WO2002082534A2

WO2002082534A2 - Method and apparatus for incorporating in-situ sensors

Info

Publication number: WO2002082534A2
Application number: PCT/US2002/008037
Authority: WO
Inventors: Christopher A. Bode; Thomas J. Sonderman
Original assignee: Advanced Micro Devices, Inc.
Priority date: 2001-04-06
Filing date: 2002-02-28
Publication date: 2002-10-17
Also published as: TW569302B; WO2002082534A3

Abstract

The present invention provides for a method and an apparatus for incorporating in-situ sensors into a semiconductor manufacturing process. At least one semiconductor device (105) is processed. An in-situ sensor analysis is performed upon the processed semiconductor device (105). A subsequent process is performed on at least one semiconductor device (105) in response to the in-situ sensor analysis.

Description

METHOD AND APPARATUS FOR INCORPORATING IN-SITU SENSORS

TECHNICAL FIELD

This invention relates generally to semiconductor manufacturing, and, more particularly, to a method and apparatus for utilizing in-situ sensors to perform feedback control functions for semiconductor manufacturing.

BACKGROUND ART The technology explosion in the manufacturing industry has resulted in many new and innovative manufacturing processes. Today's manufacturing processes, particularly semiconductor manufacturing processes, call for a large number of important steps. These process steps are usually vital, and therefore, require a number of inputs that are generally fine-tuned to maintain proper manufacturing control.

The manufacture of semiconductor devices requires a number of discrete process steps to create a packaged semiconductor devices from raw semiconductor material. The various processes, from the initial growth of the semiconductor material, the slicing of the semiconductor crystal into individual wafers, the fabrication stages (etching, doping, ion implanting, or the like), to the packaging and final testing of the completed device, are so different from one another and specialized that the processes may be performed in different manufacturing locations that contain different control schemes.

Among the factors that affect semiconductor device manufacturing are effectively initiating and continuing a manufacturing process without significant human interaction, which can cause delays or errors in the manufacturing process. One of the process steps that is adversely affected by such factors is the photolithography overlay process. Overlay is one of several important steps in the photolithography area of semiconductor manufacturing. Overlay control involves measuring the misalignment between two successive patterned layers on the surface of a semiconductor device. Generally, aligning these layers ensures that the multiple layers of the semiconductor devices are connected and functional. As technology facilitates smaller critical dimensions for semiconductor devices, the need for reducing misalignment errors increases dramatically.

Generally, photolithography engineers currently analyze the overlay errors a few times a month. The results from the analysis of the overlay errors are used to make updates to exposure tool settings manually.

Some of the problems associated with the current methods include the fact that the exposure tool settings are only updated a few times a month. Furthermore, currently the exposure tool updates are performed manually. Similarly, improvements in error prevention and correction in other types of semiconductor manufacturing processes are also needed to improve yields in semiconductor manufacturing processes.

Generally, a set of processing steps is performed on a lot of wafers on a semiconductor manufacturing tool called an exposure tool or a stepper. The manufacturing tool communicates with a manufacturing framework or a network of processing modules. The manufacturing tool is generally connected to an machine interface. The machine interface is connected to a machine interface to which the stepper is connected, thereby facilitating communications between the stepper and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. The input parameters that control the manufacturing process are revised periodically in a manual fashion. As the need for higher precision manufacturing processes are required, improved methods are needed to revise input parameters that control manufacturing processes in a more automated and timely manner. Furthermore, wafer-to-wafer manufacturing variations can cause non-uniform quality of semiconductor device . Generally, the machine interface has to wait for a user input to begin a semiconductor manufacturing process, which may result in lost production time. Furthermore, the user input received by the machine interface may contain errors, which can result in manufacturing problems. Current manufacturing feedback mechanisms tend to address errors at a maximum rate of one manufacturing lot at a time, which may be too infrequent to address some manufacturing errors.

The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.

DISCLOSURE OF INVENTION In one aspect of the present invention, a method is provided for incorporating in-situ sensors into a semiconductor manufacturing process. At least one semiconductor device is processed. An in-situ sensor analysis is performed upon the processed semiconductor device. A subsequent process is performed on at least one semiconductor device in response to the in-situ sensor analysis.

