Abstract:
Device design information ( 18 ) for a semiconductor device is used to generate theoretical probability of failure information ( 21 ), which represents the probability that a manufacturing defect will cause an electrical failure in an actual device fabricated according to the design information. An actual wafer ( 23 ), which contains a plurality of devices ( 22 ) manufactured according to the design information, is inspected for actual defects ( 25 ). The probability of failure information is then used to determine for each of several detected defects a corresponding probability value. Then, the individual probability values for the respective defects are combined in order to obtain a composite failure probability, which serves as a basis for evaluating the expected yield of operational devices from the particular wafer.

Description:
This application claims priority under 35 USC §119(e)(1) of provisional application No. 60/129,801 filed Apr. 4, 1999. 
    
    
     TECHNICAL FIELD OF THE INVENTION 
     This invention relates in general to semiconductor wafer fabrication and, more particularly, to a method for predicting how many operable devices will be obtained from a given semiconductor wafer in light of defects therein. 
     BACKGROUND OF THE INVENTION 
     As electronic systems have continued to grow in importance in modern society, the need for effective fabrication of the semiconductor devices underlying the electronic systems has also grown. The increased need for semiconductor fabrication abilities has also increased the requirements for monitoring the fabrication process. In this regard, as the level of integration increases, the size of the semiconductor devices decreases. As a result, a defect of a given size has an increasingly greater potential for causing a potential failure of a device, such as a short or an interruption of electrical continuity. Consequently, it is important to have information about how many semiconductor devices on a given semiconductor wafer will have to be scrapped because they are inoperable, due to defects introduced in the manufacturing process. 
     One traditional method of measuring device yields from fabricated semiconductor wafers has involved generating a histogram based on production inspection of the wafers. The histograms show the number of defects on the wafer for each of several defect size ranges. Probability of failure information is estimated based on the design of the devices on the wafer, and a graph curve of this probability of failure information is manually overlaid on the histogram. The histogram with the overlaid probability of failure information are used to estimate a device yield rate for a wafer embodying a particular device design. One of the problems with this known technique is that the number of predicted failures for a given design may be overstated, due to defects being counted multiple times, for example where each layer is inspected after its fabrication and a single defect in one layer is picked up by inspections for that layer and other layers. In addition, this known technique evaluates failure only at the wafer level, and makes no device specific inquiries. 
     SUMMARY OF THE INVENTION 
     From the foregoing, it may be appreciated that a need has arisen for a method and apparatus for accurately predicting how many operable devices will be obtained from a given semiconductor wafer in light of defects therein. 
     According to the present invention, a method and apparatus are provided to address this need and involve using design information to generate further information which defines a probability of failure as a function of a defect characteristic for potential defects, and inspecting a part fabricated according to the design information to identify defects therein and at least one characteristic of each defect. The method and apparatus further involve generating a list of defect characteristics which each correspond to a respective defect detected in the part in the inspecting step, using each defect characteristic in the list to determine from the further information a respective corresponding defect failure probability, and combining the defect failure probabilities to determine a survival probability. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     A better understanding of the present invention will be realized from the detailed description which follows, taken in conjunction with the accompanying drawings, in which: 
     FIG. 1 is a diagrammatic top view of a semiconductor wafer which is one example of various wafers that may be evaluated according to the present invention; 
     FIG. 2 is a diagrammatic cross sectional view of the wafer of FIG. 1, taken along the line  2 — 2  in FIG.  1  and showing a plurality of layers and a plurality of devices in the wafer; 
     FIG. 3 is a block diagram of a yield prediction system which embodies the present invention; 
     FIG. 4 is a graph showing probability of failure information for several layers of the wafer of FIG. 1; and 
     FIG. 5 is a flowchart showing the operation of a utility which is part of the system of FIG.  3  and which predicts survival or yield information for wafers such as the wafer shown in FIG.  1 . 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1 is a diagrammatic top view of a wafer  23  which is one example of various semiconductor wafers that may be evaluated according to the present invention. The wafer  23  includes a plurality of semiconductor devices  22  which are each fabricated according to device design information. One example of device design information is shown diagrammatically at  18  in FIG.  3  and is discussed later, but the present invention is not limited to any particular wafer or device design. In the disclosed wafer  23 , each of the devices  22  is fabricated using the same device design information. 
     FIG. 2 is a diagrammatic cross-sectional view of the wafer  23 , showing a plurality of layers  12  therein, and showing several of the semiconductor devices  22  therein. Each semiconductor device.  22  is created in a known manner on a substrate  14  by fabricating the layers  12  from various known conducting, insulating and semiconducting materials  14  to form the various electronic components that make up each semiconductor device  22 . In the disclosed embodiment these layers  12  include MOAT, POLY, CONT, MET1, VIA1, MET2, VIA2, and MET3 layers, which are types of layers known to those skilled in the art. 
