Abstract:
A method and apparatus for providing communication between a defect source identifier and a tool data collection and control system. The defect source identifier collects wafer data until a defect is identified. Upon identification of a defect, a request is sent to the tool data collection and control system to request data of the tool parameters at the time the defect occurred. The tool data collection and control system retrieves the tool parameters and communicates them to the defect source identifier through a network. The tool parameters are processed by the defect source identifier to extract certain wafer data. The selected wafer data is communicated to the tool data collection and control system and is used to execute a prediction model to predict failure possible of the tool elements.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS  
       [0001]    This application claims benefit of U.S. provisional patent application Ser. No. 60/240,631, filed Oct. 16, 2000, which is herein incorporated by reference. 
     
    
     
       BACKGROUND OF THE INVENTION  
         [0002]    1. Field of the Invention  
           [0003]    The present invention generally relates to semiconductor wafer processing systems and, more particularly, the invention relates to a method and apparatus for providing communications between a defect source identifier and a tool data collection and control system.  
           [0004]    2. Description of the Background Art  
           [0005]    Semiconductor wafer processing systems comprise a plurality of process chambers arranged to process semiconductor wafers in a serial manner to produce integrated circuits. As the wafers are processed, they are intermittently positioned in a metrology station or stations to measure the effectiveness of the process steps being performed. When defects are detected by the metrology station, the system operator is notified. The system operator then generally reviews empirical data to determine the source of the defect. Once the source is identified, the operator adjusts the operating parameters of the various chambers within the tool to mitigate future defects.  
           [0006]    The defect source identification process may be automated using a defect source identifier as disclosed in U.S. patent application Ser. No. 09/905,607, filed Jul. 13, 2001, which is herein incorporated by reference. The defect source identifier collects data from the metrology station with regard to defects that are found on a wafer and analyzes the defects to automatically determine a source of those defects. Once the source is identified solutions to the source of defects can be suggested to an operator.  
           [0007]    There is a need in the art for an integrated solution wherein the defect source identifier can communicate with a tool data collection and control system to create an automated process to predict tool failure and correct possible failures prior to actual failure.  
         SUMMARY OF THE INVENTION  
         [0008]    The present invention generally provides a method and apparatus for providing communication between a defect source identifier (DSI) and a tool data collection and control system. The defect source identifier collects defect data until a defect is identified. Upon identification of a defect, a request is sent to a semiconductor wafer processing tool to request the tool parameters that were being used at the time the defect occurred. The tool data collection and control system retrieves the tool parameters and communicates them to the DSI through a network or other form of communication link. The defect source identifier (DSI) can then identify defect sources per chamber if the chamber information is provided by the tool data collection and analysis tool. The tool parameters are processed by the defect source identifier such that select wafer data is extracted that is relevant to the tool parameters at the occurrence of the defect. The select defect data is communicated to the tool data collection and control system. The data is used to execute a prediction model to predict failure occurrence of the tool components. If the model does not predict a failure of the tool or any component of the tool is imminent, the tool data collection and control system returns to its steady state. If a failure is predicted, the tool data collection and control system takes corrective action and then updates the defect source identifier with the action that is taken. To enhance accuracy of the invention, a data mining engine may be used to correlate defect data, tool parameter information (process data), and parametric data (e.g., electric test results). 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0009]    So that the manner in which the above recited features of the present invention are attained and can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings.  
         [0010]    It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.  
         [0011]    [0011]FIG. 1 is a block diagram of a semiconductor wafer processing system, according to an embodiment of the invention;  
         [0012]    [0012]FIG. 2 is a flow diagram of a process according to an embodiment of the invention;  
         [0013]    [0013]FIG. 3 is a flow diagram of a process used to correlate defect and process data, according to an embodiment of the invention; and  
         [0014]    [0014]FIG. 4 is a graphical representation of defect and process data, according to an embodiment of the invention. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0015]    [0015]FIG. 1 depicts a block diagram of a semiconductor wafer processing system  100  comprising a semiconductor wafer processing tool  102  (the “tool”), a tool data collection and control system  110 , a defect source identifier  124  and a communications network  138 . Also coupled to the communications network  120  may be a data mining engine  150 , a defect knowledge library (DKL)  152  and a process knowledge library (PKL)  154 . The DKL and PKL can be combined into a single knowledge library.  
         [0016]    The tool  102  comprises a plurality of process chambers  104 A,  104 B, and  104 C attached to a platform  105 . The platform  105  contains a centrally located robot  106  that accesses the process chambers  104 A,  104 B and  104 C. The platform  105  also comprises a pair of load-locks  108  or other form of factory interface and at least one integrated metrology station  136 . In some tools, the metrology station  136  may not be integrated, i.e., the station may be a stand-alone station  162 . The tool  102  is controlled by the tool data collection and control system  110 . The tool data collection and control system  110  provides control signals to control all of the process parameters and movement of wafers within the tool  102 .  