In another aspect of the present invention, an apparatus is provided for incorporating in-situ sensors into a semiconductor manufacturing process. The apparatus of the present invention comprises: a computer system; a manufacturing model coupled with the computer system, the manufacturing model being capable of generating and modifying at least one control input parameter signal; a machine interface coupled with the manufacturing model and the computer system, the machine interface being capable of receiving process data from the manufacturing model and the computer system; a processing tool coupled with the machine interface, the processing tool being capable of receiving at least one control input parameter signal from the machine interface and performing a manufacturing process and performing an inline sensor data acquisition; a metrology tool coupled with the processing tool, the metrology tool being capable of acquiring offline metrology data; and a metrology data processing unit coupled with the metrology tool and the processing tool, the metrology data processing unit being capable of organizing and analyzing the acquired inline sensor data and the offline metrology data and calculating at least one manufacturing error for generating modification data.

BRIEF DESCRIPTION OF THE DRAWINGS The invention may be understood by reference to the following description taken in conjunction with the accompanying drawings, in which like reference numerals identify like elements, and in which: Figure 1 illustrates a system in accordance with one embodiment of the present invention; Figure 2 illustrates a feedback path for performing feedback corrections in accordance with one embodiment of the present invention;

Figure 3 illustrates a simplified diagram of a processing tool used by the system in accordance with one embodiment of the present invention;

Figure 4 illustrates a flowchart depiction of a method in accordance with a first embodiment of the present invention;

Figure 5 illustrates a flowchart depiction of a method of performing a first in-situ sensor analysis described in Figure 4, in accordance with the first embodiment of the present invention; Figure 6 illustrates a flowchart depiction of a method in accordance with a second embodiment of the present invention;

Figure 7 illustrates a flowchart depiction of a method of performing a second in-situ sensor analysis described in Figure 6, in accordance with the second embodiment of the present invention; Figure 8 illustrates a flowchart depiction of a method of performing a merging function, in accordance with one embodiment of the present invention; and

Figure 9 illustrates a flowchart depiction of a method of performing a correlation and analysis of the in-situ sensor data and the metrology tool data described in Figure 8, in accordance with one embodiment of the present invention. While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the description herein of specific embodiments is not intended to limit the invention to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

MODE(S) FOR CARRYING OUT THE INVENTION Illustrative embodiments of the invention are described below. In the interest of clarity, not all features of an actual implementation are described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

There are many discrete processes that are involved in semiconductor manufacturing. Many times, semiconductor devices are stepped through multiple manufacturing process tools. Wafer-to-wafer, or wafer-lot to wafer-lot, variations can result in an output of non-uniform semiconductor devices. Furthermore, fast, efficient, and accurate identification of materials that are ready for manufacturing process can improve overall results in semiconductor manufacturing environments. Embodiments of the present invention provide for performing a wafer-to-wafer error analysis using an inline or an in-situ sensor. In one embodiment the inline sensor taught by the present invention is a metrology data acquisition tool that is integrated into a processing tool. In an alternative embodiment, the inline sensor taught by the present invention is a metrology data acquisition tool that is integrated into a processing tool and capable of acquiring real-time metrology data acquisition.

Semiconductor devices are processed in a manufacturing environment using a number of input control parameters. Turning now to Figure 1, a system 100 in accordance with one embodiment of the present invention is illustrated. In one embodiment, semiconductor products 105, such as semiconductor wafers, are processed on processing tools 120a, 120b using a plurality of control input signals, or manufacturing parameters, on a line 123. In one embodiment, control input signals, or process signals, on the line 123 are sent to the processing tools 120a, 120b from a computer system 130 via machine interfaces 1 15a, 1 15b. In one embodiment, the first and second machine interfaces 1 15a, 1 15b are located outside the processing tools 120a, 120b. In an alternative embodiment, the first and second machine interfaces 115a, 1 15b are located within the processing tools 120a, 120b.

In one embodiment, the computer system 130 sends control input signals on the line 123 to the first and second machine interfaces 1 15a, 1 15b. The computer system 130 employs a manufacturing model 140 to generate the control input signals on the line 123. In one embodiment, the manufacturing model 140 contains a manufacturing recipe that determines a plurality of control input parameters that are sent on the line 123. The computer system also comprises a metrology data processing unit to process metrology data.

