constraint is that the stress difference (? S = S _test - S _target ) between the stress (S _test ) of the epitaxial features of the set of processed wafers and the desired stress (S _target ) , It can be specified that the tuning of the HFRF power is disabled. E.g,

If -0.2 <? S <0.2, SiH ₄ is disabled in tuning.

The process parameter / process monitor item constraints recognize that tuning of process parameters affecting such wafer characteristics should be limited to minimize wafer property changes during tuning when variations in wafer characteristics from the desired wafer characteristics are small .

(3) constraints between process monitor items

_{OOF = Min | W 1 * ΔS} + W 2 * ΔRI + W 3 * ΔT |

Which defines an optimization object function (OOF), such as that provided by &lt; RTI ID = 0.0 &gt;

Where W ₁ , W ₂ , and W ₃ are stress changes (ΔS = S _test - S _target ), sheet resistance change (ΔRI = RI _test - RI _target ), and thickness variations ([Delta] T = T _test - T _target ). Thus, the optimizer object function weighs the changes in the process monitors relative to each other. As discussed further below, the optimal set of process parameter tools minimize the value of the optimization object function, in which case the optimization object function is ideally equal to zero. In the present example, for the sake of discussion, if the thickness and stress of the epitaxial features are deemed important, the following object optimization functions (W ₁ = 6, W ₂ = 1, and W ₃ = 6), the sheet resistance can be "sacrificed "

OOF = Min | 6 *? S + 1 *? RI + 6 *? T |

At block 230 in Figure 4, the combination calculator module 144 generates a set of potential process parameter tool tuning solution sets. Figure 6 is a block diagram of a process flow 400 that may be implemented by the tool tuning solution module 140 to generate a set of potential process parameter sets in accordance with various aspects of the present disclosure. In this example, the combination calculator module 144 implements the process flow 400 for generating a set of potential process parameter sets at block 230. At block 410, the combination calculator module 144 generates potential process parameter values by determining how a single process parameter should be tuned to achieve a single target wafer characteristic (from a corresponding single test wafer characteristic). This generation can be effected either indirectly or directly by the process parameters (here, gas flow rate, HFRF power, deposition time, and spacing), respectively, to the process monitor items (here, the stresses, sheet resistance, and thickness of the epitaxial features) . Referring to FIG. For example at 5, the sensitivity table 310 is the process monitor entry for the changes in the selected process parameter for tuning _{(X 1, X 2, X} 3, X 4) (Y 1, Y ₂ , and Y ₃ ). The sensitivity table 310 shows how the unit change in each process parameter affects each process monitor item, respectively. For example, the sensitivity table 310 does not affect stress, changing the gas flow rate, and changing the gas flow rate by one unit increases the sheet resistance by 0.2 ohm / square and changes the gas flow rate by one unit To increase the thickness by 1 nm. Sensitivity table 310 also indicates that HFRF power affects stress, sheet resistance, and thickness. For example, for every two unit increments in HFRF power, the stress is reduced by -0.5 GPa, the sheet resistance is reduced by -0.1 ohm / square, and the thickness is reduced by -0.25 nm. The sensitivity table 310 also changes the deposition time (for every 0.1 unit change in deposition time, the thickness is increased by 100 nm) and the gap (for every unit change in spacing the thickness is reduced by 2 nm) Indicating that only thickness is affected. Sensitivity table 310 may include data associated with test wafers processed by process tool 30, historical data associated with wafers processed by process tool 30, engineer experience and / or knowledge, other factors, Or a combination thereof.