         [0017]    An example of a tool data collection and control system  110  that operates in accordance with the present invention is manufactured by Applied Materials, Inc. of Santa Clara, Calif. under the trademark SMARTSYS. This tool data collection and control system is described in U.S. patent application Ser. No. 09/561,440, filed Apr. 28, 2000, entitled “Wafer Fabrication Data Acquisition and Management Systems”, which is incorporated herein by reference in its entirety.  
         [0018]    The tool data collection and control system  110  comprises a central processing unit (CPU)  112 , memory  118 , input/output (I/O) circuits  114 , and support circuits  116 . The tool data collection and control system  110  is a general purpose computer that is programmed by software  119  stored in memory  118 . When the software  119  is executed by the CPU  112 , the computer operates as a specific purpose computing system that performs the processes of the tool data collection and control system  110 . This CPU  112  is supported by well-known support circuits  116  such as clocks, power supplies, cache and the like. The I/O circuits  114  comprise such well-known components such as a mouse, keyboard and monitor as well as communications circuits such as Ethernet cards and other communications circuits. The memory  118  may comprise removable memory, random access memory, read only memory, hard disk drives or any combination thereof.  
         [0019]    The defect source identifier (DSI)  124  comprises a central processing unit (CPU)  126 , a memory  132 , I/O circuits  130  and support circuits  138 . The defect source identifier  124  is similarly constructed as a general purpose computer as the tool data collection and control system  110  and operates in accordance with DSI software  133  stored in memory  132  and executed by the CPU  126 . The DSI software  133  comprises executable instructions as well as information that is stored in various databases. An example of a DSI is described in U.S. patent application Ser. No. 09/905,607, filed Jul. 13, 2001, which is incorporated herein by reference.  
         [0020]    The DSI  124  receives defect information from a number of sources including one or more of an integrated metrology station  136 , a stand-alone metrology station  162 , a wafer inspection station  156 , a wafer review station  158  and an electrical test station  168 . The defect information is processed by the DSI software  133  and stored in the DSI memory  132  or stored in the defect knowledge library  152 .  
         [0021]    The tool data collection and control system I 10  is coupled to the DSI  124  via a communications network  120 . This network  120  may comprise a dedicated communication link between the DSI  124  and system  110  or may be a more conventional computer network such as Ethernet. Additionally, the metrology station or stations  136  are coupled to the DSI  124  by the network  120 . Other tools and tool data collection and control systems may also be coupled via the network  120  to the DSI  124 .  
         [0022]    The tool data collection and control system  110  executes software  119  that provides control signals to the tool  102  to move the robot  106  such that wafers are sequentially moved from process chamber  104 A,  104 B,  104 C to process chamber  104 A,  104 B,  104 C to create integrated circuits or portions of integrated circuits. Intermittently during the process, a wafer (or wafers) is moved to the integrated metrology station  136  for analysis. Other test and measurement stations  156 , 158 ,  160 , 162  may also be used to create and supply defect information to the DSI  124 . The station (or stations) tests the wafers for defects and reports those defects to the DSI  124 .  
         [0023]    The DSI  124  communicates wafer data to the tool data collection and control system  110  such that the tool data collection and control system  110  can use the DSI supplied information to predict a possible failure within the tool  102 . Upon detection of a possible failure, the tool data collection and control system  110  may mitigate the failure by performing certain maintenance functions or produce operator warnings to avoid the failure. Any corrective action taken by the data collection and control system  110  is reported to the DSI  124  such that the DSI  124  can update its database of tool parameters. For example, if a chamber within the tool  102  is taken offline to avoid failure, the defect source identifier  124  must not identify that chamber as a source of defects in the future.  
         [0024]    As described with respect to FIG. 3 below, the corrective process may be augmented using a data mining engine  150 . In one illustrative embodiment, the data mining engine  150  is a general purpose computer comprising a CPU  164 , support circuits  166 , I/O circuits  168  and memory  170 . The memory  170  stores data mining software  172  that, when executed by the CPU  164 , causes the general purpose computer to operate as a special purpose computer (i.e., function as a data mining engine  150 ). The data mining engine  150  communicates with the DSI  124 , system  110 , DKL  152  and PKL  154  via the network  120 . The engine  150  correlates tool information (process data) gathered by the tool data collection and control system  110  with DSI information (wafer data) gathered by the DSI  124 . Upon finding operational parameter variations that correlate with wafer data, the engine  150  accesses the DKL  152  and PKL  154  to identify solutions to the identified defects, parameters and correlations. The correlation information can be used to augment the component failure prediction model. The process data, wafer data and correlation information may then be stored in the DKL and PKL to further enhance the historical records contained in these libraries.  
         [0025]    [0025]FIG. 2 depicts a flow diagram of a method  200  of a communication process between a DSI process  202  and a tool data collection and control system process  204 . The steady state process for the DSI is to collect DSI data on a regular basis at step  206 . The steady state process for the tool data collection and control process  204  is to collect tool data as the tool is operated in accordance with the control system at step  220 .  