In one embodiment, the manufacturing model 140 defines a process script and input control that implement a particular manufacturing process. The control input signals on a line 123 that are intended for processing tool A 120a are received and processed by the first machine interface 115a. The control input signals on a line 123 that are intended for processing tool B 120b are received and processed by the second machine interface 115b. Examples of the processing tools 120a, 120b used in semiconductor manufacturing processes are steppers, scanners, step-and-scan tools, and etch process tools. In one embodiment, processing tool A 120a and processing tool B 120b are photolithography process tools, such as steppers. In one embodiment, one or more processed semiconductor wafers are examined by an inline sensor

125. The inline sensor 125 is capable of acquiring metrology data from the processed semiconductor wafer, one wafer at a time. Therefore, metrology data acquired from each processed semiconductor wafer can be sent back to the system 100 and processed for immediate corrective feedback. This allows the system 100 to make real time corrective feedback with a manufacturing lot of wafers. Furthermore, embodiments of the present invention allow for merging of the inline sensor data and metrology data acquired by external metrology tools 150, thereby providing even more accurate feedback results.

One or more of the semiconductor wafers that are processed by the processing tools 1 10a, 1 10b can also be sent to a metrology tool 150 for acquisition of metrology data. The metrology tool 150 can be a scatterometry data acquisition tool, an overlay-error measurement tool, a critical dimension measurement tool, and the like. Data from the metrology tool 150 and the inline sensor 125 are correlated and organized according to the appropriate processed semiconductor wafers by an "inline sensor and metrology tool data correlator" 160. In one embodiment, the inline sensor and metrology tool data correlator 160 correlates the inline sensor data and the metrology tool data to a particular processed semiconductor wafer or group of wafers. In one embodiment, the inline sensor and metrology tool data correlator 145 is integrated into the computer system 130. In one embodiment, the inline sensor and metrology tool data correlator 145 is a computer software program embedded into the computer system 130, wherein the computer system 130 is integrated within an APC framework. A "correlated manufacturing data analyzer" 170 analyzes the correlated metrology data and prompts the system to implement corrective measures. In one embodiment, the correlated manufacturing data analyzer 170 is a computer software program embedded into the computer system 130 Among the bases for modifying the control input signal on the line 123 are metrology measurements, inline and external, performed on processed semiconductor wafers, such as scatterometry measurements. The metrology measurements are used to perform a feedback modification of the control input signals. In one embodiment, the feedback modification of the control input signals on the line 123 are performed on photolithography processes, such as line width adjustments using photo exposure dosages and line profile adjustments using exposure focus modifications. Feedback modification of the control input signal on the line 123 can also be performed on etch processes, such as etch line shape adjustments using etch recipe modifications.

In the context of a manufacturing process such as a stepper process, the control inputs on the line 123 that are used to operate the processing tool 120 include an x-translation signal, a y-translation signal, an x- expansion wafer scale signal, a y-expansion wafer scale signal, a reticle magnification signal, and a reticle rotation signal. Generally, errors associated with the reticle magnification signal and the reticle rotation signal relate to one particular exposure process on the surface of the wafer being processed in the exposure tool.

Turning now to Figure 2, one embodiment of a feedback loop in accordance with the present invention, is illustrated. The processing tool 120a is coupled with the system 100 via the machine interface 115a. The machine interface 115a comprises a machine control unit 250, which is capable of controlling the behavior of the processing tool 120a based upon data received from a shared database 230. The shared database 230 contains process data that can be utilized by the machine interface semiconductor manufacturing processes using the processing tool 120a. In one embodiment, the shared database 230 is integrated into the computer system 130. In one embodiment, the process data comprises manufacturing parameters, feedback data, and the like. The shared database 230 can be accessed by a plurality of processing tools like the processing tool 120, or multiple portions within the processing tool 120a. A control system interface 210 provides a path for the machine interface 115a to communicate with various portions of the system 100, such as the computer system 130, the inline sensor and metrology tool data correlator 160, and the correlated manufacturing data analyzer 170. The computer system 130 is capable of modifying or placing process data into the shared database 230 via the control system interface 210.

