Computer-implemented methods, computer-readable media, and systems for classifying defects detected in a memory device area on a wafer

Computer-implemented methods, computer-readable media, and systems for classifying defects detected in a memory device area on a wafer are provided.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to computer-implemented methods, computer-readable media, and systems for classifying defects detected in a memory device area on a wafer. Certain embodiments relate to classifying defects detected in a memory device area on a wafer based on positions of the defects within the different types of blocks in the memory device area in which the defects are located.

2. Description of the Related Art

Memory devices such as DRAM and Flash memory include repeating blocks (e.g., memory cell block, sense/amplifier block, wordline driver block, conjunction, and others). More than about 80% of memory devices can be occupied by a memory cell block. The memory cell block includes repeating structures. For example, the memory cell block may include 2 F˜8 F repetitive structures having the same pattern background.

Currently used methods for classifying defects include using the design background or defect attributes to classify the detects. One such method for classifying defects is design based binning (DBB). Examples of DBB are described in commonly owned U.S. patent application Ser. No. 11/561,659 by Zafar et al., published as U.S. Patent Application Publication No. 2007/0288219 on Dec. 13, 2007, which was filed on Nov. 20, 2006, and which is incorporated by reference as if fully set forth herein. DBB, in general, can be described as pattern based binning that may use graphical data stream (GDS) clips. For example, DBB may include extracting design clips corresponding to locations of defects detected on a wafer, comparing the clips against themselves, and binning the defects into groups such that the clips for the defects in each of the groups are substantially the same. Therefore, defects having the same pattern background are classified into the same bin. DBB may also include generating results such as a pareto chart showing the number of defects in each of the pattern based groups. In addition, DBB can involve using design and inspection information to identify and classify potential systematic pattern problems.

However, memory blocks have repeating structures, which means that the design background provides little or no differentiation for defects as design rules continue to shrink. In particular, since defects in memory device areas will in general have the same pattern background, DBB does not provide differentiation among different defects because different defects will have the same pattern background and will thereby be binned into the same group. In this manner, for memory devices, it is not helpful to use the design background for defect classification. Therefore, although DBB methods and systems have proven to be extremely useful in a number of applications, DBB is difficult to use for memory devices. In particular, DBB will have substantially limited use for DRAM and Flash memory devices.

Accordingly, it would be advantageous to develop more effective methods and systems for classifying defects detected in a memory device area on a wafer.

SUMMARY OF THE INVENTION

The following description of various embodiments of computer-implemented methods, computer-readable media, and systems is not to be construed in any way as limiting the subject matter of the appended claims.

One embodiment relates to a computer-implemented method for classifying defects detected on a memory device area on a wafer. The method includes using a computer system to perform the following steps. The steps of the method include determining positions of inspection data acquired for the memory device area by an inspection system. The memory device area includes different types of blocks. The inspection data includes data for defects detected in the memory device area. The steps of the method also include determining positions of the defects with respect to a predetermined location within the blocks in which the defects are located based on the positions of the inspection data, in addition, the steps of the method include classifying the defects based on the positions of the defects within the blocks.

Each of the steps of the computer-implemented method described above may be further performed as described herein. In addition, the computer-implemented method described above may include any other step(s) of any other method(s) described herein. Furthermore, the computer-implemented method described above may be performed by any of the systems described herein.

Another embodiment relates to a computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for classifying defects detected in a memory device area on a wafer. The computer-implemented method includes the steps of the computer-implemented method described above.

The computer-readable medium described above may be further configured according to any of the embodiment(s) described herein. Each of the steps of the computer-implemented method executable by the program instructions may be further performed as described herein. In addition, the computer-implemented method executable by the program instructions may include any other stem's) of any other method(s) described herein.

An additional embodiment relates to a system configured to classify defects detected in a memory device area on a wafer. The system includes an inspection subsystem configured to acquire inspection data for the memory device area formed on the wafer. The memory device area includes different types of blocks. The inspection data includes data for defects detected in the memory device area. The system also includes a computer subsystem configured to determine positions of the inspection data, determine positions of the defects with respect to a predetermined location within the blocks in which the defects are located based on the positions of the inspection data, and classify the defects based on the positions of the defects within the blocks.