     A plurality of defects  25  may be introduced into the layers  12  of the semiconductor devices  22  during the fabrication process. Each device  22  may experience electrical failure which renders the device  22  inoperative, due to the defect or defects  25  therein. Each defect  25  has associated characteristics, such as a size of the defect  25 , the layer  12  where the defect  25  occurs, and a location on the layer  12  of the defect  25 . 
     FIG. 3 is a block diagram of a yield prediction system  10 . The yield prediction system  10  includes a critical area analysis section  16 , which uses the device design information (depicted at  18 ) in order to generate probability of failure information  21  for the semiconductor devices described by the device design information  18 . In the disclosed embodiment, the critical area analysis section  16 , the failure information  21 , a database  28  and a utility  33  are implemented on a computer  38 . 
     The techniques used by the critical area analysis section  16  are known, but are described here briefly for purposes of convenience and completeness. More specifically, the critical area analysis section  16  uses the device design information  18  to generate a simulated semiconductor device conforming to that design information. The critical area analysis section  16  then analyzes a plurality of simulated defects which could potentially be introduced into the layers of the simulated semiconductor device during an actual fabrication process. The critical area analysis section  16  of the disclosed embodiment uses Monte Carlo analysis to estimate the electrical failure probability, but other methods known in the art, such as shapes expansion, may alternatively be used. Monte Carlo analysis randomly distributes a plurality of simulated defects of various sizes on each of the layers of the simulated device. The critical area analysis section  16  then analyzes the simulated defects, in order to determine the probability that each simulated defect, based on its size and location, would cause a failure in the specific circuit represented by the particular design information. Section  16  generates separate probability of failure information  21  for each layer in the device. This probability information for each of the defects in a given layer is then combined in order to obtain the probability of failure information  21  for that layer. 
     In more detail, by using the device design information  18 , the critical area analysis section  16  can determine whether each simulated defect would interact with one or more electrical components or interconnections formed in the layers of the simulated device and whether the interaction will cause an electrical failure, such as a short or interruption of electrical continuity. This critical area analysis is performed individually for each layer in the simulated device. The failure information  21  generated by the critical area analysis section  16  for each layer relates the size of the simulated defects to the probability of a failure in that layer. 
     FIG. 4 is a graph showing an example of probability of failure information  21  for each of the layers  12  of the wafer  23  shown in FIGS. 1 and 2. In particular, the failure information  21  includes a plurality of graph curves  51 - 58 , which each correspond to a respective layer  12  in FIG. 2, and which relate the probability of electrical failure in that layer to the sizes of defects  25 . In the disclosed embodiment, the failure information  21  is stored as a lookup table, but it is shown graphically in FIG. 4 for purposes of clarity. As discussed in more detail below, the failure information  21  may be used in conjunction with inspection information from an actual device  22  having actual defects  25  in order to determine the failure probability of the actual device  22 . 
     Referring again to FIG. 3, an inspection station  26  is used to inspect each layer  12  of the wafer  23 . The inspection station  12  is a commercially available device with which those skilled in the art are already familiar, but it :is briefly described here for purposes of completeness. A subset of wafers from a production batch is selected for inspection, the wafer  23  of FIGS. 1-2 being one example of such a wafer. The inspection station  26  performs a separate inspection for each layer  12  of the wafer  23 , after each respective layer has been fabricated. The inspection station  26  is used to detect the defects  25  introduced into the layers  12  of the devices  22  on the wafer  23  during the fabrication process. The inspection station  26  also detects the size of each defect  25 , and location on the layer  12  of each defect  25 . The defects and the associated characteristics detected by the inspection station  26  are all stored in the database  28 . The database  28  stores the result of multiple past inspections of various different wafers  23  inspected by the inspection station  26 . 
     A classification station  31  associates a classification with each defect  25 , and the classification associated with the defect  25  is stored in the database  28  with the other information for that defect  25 . In the disclosed embodiment of the present invention, the classification is determined by having a human operator use a microscope to visually inspect the actual defects  25  in each layer  12  of the wafer  23 , using the defect location information from the database to visually locate each defect. Based on this visual inspection, the human operator manually assigns a classification to each defect  25 . For example, a defect may be classified as an excursion defect, which may be a repeating defect caused by the failure of a specific piece of equipment. Alternatively, it may be classified as a nuisance defect, such as a color variation. Any of a variety of other suitable and well-known classifications may be used as well. 