At block 410, the combination calculator module 144 uses the sensitivity table 310 to generate a one-to-one tuning table 320 indicating how each process parameter is tuned to achieve each target wafer characteristic. For example, the sensitivity table 310 indicates that tuning the gas flow rate, deposition time, and spacing does not affect stress, and the one-to-one tuning table 320 indicates that tuning of these process parameters It is not applicable (N / A). Further, because the test stress meets the target stress (table 300) and the changes in the HFRF power change the stress (table 310), the one-to-one tuning table 320 may be used to maintain the target stress, Indicating that no tuning (0) for the HFRF power is needed to maintain the target stress. For sheet resistance, the sensitivity table 310 indicates that tuning the deposition time and spacing does not affect the stress, and the one-to-one tuning table 320 indicates that tuning of these process parameters is applicable to achieve the target sheet resistance (N / A). Since the sheet resistance is higher than the target sheet resistance by 1 ohm / square (table 300) and the sensitivity table 310 indicates that every unit increase in gas flow rate increases the sheet resistance by 0.2 ohm / square, The one-to-one tuning table 320 indicates that reducing the gas flow rate by five units (achieving target sheet resistance by reducing the sheet resistance by 1 ohm / square) achieves target sheet resistance. Further, the sheet resistance is 1 ohm / square higher than the target sheet resistance (table 300), and the sensitivity table 310 shows that every two unit increases in HFRF power reduce the sheet resistance by -0.1 ohm / square , The one-to-one tuning table 320 indicates that increasing the HFRF power by 10 units (achieving target sheet resistance by reducing the sheet resistance by 1 ohm / square) achieves the target sheet resistance. Likewise, in the case of thickness, the one-to-one tuning table 320 has the effect of reducing the gas flow rate by two units (every unit increase in the gas flow rate is equal to Achieving the target thickness and increasing the HFRF power by 8 units (because every two unit increase in HFRF power reduces the thickness by 0.25 nm) is achieved by achieving a target thickness (as by increasing the thickness by 1 nm) Reducing the time by 0.02 units achieves the target thickness (because every 0.1 unit increase in deposition time increases the thickness by 100 nm), and increasing the gap by one unit (By reducing the thickness by 2 nm).

The combination calculator module 144 then executes the growth process 420 to create the table 330 to generate potential unit changes for each of the process parameters. For example, referring to FIGS. 5 and 6, the combination calculator module 144 may be configured to calculate 1, 0, -1, -2, -3, -4, -5, And-6 gas flow rates (SiH ₄ ). Table 330 includes potential unit changes provided in a one-to-one tuning table 320 (where -5 and -2 for gas flow rates). At block 422, the combination calculator module 144 evaluates potential unit changes for each process parameter provided in the one-to-one tuning table 320 to determine whether the potential unit changes include zero (0) do. If the potential unit changes do not include zero, then at block 424, zero is added to the potential unit changes and the growth process then continues to block 426. [ If the potential unit changes include zeros, the growth process 420 continues at block 426. In this example, potential unit value changes to the gas flow rate provided in the one-to-one tuning table 320 do not include zero, so zero is added to potential unit changes. At block 426, the combination calculator module 144 determines the average of the potential unit changes, which are included as potential unit changes. In this example, for gas flow, the potential unit changes now include -5, -2, and 0, and the average of the potential unit changes is approximately -2.333. The average rounds to the closest complete unit, so the average is -2. Since the potential unit changes already include -2, no additional potential unit changes are added to the potential unit changes at block 426. Alternatively or additionally, the median of potential unit changes is determined and included in potential unit changes. The growth process 420 continues to block 428, where the combination calculator module 14 expands potential unit changes by adding +1 and -1 units to each potential unit value. Thus, in this example, the unit is added and subtracted from -5 to bring about potential unit changes of -4 and -6, and the unit is added from -2 to get -3 and -1 potential unit changes And the unit is added from 0 to subtract 1 and -1 as potential unit changes and subtracted. The combination calculator module 144 then calculates the quartiles for the potential unit changes (at this point, 1, 0, -1, -2, -3, -4, -5, and -6 And continues to the growth process 420 at block 428 by further extending the potential unit changes to the gas flow rate by including these quadrants as potential unit changes. As with determining the average (or median), quadrants are rounded to the nearest complete unit. In this example, the quadrants are already included as potential unit changes. Thus, the combination calculator module 144 generates potential unit changes 1, 0, -1, -2, -3, -4, -5, and -6 for the table 330. The combination calculator module 144 likewise implements the growth process 420 to further provide potential unit changes in HFRF power, deposition time, and spacing in table 330. By implementing the growth process 420, the combination calculator module 144 can calculate the approximate unit changes based on potential unit changes to the process parameters in the table 330 (generated after the trimming process 440) Create 256 potential process parameter tool set solutions. The combination calculator module 144 increases the number of potential tool tuning process parameter combinations from the number of potential tool tuning process parameter combinations generated using the table 320. [