The shared database 230 comprises a control algorithm 220. The processing tool 120a is capable of communicating with the control algorithm 220 via the machine interface 1 15a. In one embodiment the processing tool 120a sends its present process settings to the control algorithm 220, which then utilizes this information to determine new calculations for subsequent semiconductor processing. In one embodiment, the control algorithm 220 determines the new calculations based upon feedback received from measurements of previously processed semiconductor wafers. The control algorithm 220 is capable of modifying recipe settings for manufacturing processes, photolithography exposure time, etch rates for etching processes, and the like. The control algorithm 220 modifies these settings by utilizing feedback information relating to errors or perceived errors relating to previously processed semiconductor wafers. Therefore, the control algorithm 220 is capable of performing run-to-run control-type modifications, which comprise modifying control settings based upon feedback error data for one process run to another process run.

Furthermore, the control algorithm 220 can also perform wafer-to-wafer control modifications based upon in-situ sensor measurements of processed manufacturing data. In one embodiment, the feedback loop, in accordance with the present invention includes the tool 120a performing a manufacturing process and delivering in-situ sensor measurements to the control system interface 210 via the machine interface 1 15a. The control system interface 210 then forwards the data from the processing tool 120a to the system 100, where control modifications and calculations are performed. The control system interface 210 then sends updated process data to the shared database 230. The control algorithm 220 is capable of implementing the modified control data, which is sent to the processing tool 120a via the machine interface 1 15a. The machine controller unit 250 and the machine interface 1 15a then perform appropriate control modifications to perform subsequent manufacturing processing in the processing tool 120.

Turning now to Figure 3, a block diagram representation of the processing tool 120a, in accordance with one embodiment of the present invention, is illustrated. In one embodiment, the processing tool 120a comprises a processing portion 310, a sensor portion 320, a sensor data analysis unit 330, and a communication interface 340. The processing portion 310 is capable of performing semiconductor manufacturing processes, such as photolithography processes, etching processes, Rapid Thermal Anneal (RTA) processes, and the like. The sensor portion 320 in the processing tool 120 is capable of acquiring inline or in-situ metrology data (sensor data) from the semiconductor wafers processed by the processing portion 310. The sensor data analysis unit 330 receives sensor data from the sensor portion 320 and performs an analysis of the sensor data. The sensor data analysis unit 330 is also capable of sending sensor data to the control system interface 210 via the processing tool communication interface 340. The processing tool communication interface 340 is capable of allowing the processing portion 310 and the sensor portion 320 to communicate with the shared database 230. Therefore, the shared database 230 can receive sensor data from the sensor portion 320, and send process data to the processing portion 310 for processing of semiconductor wafers.

The sensor portion 320 allows for inline acquisition of metrology data from semiconductor wafers processed by the processing portion 310 of the processing tool 120a. In one embodiment, the sensor portion 320 allows for wafer-by-wafer acquisition of metrology data from processed semiconductor wafers in an inline fashion. In other words, when a semiconductor wafer is processed by the processing portion 310, metrology data relating to that processed semiconductor wafer can be immediately acquired by the sensor portion 320. This provides an advantage of preventing a delay in acquiring metrology data. For example, without the utilization of the sensor portion 320 of the processing tool 120, an entire lot of wafers are generally processed before significant metrology data is acquired. This can prevent immediate feedback on a wafer-by-wafer basis to correct manufacturing errors. Furthermore, waiting for an individual lot of wafers to be completed before acquiring metrology data, can cause further delay in implementing the next manufacturing process to be performed on the lot of semiconductor wafers. It is desirable to receive immediate feedback corrections before implementing a second process on the lot of semiconductor wafers so that further corrections can be made in a different process to compensate for errors that occurred on the previous semiconductor manufacturing process. Turning now to Figure 4, a flowchart depiction of one embodiment of a method in accordance with the present invention is illustrated. In one embodiment, initially, the machine interface 1 15a acquires manufacturing settings, such as control parameter settings, for processing semiconductor wafers (block 410). When the machine interface 1 15a receives process settings, it uses the machine control unit 250 to prompt the processing tool 120a to implement the appropriate processing procedures as prescribed by the system 100. After performing a process step, the processing tool 120 performs a first embodiment of an in-situ sensor analysis (block 430). In one embodiment, the in-situ sensor analysis is initiated by the activation of the sensor portion 320 of the processing tool 120a. A flowchart depiction of one embodiment of performing the first embodiment of the in-situ sensor analysis described in block 430 of Figure 4, is illustrated in Figure 5.