The embodiment of the system described above may be further configured according to any of the embodiment(s) described herein. In addition, the embodiment of the system described above may be configured to perform any step(s) of any method embodiment(s) described herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

One embodiment relates to a computer-implemented method for classifying defects detected in a memory device area on a wafer. For example, the method may be used to classify defects detected on memory blocks in DRAM, Flash memory, and SRAM areas in logic devices. As described further herein, the embodiments described herein will provide significant yield improvement, especially for memory manufacturers suffering from defects on memory blocks.

The method includes using a computer system to perform the following steps. The computer system may be configured as described further herein. The steps of the method include determining positions of inspection data acquired for the memory device area by an inspection system. In one embodiment, determining the positions of the inspection data includes determining the positions of the inspection data in design data space. Determining the positions of the inspection data in design data space may be performed as described in commonly owned U.S. patent application Ser. No. 11/561,735 by Kulkarni et al. filed on Nov. 20, 2006, published as U.S. Patent Application Publication No. 2007/0156379 on Jul. 5, 2007, which is incorporated by reference as if fully set forth herein. For example, determining the positions of the inspection data in design data space may include aligning a portion of the inspection data to design data for the memory device area being formed on the wafer thereby determining the positions of the inspection data in design data space. The design data may include any design data or design data proxies described in the above-referenced patent application by Kulkarni et. al.

The memory device area includes different types of blocks. For example, as shown inFIG. 1, in a typical DRAM memory unit, a memory device area may include different types of blocks including a cell block, a sub wordline driver (SWD) block, a sense/amplifier (S/A) block, and a conjunction block. The different types of blocks may be different in that they include different repeating structures (or repeating structures that have at least one different characteristic) and have different electrical functions.

The inspection data includes data for defects detected in the memory device area. The inspection data may include any suitable inspection data that can be acquired for the memory device area by an inspection system. The inspection system may be a bright field (BF) inspection system, a dark field (DF) inspection system, or a BF and inspection system. The inspection system may acquire the inspection data by scanning light over the wafer, detecting light reflected and/or scattered from the wafer, and generating the inspection data in response to the detected light. The inspection system may detect defects in the memory device area in any suitable manner.

The method also includes determining positions of the defects with respect to a predetermined location within the blocks in which the defects are located based on the positions of the inspection data. In other words, the positions of the defects in each different block are determined with respect to the predetermined location within each different block. For example, the positions of the defects in the memory cell block are determined with respect to a predetermined location in the memory cell block, the positions of the defects in the SWD block are determined with respect to a predetermined location in the SWD block, etc. In one embodiment, the predetermined location includes a center or a corner of the blocks. The corner of the block may be a lower left corner of the block. For example, as shown inFIG. 1, three defects (Defect1, Defect2, and Defect3) may be detected in the cell block of the memory device area. Therefore, the positions of the defects may be determined with respect to center10of the cell block or lower left corner12of the cell block. In addition, the positions of the defects are relative positions with respect to the center or the lower left corner of the cell block as illustrated inFIG. 1by the dashed lines between the center of each defect and the center and lower left corner of the cell block.

In one embodiment, determining the positions of the defects includes determining the positions of the defects with respect to the predetermined location in design data space. For example, as described above, the method may include determining the design data space positions of the inspection data. Therefore, determining the positions of the inspection data in design data space may involve determining the positions of the inspection data corresponding to the defects as well as the predetermined locations design data space. In this manner, the positions of the defects with respect to the predetermined location can be determined in design data space. As such, the coordinates of the positions of the defects with respect to the predetermined location can come from the design data that corresponds to the inspection defect location. For example, as described above, the positions of the defects are determined relative to the predetermined location (e.g., inspection die corner) within the blocks in which the defects are located. Therefore, to get the defect coordinates relative to the predetermined location (e.g., the center or tower left corner) of the block, the design data can be used that will have all of the layout information.