The combination calculator module 144 then performs a trimming process 440 to generate table 340 to reduce potential unit changes for each of the process parameters. In this example, it is desirable to reduce the potential process parameter tool sets to less than about 200, and based on potential unit changes to process parameters in table 330, About 25 potential process parameter tool set solutions were created to tune the tool 30. 5 and 6, at block 442, the combination calculator module 144 determines that the set of potential process parameter tools exceeds a specified limit (here, There are 56 more potential parameter tool sets). The combination calculator module 144 evaluates the number of potential unit changes for each process parameter X _i to determine which process parameter &lt; RTI ID = 0.0 &gt;&Lt; / RTI &gt; have the most potential unit changes. In this example, both the gas flow rate and the HFRF power have the most potential unit changes (specifically, eight potential unit changes). At block 446, the combination calculator module 144 determines the absolute maximum potential unit change from the potential unit changes of the process parameter having the most potential unit changes (as determined at block 146) and the absolute minimum potential unit Determine the change. The absolute maximum potential unit change and the absolute minimum potential unit change are then trimmed from potential unit changes to the process parameters, unless such changes are equal to zero. For example, for gas flow, the absolute maximum potential unit change is 6, and the absolute minimum potential unit change is zero. Similarly, for HFRF power, the absolute maximum potential unit change is 12, and the absolute minimum potential unit change is zero. Accordingly, a potential unit change of -6 is removed from the potential unit changes to the gas flow rate, and thus 12 potential unit changes are removed from potential unit changes to the HFRF power. By implementing the trimming process 440, the combination calculator module 144 can calculate the approximate unit changes based on the potential unit changes to the process parameters in the table 340 (generated after the trimming process 440) Create 196 potential process parameter tool set solutions. The combination calculator module 144 thus reduces the number of potential tool tuning process parameter combinations from 256 to 196. [ The combination calculator module 144 then returns to block 442, where it is determined that the potential process parameter tool set solutions no longer exceed the prescribed limits.

Potential process parameter tool set solutions generated by the combination calculator module 144 in table 340 may be too aggressive, i.e., suggest too many changes to one or more process parameters. At block 448, the combination calculator module 144 cooperates with the robust buffering module 148 to rob potentially process parameter tool set solutions through robust buffering. The robust buffering module 148 corrects overly sensitive correlations (here, the corrections defined in the sensitivity table 310) between the various process monitor items for changes in process parameters at block 240 Define sensitivity adjustment criteria. In this example, the sensitivity adjustment criterion may be to discount the number of process parameter unit changes for each process parameter to further reduce the number of potential unit changes for each process parameter as provided in table 350 . For example, the sensitivity adjustment criterion defines a discount of 0.6, which means that the number of process parameter unit changes for each process parameter is reduced by 40%. In this example, since the gas flow rate has seven potential process parameter unit changes, such a discount indicates that each of these potential changes is rounded off in that unit and estimated as 60% of their original value. That is, the original set (1,0, -1, -2, -3, -4, -5) is discounted as (0.6,0, -0.6, -1.2, -1.8, -2.4, -3) 1, -1, -2, -2, -3), and then rounded off to (1,0, -1, -2, -3). Similarly, for potential parameter unit changes in HFRF power, (-2,0,2,4,6,8,10) is discounted to (-1.2,0,1.2,2.4,3.6,4.8,6) And is rounded off to (-1,0,1,2,4,5,6). Alternatively, the sensitivity adjustment criterion defines, for example, a weighted minimum function,

Where changes in process parameters (X) are weighted against changes in process monitor items (Y). By performing robust buffering at block 448, the combination calculator module 144 thus determines the number of potential process parameter tool set solutions based on potential unit changes to the process parameters in the table 350 To about 75.

The combination calculator module 144 also applies operational constraints criteria (as defined by the engineering-based knowledge module 142) to generate a set of potential tool tuning process parameter solutions. For example, as noted above, an exemplary operating constraint on the gas flow rate (SiH ₄ ) defines -5 ≤ ΔSiH ₄ (side) + ΔSiH ₄ (top) ≤ 5. Thus, any potential unit change in the gas flow rate of ± 5 is eliminated from consideration when generating potential tool tuning process parameter solutions. This constraint may be applied to potential unit changes provided in table 350. [ Further, as mentioned above, an exemplary operating constraint criterion is that when SiH ₄ is disabled in tuning and (2) -0.2 <ΔS <0.2 when (1) -0.001 <ΔRI <0.01, SiH ₄ is disabled in the tuning. In this example, the stress change (as indicated in table 300) is zero, and therefore the gas flow rate (SiH ₄ ) must be disabled in the tuning. This constraint can be applied at various times during the process flow to create a set of potential tool tuning process parameter solutions. For example, a potential unit change to the gas flow rate is initially set to zero, whereby the potential unit changes to the gas flow rate are stored in table 320, table 330, table 340, and table 350, Is generated by the combination calculator module 144 as shown in FIG. In another example, if the combination calculator module 144 creates the table 350, the set of potential tool tuning process parameter solutions only includes a combination of process parameters for which the gas flow rate is set to zero unit change. The disclosure of the present invention envisions alternative applications of operational constraints in generating a set of potential tool tuning process parameter solutions.