Turning now to Figure 5, a flowchart depiction of one embodiment of the steps for performing an in- situ sensor analysis is illustrated. The equipment interface 1 15a acquires in-situ sensor data, or in-situ metrology data, after the processing of a semiconductor wafer (block 510). In one embodiment, the sensor portion 320 of the processing tool 120a performs metrology data collection or metrology measurements, on semiconductor wafers processed by the processing portion 310 of processing tool 120a. Therefore, the system 100 is able to perform an inline metrology data analysis for each semiconductor wafer processed by the processing tool 120. For example, the sensor portion 320 is capable of measuring overlay errors and misregistration errors on semiconductor wafers that have been processed through a photolithography process in the processing portion 310 of the processing tool 120a.

Once the processing tool 120a acquires inline sensor data, the system 100 performs an analysis of the in-situ metrology data acquired by the sensor portion 320 (block 520). The analysis of the in-situ metrology data, in one embodiment, is performed by the sensor data analysis unit 330, which is also located within the processing tool 120a. The analysis of the in-situ metrology data includes calculating manufacturing errors. The results from the analysis of the in-situ metrology data are sent to the control system interface 210, which forwards the data to the computer system 130. Upon completion of the analysis of the in-situ metrology data, the system 100 performs a compensation step for the processing of the next wafer in the lot by adjusting manufacturing parameters (block 530).

In one embodiment, calculations and modifications of the manufacturing parameters are performed by the computer system 130. The adjusted and modified manufacturing parameters are stored in the shared database 230 for retrieval by the machine interface 115a. The completion of the steps described in Figure 5, substantially completes the steps of performing the in-situ sensor analysis described in block 430 of Figure 4. Turning back to Figure 4, once the in-situ sensor analysis in substantially complete, the system 100 performs a manufacturing process of subsequent semiconductor wafers in the lot (block 440). The steps described in Figure 4 provide for an embodiment that is capable of acquiring settings for a manufacturing process, executing the manufacturing process based on the setting acquired, performing an in-situ sensor analysis (i.e., an inline metrology data acquisition, analysis, and compensation), and processing a subsequent wafer. The above steps can be performed on a wafer-by-wafer basis, providing for more accurate and immediate corrections of manufacturing errors within a manufacturing lot of semiconductor wafers.

Turning now to Figure 6, a flowchart depiction of the steps for performing a method in accordance with a second embodiment of the present invention, is illustrated. Similar to the steps described in Figure 4, manufacturing process settings for performing a semiconductor manufacturing process are acquired by the machine interface 115 (block 610). In one embodiment, the manufacturing settings of the process are acquired from the shared database 230. Subsequently the processing tool 120a performs a semiconductor manufacturing process based upon the process settings acquired by the machine interface 115a (block 620). The system 100 then performs a second embodiment of an in-situ sensor analysis upon the processed semiconductor wafers (block 630). A more detailed flowchart description of the steps of performing the second embodiment of the in- situ sensor analysis described in block 630, is illustrated in Figure 7.

Turning now to Figure 7, the processing tool 120a acquires an in-situ metrology data acquisition based on the wafer processed by the processing tool 120. In one embodiment the acquisition of the in-situ, or inline, metrology data is performed by the sensor portion 320 of the processing tool 120a. The processing tool 120a, in one embodiment, then performs an analysis of the in-situ metrology data acquired by the sensor portion 320 (block 720). The analysis of the metrology data comprises calculating the overlay errors resulting from the previous photolithography process performed on the semiconductor wafer. In one embodiment, the sensor data analysis unit 330 within the processing tool 120a performs the analysis of the in-situ metrology data.