In one embodiment, determining the positions of the inspection data includes determining the positions of the inspection data in inspection data space, and determining the positions of the defects includes determining the positions of the defects with respect to the predetermined location in the inspection data space. For example, using design space can be the best way to get accurate defect coordinates and to be combined with design based binning (DBB). However, there are two alternative ways to get defect coordinates that could be used to classify the defects as described further herein. One alternative to get the defect coordinates is to use design data (e.g., graphical data stream (GDS)) to generate inspection care area groups (which may be commonly referred to as “GDStoCA”). GDStoCA can be used to define different types of blocks, and the inspection system can report the defect coordinates with respect to the predetermined location. Another way to get the defect coordinates is to use manually drawn inspection care areas and to group the care areas into different types of blocks. The inspection system can then report the defect coordinates with respect to the predetermined location. Therefore, the coordinates of the positions of the defects with respect to the predetermined location can come from a.) the design data that corresponds to the inspection defect location, design data space; b.) the inspection defect location based on inspection care areas drawn by design data (e.g., GDStoCA), inspection data space; or c.) the inspection defect location based on inspection care areas drawn and grouped manually, inspection data space. Examples of methods and systems that can be used for determining the inspection defect location based on the inspection care areas drawn by GDStoCA are illustrated in commonly owned U.S. Pat. No. 6,529,621 to Glasser et al., U.S. Pat. No. 6,748,103 to Glasser et al., and U.S. Pat. No. 6,886,153 to Bevis, which are incorporated by reference as if fully set forth herein.

In one embodiment, the method includes reporting x, y addresses of the different types of blocks within the memory device area and the positions of the defects within the blocks. In one such embodiment, the positions of the defects within the blocks include x, y locations with respect to the predetermined location within the blocks. For example, the data that can be reported by the methods described herein include memory block x, y address and defect x, y location with respect to the tower left corner or the center of each block.

The method further includes classifying the defects based on the positions of the defects within the blocks. Therefore, the method includes location-based defect classification in memory block. As described above, the predetermined location may include a center or a corner (e.g., the lower left corner) of the blocks. Therefore, classifying the defects may include classifying the defects based on the relative location of the defects to the lower left corner or the center of the block. In other words, classifying the defects may include binning the defects based on the relative location to the lower left corner or the center of the block. Classifying the defects may be further performed as described herein.

In one embodiment, classifying the defects includes classifying the defects based on the positions of the defects within the blocks and the types of the blocks in which the defects are located. For example, classifying the defects may include classifying the defects by block (e.g., memory cell, S/A, SWD, etc.). In particular, different types of blocks such as cell block, S/A block, SWD block, conjunction block, etc. can be found in the inspection data, which allows binning (and sampling) of defect by block type. In this manner, detects in different types of blocks can be included in different bins, and different types of defects in each of the different types of blocks may also be included in different bins (or different sub-bins).

In another embodiment, classifying the defects includes determining if the defects are systematic defects. Determining if the defects are systematic defects may be performed in various manners as described further herein.

In some embodiments, classifying the defects includes stacking the inspection data for multiple blocks having the same type to separate the defects into systematic defect bins or random defect bins. For example, classifying the defects may include stacking the cell area to identify systematic defects. In addition, different types of blocks within the memory device area may be separately stacked. For example, S/A, SWD, and conjunction blocks may be stacked separately from the memory cell blocks. In one such example, as shown inFIG. 2, inspection data14, which includes inspection data for defects16, for multiple blocks18having the same type may be stacked. Such stacking may be performed for the cell block area. Although four multiple blocks are shown inFIG. 2, the inspection data for any number of multiple blocks can be stacked.

In this manner, defects that have substantially the same position within the multiple blocks can be identified and binned into one group. For example, as shown inFIG. 3, results of stacking the inspection data (e.g., a stacked cell) may include different bins (e.g., Bin1, Bin2, Bin3, and Bin4). Each of the bins corresponds to defects detected at different positions within the multiple blocks18, and each of the bins may include multiple defects detected at substantially the same position within the multiple blocks. In this manner, each of the bins includes only defects detected at substantially the same position within the multiple blocks. In addition, results of stacking of the inspection data that may be generated by the method embodiments described herein may include a histogram or pareto chart, as shown inFIG. 4. As shown inFIG. 4, the histogram may illustrate the different bins (e.g., Bin1, Bin2, and Bin3) into which the defects were grouped as well as the frequency with which defects were grouped into each bin. As such, the histogram illustrates the frequency with which defects were detected at substantially the same position in multiple blocks having the same type. Defects that are detected at substantially the same position at relatively high frequencies are most likely systematic defects, while defects that are detected at substantially the same position at relatively low frequencies are most likely random detects. As such, as a result of stacking the inspection data for multiple blocks having the same type, the defects will effectively be separated into systematic defect bins and random defect bins, in this manner, defects that are most likely systematic defects can be identified.