The combination calculator module 144 then determines all potential combinations of process parameters from the table 350 to generate a set of potential process parameter tool tuning solutions. At block 450, the combination calculator module 144 aligns the potential process parameter tool sets according to an intelligent sorting algorithm. In the example, the intelligent sorting algorithm is defined by the engineering based knowledge module 142. In this example, the combination calculator module 144 sorts and orders potential parameter tool sets from the minimum overall process parameter changes to the maximum overall process parameter changes. As in table 350, (SiF ₄ , HFRF, deposition time, interval) has 105 combinations, including C1 = (0,0,0,1) and (1,6,0,1) And C1 will be prioritized due to its minimum changes in all tuning parameters.

Turning now to Figures 3 and 4, the tool tuning optimization process flow 200 then continues to block 250 where the statistics optimizer module 146 determines a set of potential process parameter tool tuning solutions (in this example, An ordered set of potential process parameter tool sets from table 350) and narrows it down to a set of best-effort process parameter tool tuning solutions. The statistics optimizer module 146 applies operational constraints (block 220) defined by the engineering based knowledge module 142 when narrowing the set of potential parameter tool tuning solutions. For example, the statistics optimizer module 146 may include an optimization object function defined by the engineering-based knowledge module 142,

OOF = Min | 6 *? S + 1 *? RI + 6 *? T |

Process parameters to minimize tooltuning Evaluate a set of potential process parameter tool tuning solutions to find solutions.

The statistics optimizer module 146 uses any suitable statistical optimizer. For example, the statistics optimizer module 146 implements a multiple input / multiple output (MIMO) optimizer as described in U.S. Patent Application Publication No. 2012/0130525 entitled " Adaptive and Automatic Determination of System Parameters " Incorporates the entire contents of this document as a reference. Because the combination calculator module 144 provides a set of narrowly tailored and aligned potential process parameter tool tuning solutions, the statistics optimizer module 146 is able to optimize the potential potential process parameter tools &lt; RTI ID = 0.0 &gt; A set of tuning solutions can be created.

At block 260, the optimal process parameter tool tuning solutions are ranked according to some criteria of the final results of the optimized OOF values. For example, optimal process parameter tool tuning solutions are ranked according to the rating criteria defined by the engineering based knowledge module 142 and / or the robust buffer module 148. Thereafter, at block 270, some of the optimal process parameter tool tuning solutions are also proposed to tune the process tool, based on some criteria as well, such as when the best solution is selected or the top three Average is proposed. Returning to Fig. 3, the process tool 30 is tuned according to one of the proposed optimal process parameter tool tuning solutions. The tool tuning process flow 100 then returns to block 110 where the wafers processed by the process tool 30 when the process tool 30 is configured according to the optimal process parameter tool tuning solution, Another process tool performance check is performed to determine whether to represent process monitor items. If the process monitor items are in the specification, at block 120, the process tool is released for production and the process tool 30 processes the wafers. If the process monitor items are not in the specification, the tool tuning optimization process is repeated until the process tool 30 is released for production at block 130.

7 is a flow diagram of a method 500 for tuning process parameters of a process tool such that wafers processed by a process tool exhibit desired process monitor items in accordance with various aspects of the present disclosure. In the example, a method 500 is used to tune the process tool 30 in the integrated circuit device manufacturing system 10 of FIG. At block 510, a set of potential tool tuning process parameter combinations is generated as described above with reference to Figures 1-6. The set of potential tool tuning process parameter combinations includes process monitor item data associated with the wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to each process parameter, operating constraint criteria, and sensitivity adjustment criteria . At block 520, a set of optimal tool tuning process parameter combinations is generated from the set of potential tool tuning process parameter combinations described above with reference to Figures 1-6. At block 530, the process tool is configured according to one of the optimal tool tuning process parameter combinations described above with reference to Figures 1-6. The method 500 may further comprise processing the wafers with a process tool configured according to one of the optimal tool tuning process parameter combinations. Additional steps may be provided before, during, and after method 500, and some of the described steps may be replaced, removed, or moved for other embodiments of method 500.