     The utility  33  is a program which uses the failure information  21  and the database  28  in order to predict a survival probability for each device  22 . The operation of the utility  33  is described in more detail later in association with FIG.  5 . Setup information  36  is used to control and refine the operation of the utility  33 . The setup information  36  is obtained from an operator, for example through command line parameters, or through a graphical user interface (GUI). The setup information  36  allows operator control of which defects  25  and which defect characteristics the utility  33  should use in predicting the survival probability. For example, the setup information  36  may restrict the utility  33  to using only new defects which were not present in the prior layer or layers, random defects, repeating defects, defects within a particular area, or some combination thereof. The setup information  36  may also be used to instruct the utility  33  to ignore defects  25  of various classifications, such as nuisance defects. The setup information  36  may also be used to select which of the many wafer inspections stored in the database  28  to make a prediction for. For example, the setup information  36  may restrict the utility  33  to use of wafers  23  from a particular date range, those that match a particular pattern, those which are in certain production lots, or some other combination thereof. 
     FIG. 5 is a flowchart showing the operation of the utility  33 . The operation begins at block  101 , where the utility  33  generates from the information in the database  28  a list of defects  25  and associated defect characteristics. The selection of the defects  25  and defect characteristics for the list may be controlled based on various filtering criteria, such as the criteria specified in the setup information  36 . The filtering criteria acts to limit and control which defects  25  and associated defect characteristics are used by the utility  33 . The filtering criteria may also filter based on the size of the defect, the layer where the defect is located, the location of the defect within the layer, whether or not the defect repeats from device  22  to device  22 , and various other characteristics. Only defects which satisfy the filtering criteria are included in the list. 
     Next, at block  103 , the utility  33  selects the first defect  25  and defect characteristic from the list. 
     Proceeding to block  106 , the probability of failure for the selected defect  25  is calculated. The probability of failure for the selected defect  25  is calculated by using the size of the defect to look up the failure information  21  for the respective layer  12  having the defect  25 . For example, referring to FIG. 4, a defect of size 10 in the VIA1 layer would have a probability of failure slightly higher than 0.4. 
     Then, at block  108 , the complement of the defect failure probability calculated in block  106  is determined. The complement is calculated by taking one minus the failure probability determined in block  106 . The complement calculated in step  108  is then stored for later use, as discussed below. 
     Next, control proceeds to decisional block  111 . If there are any more defects which remain in the list and need to be handled, then the YES branch of decisional block  111  is followed, the next successive defect in the list is selected at block  112 , and control returns to block  106 . Alternatively, if there are no more defects in the list which still need to be handled, then the NO branch of decisional block  111  is followed and control proceeds to block  113 . 
     At block  113  the survival probability for each device is computed. The survival probability is found by taking the product of all of the complements from block  108  for a given device  22 . Depending on the setup information  36 , this may represent defects for that device for only one layer, or defects in all layers. The survival probability represents the probability that the device  22  will properly operate with the defects in that device which are in the list generated by block  101 . 
     Proceeding to block  116 , a wafer survival probability is computed. The wafer survival probability is found by computing the average of the survival probabilities for all of the devices  22  on the wafer  23 . Depending on the setup information  36 , the survival probability for each device may represent all of the defects for that device in all layers, or only the defects for that device in one layer, and so forth. 
     Next, at block  118 , a report is prepared. The report contains each device survival probability computed in block  113 , and the wafer survival probability computed in block  116 . At block  121  the report prepared at block  118  is displayed to the user, for example by printing it out. 
     The present invention provides a number of technical advantages. One such technical advantage is the capability for an on-the-fly evaluation of probable device yield from a wafer, which results in cost savings by permitting low-yield wafers to be scrapped early in the fabrication process. Another advantage is more accurate determination of the device yield rate for wafers. A further advantage is that analysis is carried out on a defect-by-defect basis and also on a device-by-device basis. A further advantage is the ability to identify the particular layer having the lowest yield, so that the cause of the defects can be more accurately determined, and changes made to the fabrication process for that layer. Conversely, the invention can be used to measure the effectiveness of a change in the process used to fabricate a given layer. In a situation where the calculated yield is better than the actual yield, it may indicate that further study should be focused on the fabrication process itself, rather than on defects. Using these techniques, production yields for new products can be improved more quickly than with prior techniques. A further advantage is that the techniques according to the invention are compatible with existing defect databases. 
     Although one embodiment has been illustrated and described in detail, it should be understood that various changes, substitutions and alterations may be made therein without departing from the scope of the present invention. For example, although the disclosed embodiment describes analysis of particular types of layers, any suitable composition or type of layer used in fabricating semiconductor devices may be used. In addition, although the specific examples given for defect classifications include excursion defects and nuisance defects, many other additional defect classifications may be used. Further, the specific techniques set forth for combining the failure probabilities determined for respective single defects are not limiting, and there are numerous other ways in which the failure probabilities for individual defects may be combined. Moreover, the failure probabilities for individual defects are determined in the disclosed embodiment as a function of the size of the defects, but it will be recognized that it would be determined as a function of another characteristic, or a combination of characteristics. Other changes, substitutions and alterations are also possible without departing from the spirit and scope of the present invention, as defined by the following claims.