         [0026]    When, at step  208 , a defect is detected within the wafer data, the DSI process  202  proceeds to step  210  where the DSI process requests tool information (also referred to herein as tool data or process data) containing the operating parameters of the tool at the time the defect occurred. This request for tool data is sent to the tool data collection and control system through the network and forms an interrupt of the steady state operation of the tool data collection and control process  204 . At step  222 , the interrupt effectively causes the tool data collection and control system to retrieve and send tool data, at step  224 , to the DSI process  202 . The DSI process  202  receives the tool data at step  212  and processes that data at step  214 . The processing at step  214  extracts select wafer data that is relevant to the tool&#39;s parameters at the time of the defect being detected.  
         [0027]    At step  216 , the select wafer data is sent to the tool data collection and control system. At step  226 , the select wafer data is received and is used as input data to a failure prediction model that forms part of the software  119  of FIG. 1. One embodiment of a prediction model is manufactured by TRIAD Software of Ramsey, N.J., but other prediction models may be utilized. At step  228 , the prediction model predicts whether a failure is imminent in one of the components within the tool. For example, the defect source identifier may identify a defect being caused by a particular chamber from the information given on the occurrence of the defect and the tool parameters at the time of the defect&#39;s occurrence as well as past parameters that have been collected. The tool data collection and control system can predict whether a certain chamber will fail. If at step  230 , no failure is predicted the tool data collection and control system returns to its steady state process at step  220 . However, if a failure is predicted, the process  204  proceeds to step  232  where a correction to the possible failure is performed. Such a correction may involve warning an operator of the imminent failure and having the operator correct some parameter of the process to avoid the failure. Alternatively, a particular defect may require a chamber to enter into a cleaning process or the like. Once the corrective action is taken, the action that was taken is communicated at step  234  to the DSI process  202 . At step  218 , the DSI process  202  updates the DSI databases with the corrective action taken by the data collection and control system. Upon updating the DSI databases, the tool data collection and control system returns to its steady state process at step  220 . Similarly, after the DSI databases are updated with respect to the tool components that are operating and can be identified as sources of defects, the DSI process  202  returns to its steady state of collecting wafer data at step  206 .  
         [0028]    The tool data collection and control system, in lieu of sending a correction to the possible failure, may require additional information to be collected by the DSI. For example, the tool data collection and control system  110  may request the DSI  124  to collect more information from the metrology station  136  regarding a particular defect or change the sampling methodology or rates of sampling depending on the potential problem that has been identified. As such, the DSI  124  may collect information on additional wafers or it may more accurately analyze a defect on a particular wafer as requested by the data collection and control system.  
         [0029]    [0029]FIG. 3 depicts a flow diagram of the method  300  of operation of the data mining engine  150  of FIG. 1. The method  300  begins with step  302  and proceeds to step  304 . The method  300  may be invoked by an operator, automatically on a periodic basis, or automatically by the DSI  124  or tool data collection and control system  110 . At step  304 , the data mining engine receives DSI information and, at step  306 , the data mining engine receives tool operation data (i.e., tool parameters).  
         [0030]    [0030]FIG. 4 is a table  400  that graphically illustrates the type of tool data (process data) and DSI information (wafer data) that is supplied to the data mining engine. The table comprises a wafer number  402 , a tool information section  404  and a DSI information section  406 . Illustrative tool data include pressure  408 , temperature  410 , power  411  and other parameter  412 . Any number of parameters can be monitored and the information stored. Additionally, parameter processing can be performed to produce accumulated values, average values, peak values, filtered values and the like.  
         [0031]    The DSI information section  406  comprises a defect count  414  for each wafer, a reviewer class count  416  (i.e., the number of defects in a particular defect class for a particular wafer), an electrical test  418  (e-test) count of defective dies on a wafer and so on. Any number of defect measures can be used. Additionally, the defect measures may be processed to form accumulations, averages, filtered values, and the like.  
         [0032]    In step  308  of FIG. 3, method  300  correlates selected data parameters with selected defect measures. This processing identifies relationships between the parameters and the defects. As illustrated in FIG. 4, the pressure data  408  spikes at wafer  4 , while all the defect measures also rise for wafer  4 . This results in a correlation peak  420 . Conversely, fluctuations in temperature  410  and power  411  do not correlate with an increase in defects. As such, no correlation peak is produced for wafer  3 .  
         [0033]    At step  310 , the method  300  queries whether a correlation peak has been found. If the query is negatively answered, the method proceeds to step  312  and stops. If the query is affirmatively answered (as in the case of wafer  4  in FIG. 4), the method  300  proceeds to step  314 . At step  314 , the method accesses the defect knowledge library (DKL) and the process knowledge library (PKL). The DKL contains information regarding the defects that led to the correlation and solutions for mitigating the defects. The PKL contains process information that can be used to ensure that the solution suggested by the DKL information can be used in the process being performed by the tool.  
         [0034]    After one or more solutions have been suggested, the method  300  proceeds to step  316  wherein the solutions are sent to an operator and/or to the tool data collection and control system for implementation. At step  318 , the method stops.  
         [0035]    While the foregoing is directed to the preferred embodiment of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.