8 is a block diagram of a computer system 600 for implementing the various methods and systems described herein, e.g., various method blocks of the methods 100, 200, 400, 500 discussed above. For example, the computer system 600 is operable to determine an optimal set of tool tuning process parameters for tuning a process tool, such as the process tool 30. In this example, the tool tuning system 80 includes a computer system 600 for tuning the process tool 30. In various implementations, the devices of computer system 600 may include a network communication device or network computing device (e.g., mobile cellular phone, laptop, personal computer, network server, etc.) capable of communicating with a network . It should be appreciated that each of these devices may be implemented as a computer system 600 for communicating with a network in the following manner.

According to various embodiments of the present disclosure, a computer system 600, such as a local computer or a networked computer system, includes a bus component 602 or other communication mechanism for communicating information, A system memory component 606 (such as a processor, a microcontroller, a digital signal processor (DSP), other processing components, or a combination thereof), a system memory component 606 Static storage component 808, a disk drive component 610, a network interface component 612 (e.g., a modem, an Ethernet card, other network interface Component, or combination thereof), a display component 614 (e.g., a cathode ray tube (CRT), a cathode ray tube An input component 616 (such as a keyboard or a mouse), a cursor control component 618 (such as a mouse or trackball), and a display component (such as an analog or digital display) Such as an image capture component (e. G., A camera). In one implementation, the disk drive component 610 includes a database having one or more disk drive components.

In accordance with embodiments of the present disclosure, computer system 600 performs certain operations by processor 604 executing one or more sequences of one or more instructions contained in system memory component 606. [ In the example, these instructions are read from the system memory component 606 from another computer readable medium, such as the static storage component 608 or the disk drive component 610. In yet another example, a hardwired circuit is used in place of (or in combination with) software instructions to implement the present disclosure. Also in accordance with embodiments of the present disclosure, logic is encoded on a computer readable medium, which refers to any medium that participates in providing instructions for execution to the processor component 604. [ Such media include, but are not limited to, nonvolatile media and many forms of volatile media. In the example, the computer readable medium is non-transient. In various implementations, non-volatile media includes optical or magnetic disks, such as disk drive component 610, and volatile media includes dynamic memory, such as system memory component 606. In one aspect, the data and information associated with executing the instructions are transmitted to the computer system 600 via a transmission medium, such as in the form of sound waves or light waves, including those generated during radio wave and infrared data communications. In various implementations, the transmission medium includes coaxial cables, copper wires, and optical fibers, as well as wires including bus 602.

Some common forms of computer-readable media include, but are not limited to, a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, any other optical medium, A ROM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave, or any other medium that a computer is configured to read. In various embodiments of the present disclosure, computer system 600 performs the execution of instruction sequences to effectuate disclosure of the present invention. Various computer systems, such as computer system 600, may be coupled to communication link 630 (e.g., a communication network such as a LAN, WLAN, PTSN, and / Phone networks, as well as other wired or wireless networks), and cooperate with one another to execute the command sequences to effectuate disclosure of the present invention. In various examples, computer system 600 sends and receives messages, data, information, and instructions, including one or more programs (i. E., Application code), via communication link 630 and communication interface 612. The processor component 804 may execute program code for execution that is received and / or stored in the disk drive component 610 or some other non-volatile storage component.

Where applicable, the various embodiments provided by the present disclosure are implemented using hardware, software, or a combination of hardware and software. Also, where applicable, the various hardware components and / or software components described herein are combined into a composite of components, including software, hardware, and / or both, without departing from the spirit of the present disclosure. Where applicable, the various hardware components and / or software components described herein are separated into subcomponents including software, hardware, and / or both, without departing from the scope of the present disclosure. Also, where applicable, software components may be implemented as hardware components, or vice versa. Software such as computer program code and / or data in accordance with the teachings of the present invention may be stored on one or more computer readable media. The software identified herein may be implemented using one or more general purpose or special purpose computers and / or computer systems when networked and / or otherwise. Where applicable, the order of the various steps described herein is combined into multiple steps to provide the features described herein, and / or modified to separate into sub-steps.

In order that those skilled in the art can better understand aspects of the disclosure of the present invention, the features of the several embodiments have been described above. Those skilled in the art will readily appreciate that the disclosure of the present invention is readily available to those skilled in the art, as a basis for designing or modifying other processes and structures to accomplish the same objectives of the embodiments set forth herein and / or to achieve the same advantages You should know. Those skilled in the art will also appreciate that such equivalent constructions do not depart from the spirit and scope of the disclosure, and that various modifications, substitutions, and alterations will occur to those skilled in the art without departing from the spirit and scope of the disclosure. We must be aware that we can do it in the invention.