The system 100 then utilizes the aggregation of the inline metrology data to compute new manufacturing parameters for the processing of the entire manufacturing lot of semiconductor wafers for a subsequent manufacturing process (block 730). In other words, the inline metrology data acquired by the sensor portion 320 is used to perform real time manufacturing parameter modifications. The real time manufacturing parameter modifications are made such that subsequent semiconductor manufacturing process performed on the lot of semiconductor wafers are controlled by modified, and more accurate, control parameters. For example, during the first manufacturing process run, a predetermined film thickness is applied onto the semiconductor wafers within a particular lot. Inline measurements and offline metrology data (i.e., data acquired by the metrology tool 150) are acquired and control parameter modifications are performed in real time. The control parameter modifications are made such that when the lot of semiconductor wafers are sent for a subsequent process, such as an etch process or a polishing process, the inline measurements relating to each semiconductor wafer can be used to perform wafer-by-wafer modification during the processing of the entire lot. This provides an advantage over the previous processing techniques where the etch or the polish operations performed on the lot of semiconductor wafers was performed under an assumption that the thickness of the film on each semiconductor wafer was approximately uniform. With the use of inline metrology data, each wafer can be processed more accurately using the embodiment described in the present invention. Completion of the steps described in Figure 7 substantially completes the step of performing the second embodiment of the in-situ sensor analysis described in block 630 of Figure 6. Turning back to Figure 6, once the second embodiment of the in-situ sensor analysis is performed, the semiconductor wafers in the lot are processed using modified manufacturing parameters designed to compensate for the errors detected on previously processed semiconductor wafers (block 640).

Turning now to Figure 8, a flowchart illustration of one embodiment of performing an integration, or a merging function, of the inline metrology data acquisition process and the offline metrology data acquisition process, is illustrated. In one embodiment the machine interface 1 15a receives process settings for a manufacturing process of a lot of semiconductor wafers (block 810). The processing tool 120a associated with the machine interface 1 15a, performs a processing function upon the wafers based upon the process settings received by the machine interface 115a (block 820). Subsequently, the system 100 performs a correlating and analysis function upon the in-situ metrology data and the offline metrology data (block 830). Figure 9 illustrates a flowchart depiction of one embodiment of the method for performing the correlating and analysis function described in block 830 of Figure 8.

Turning now to Figure 9, the processing tool 120a acquires in-situ manufacturing/metrology data subsequent to performing a manufacturing process upon at least one semiconductor wafer (block 910). In one embodiment the manufacturing process is performed by the processing portion 310 of the processing tool 120a. The inline/in-siru metrology data acquisition process is performed by the sensor portion 320 of the processing tool 120a. Once the manufacturing lot of the semiconductor wafers is processed by the processing tool 120a, the system 100 acquires offline metrology data relating to the processed semiconductor wafers (block 920). In one embodiment the offline metrology data is acquired by using a metrology tool 150 to examine the processed lot of sem iconductor wafers. The system 100 then correlates the offline metrology data with corresponding in-situ sensor data (block 930). In other words, the system 100 correlates wafer data for the entire lot of semiconductor wafer (produced by the metrology tool 150) with the metrology data relating to each wafer (i.e., the inline sensor data) acquired by the sensor portion 320 of the processing tool 120a. In one embodiment the inline sensor data and metrology tool data correlator 160 is used to correlate the offline metrology data with the inline metrology data. The correlated manufacturing data analyzer 170 analyzes, organizes, and characterizes the manufacturing error for each semiconductor wafer in the manufacturing lot by examining the correlated offline metrology data and the inline metrology data (block 940). Results from the correlator manufacturing data analyzer 170 are sent to the computer system 130 for usage in the process of modifying control parameters to correct or reduce the errors, or expected errors, for subsequent manufacturing processes (block 950).

The completion of the steps described in Figure 9, substantially completes the task of performing the correlating and analysis function of the inline/in-situ sensor data and the offline metrology data, described in block 830 of Figure 8. Turning back to Figure 8, once the process of correlating an analysis of the in-situ metrology data and the offline metrology data is substantially complete, the resultant modified parameters can be used in subsequent manufacturing processes, which may result in processed semiconductor wafers that have reduced errors. Therefore, a subsequent manufacturing process is performed using the modified manufacturing parameters on the lot of semiconductor wafers (block 840). The steps described in the various embodiments of the present invention can be utilized to perform semiconductor manufacturing processes of various natures. Furthermore, the steps described by the embodiments of the present invention can be used for other types of manufacturing environments.