Claims (10)

A method of tuning process parameters of a process tool such that wafers processed by the process tool exhibit predetermined process monitor items,
Defining an action restriction criterion and a sensitivity adjustment criterion;
A process tool parameter associated with the process monitor item data associated with wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to each process parameter, the action constraint criteria, Generating a first set of possible combinations of the plurality of possible combinations;
Generating a second set of combinations of tool tuning process parameters from a first set of possible combinations of tool tuning process parameters based on the operating constraint criteria; And
Configuring the process tool according to a combination of tool tuning process parameters in a second set of combinations of tool tuning process parameters
Gt; a &lt; / RTI &gt; process tool. The method according to claim 1,
N process parameters are associated with the process tool,
Selecting n process parameters out of the N process parameters for tuning such that the first set of possible combinations of tool tuning process parameters includes various tool tuning combinations for n process parameters
Wherein N is greater than n. &Lt; Desc / Clms Page number 12 &gt; 2. The method of claim 1, wherein generating a first set of possible combinations of tool tuning process parameters comprises:
Generating a first number of possible combinations of the tool tuning process parameters;
Generating a second number of possible combinations of the tool tuning process parameters from the first number of possible combinations of tool tuning process parameters; And
Generating a third number of possible combinations of the tool tuning process parameters from the second number of possible combinations of tool tuning process parameters
Wherein the second number is greater than the first number and the third number is less than the second number. 4. The method of claim 3, wherein generating a first set of possible combinations of tool tuning process parameters further comprises: selecting a fourth possible number of tool tuning process parameters from the third number of possible combinations of tool tuning process parameters, Wherein the fourth number is less than the third number. &Lt; Desc / Clms Page number 19 &gt; 4. The method of claim 3, wherein generating the first number of possible combinations of tool tuning process parameters comprises using the process monitor item data and the sensitivity data to generate a set of process parameter values for each process parameter The method comprising the steps &lt; RTI ID = 0.0 &gt; of: &lt; / RTI &gt; The method according to claim 1,
The action constraint criterion defining an object function;
Wherein generating a second set of combinations of tool tuning process parameters from a first set of possible combinations of tool tuning process parameters comprises determining combinations of the tool tuning process parameters that minimize the value of the object function The process parameters of the process tool. 2. The method of claim 1, wherein defining the action constraint criteria comprises:
Defining correlations between the process parameters that define tuning of the process parameters;
Defining correlations between the process monitor items that define the tuning of the process parameters; And
And defining correlations between the process parameters and the process parameters that define the tuning of the process parameters. A method for tuning a process tool,
Processing wafers with a process tool; And
Determining whether the process monitor item data associated with the wafers processed by the process tool is in the specification,
If the process monitor item data is within the specification, emit the process tool for production,
And tuning a process parameter tool set of the process tool if the process monitor item data is not in the specification,
Defining an action restriction criterion and a sensitivity adjustment criterion;
Wherein the process monitoring tool is operative to: receive the process monitor item data associated with the wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to the respective process parameters, Generating a first set of possible combinations of the plurality of possible combinations;
Generating a second set of combinations of tool tuning process parameters from a first set of possible combinations of tool tuning process parameters based on the operating constraint criteria;
Configuring the process tool according to a combination of tool tuning process parameters in a second set of combinations of tool tuning process parameters; And
Processing the wafers until the process tool is released for production and repeating the step of determining whether process monitor item data associated with wafers processed by the process tool are within specifications
Gt; a &lt; / RTI &gt; process tool. 9. The method of claim 8,
Performing the processing and maintenance on the process tool prior to determining whether the process monitor item data associated with the wafers processed by the process tool is in the specification
Further comprising the steps of: In an integrated circuit manufacturing system,
A process tool configured to perform a process on wafers; And
A process tool tuning system configured to determine a set of process parameter tools for the process tool,
Using process monitor item data associated with wafers processed by the process tool, sensitivity data associated with the sensitivity of the process monitor items to each process parameter, predefined operating constraint criteria, and predefined sensitivity adjustment criteria Generating a first set of possible combinations of tuning process parameters,
And a tool tuning solution module configured to generate a second set of combinations of tool tuning process parameters from a first set of possible combinations of tool tuning process parameters based on the operating constraint criteria.

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