The principles taught by the present invention can be implemented in an Advanced Process Control (APC) Framework. The APC is a one platform from which to implement the process control strategy taught by the present invention. In some embodiments, the APC can be a factory-wide software system, therefore, the control strategies taught by the present invention can be applied to virtually any of the semiconductor manufacturing tools on the factory floor. The APC framework also allows for remote access and monitoring of the process performance. Furthermore, by utilizing the APC framework, data storage can be more convenient, more flexible, and less expensive than local drives. The APC platform allows for more sophisticated types of control because it provides a significant amount of flexibility in writing the necessary software code.

Deployment of the control strategy taught by the present invention onto the APC framework could require a number of software components. In addition to components within the APC framework, a computer script is written for each of the semiconductor manufacturing tools involved in the control system. When a semiconductor manufacturing tool in the control system is started in the semiconductor manufacturing fab, it generally calls upon a script to initiate the action that is required by the process controller, such as the overlay controller. The control methods are generally defined and performed in these scripts. The development of these scripts can comprise a significant portion of the development of a control system. The principles taught by the present invention can be implemented into other types of manufacturing frameworks.

The particular embodiments disclosed above are illustrative only, as the invention may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. It is therefore evident that the particular embodiments disclosed above may be altered or modified and all such variations are considered within the scope and spirit of the invention. Accordingly, the protection sought herein is as set forth in the claims below.

Claims

1. A method, comprising: processing at least one semiconductor device (105); performing an in-situ sensor analysis upon said processed semiconductor device (105); and performing a subsequent process on at least one semiconductor device (105) in response to said in-situ sensor analysis.

2. The method described in claim 1 , wherein performing said in-situ sensor analysis comprises: acquiring in-situ sensor data; analyzing said in-situ sensor data; and compensating for a subsequent processing of the semiconductor device (105) based upon said analysis of said in-situation sensor data.

3. The method described in claim 2, wherein acquiring said in-situ sensor data comprises acquiring said in-situ sensor data using an inline metrology sensor (125).

4. The method described in claim 2, wherein analyzing said in-situation sensor data comprises calculating a process error.

5. The method described in claim 1 , further comprising: acquiring a set of in-situ manufacturing data; acquiring a set of offline metrology data; correlating said in-situ manufacturing data with said offline metrology data; calculating process errors based upon said correlating said in-situ manufacturing data with said offline metrology data; and compensating for a subsequent processing of the semiconductor device (105) based upon said calculating process errors.

6. The method described in claim 5, wherein acquiring the set of in-situ manufacturing data comprises acquiring metrology data using an inline sensor (125).

7. The method described in claim 5, wherein compensating for the subsequent processing of the semiconductor device (105) further comprises modifying at least one manufacturing parameter.

8. A system, for incorporating in-situ sensors into a semiconductor manufacturing process,

CHARACTERIZED in that the system comprising: a computer system (130); a manufacturing model (140) coupled with said computer system (130), said manufacturing model (140) being capable of generating and modifying at least one control input parameter signal; a machine interface (1 15) coupled with said manufacturing model (140) and said computer system

(130), said machine interface (1 15) being capable of receiving process data from said manufacturing model (140) and said computer system (130); a processing tool (120) coupled with said machine interface (1 15), said processing tool (120) being capable of receiving at least one control input parameter signal from said machine interface

(115) and performing a manufacturing process and performing an inline sensor data acquisition; a metrology tool (150) coupled with said processing tool (120), said metrology tool (150) being capable of acquiring offline metrology data; and a metrology data processing unit coupled with said metrology tool (150) and said processing tool

(120), said metrology data processing unit being capable of organizing and analyzing said acquired inline sensor data and said offline metrology data and calculating at least one manufacturing error for generating modification data.;

9. The system of claim 8, wherein said processing tool (120) further comprising an inline sensor capable of acquiring inline metrology data.

10. A computer readable program storage device encoded with instructions that, when executed by a computer, performs a method, comprising: processing at least one semiconductor device (105); performing an in-situ sensor analysis upon said processed semiconductor device (105); and performing a subsequent process on at least one semiconductor device (105) in response to said in-situ sensor analysis.