In one such embodiment, the method also includes sampling the defects from the bins for defect review. For example, by stacking the memory block, random defects can be separated from systematic defects and then the defects can be sampled from the bins for defect review scanning electron microscopy (SEM) review) when the inspection is completed. In one such example, defects from the systematic defect bins may be sampled more heavily for defect review since these defects may be of most interest to the memory device manufacturer. In another example, defects can be sampled from the systematic defect bins as well as the random defect bins such that both systematic defects and random defects are reviewed. In this manner, the embodiments described herein can improve the sampling strategy (including integrated defect organizer (iDO) sampling) used for defect review, thereby providing significant value to memory device manufacturers, iDO is further described in the above-referenced patent application by Kulkarni et al.

In another embodiment, classifying the defects includes dividing one of the blocks into multiple regions within the block to separate different types of defects into different bins based on the multiple regions in which the positions of the defects are located. In this manner, classifying the defects may include binning by region. For example, a memory cell block can be divided into 2, 3, 4, or any number of regions to separate systematic defects or clusters (neighborhood defects) from random defects. In one such example, as shown inFIG. 5, the cell block of the memory device area may be divided into three regions (R1, R2, and R3). Some or all of the different types of blocks in the memory device area may be divided into any number of regions having any configuration that may or may not cover the entire area of the different types of blocks. Dividing one of the blocks into multiple regions within the block may be a sub-step of stacking inspection data as described above due to the stage accuracy limit of the inspection system. A boundary area may be set around a location to group defects into one bin. In this manner, defects that are located in the same region within a block in the memory device area can be grouped into the same bin. Results of such binning may include a histogram such as that shown inFIG. 4, except that unlike the histogram that may be generated by stacking inspection data, a histogram that is generated for results of binning by region may include different bins that correspond to different regions (e.g., Bin1may correspond to R1, Bin2may correspond to R2, etc.).

In another example of the above-described embodiment, different types of blocks within the memory device area (e.g., the cell block and the S/A block) can be divided into regions to separate defects by region. For example, as shown inFIG. 6, the cell block can be divided into three regions, which correspond to Bin1, Bin2, and Bin3, and the S/A block can be divided into two regions, which correspond to Bin4and Bin5. As shown inFIG. 6, some of the regions are located in a central area of the different types of blocks while other regions are located proximate to edges of the different types of blocks and surrounding the central area. In particular, as shown inFIG. 6, the region corresponding to Bin1is located in the centermost area of the cell block, the region corresponding to Bin3is located in a central area of the cell block surrounding the region corresponding to Bin1, while the region corresponding to Bin2is located proximate to the edges of the cell block surrounding the region corresponding to Bin3. In addition, the region corresponding to Bin4is located in a central area of the S/A block, while the region corresponding to Bin5is located proximate to the edges of the S/A block and surrounding the region corresponding to Bin4.

In this manner, defects that are located in a central area of the different types of blocks and defects that are located near edges of the different types of blocks can be separated into different bins. Therefore, some of the bins may be systematic defect bins while other bins may be random defect bins. For example, systematic defects such as polymer-induced bridges, shorts, small contacts, and lithography-related defects may tend to occur along the edge of the memory cell block or S/A cell block. In this manner, defects in bins corresponding to a region near the edges of one type of block may be identified as systematic defects at the edge and separated from random defects grouped into other bins. Results of such binning by region may include a histogram such as that described above, except that the number of bins shown in the histogram may correspond to the number of regions into which the different types of blocks are divided.

In one embodiment, classifying the defects includes identifying defects located in regions of the blocks that are prone to nuisance defects as nuisance defects and eliminating the defects identified as the nuisance defects from results of inspection of the wafer. In this manner, inspection system sensitivity can be increased by separating out nuisance on special areas such as edges of the memory block. Regions of the blocks that are prone to nuisance defects and special areas of the different types of blocks can be determined in any suitable manner. In addition, identifying defects located in regions of the blocks that are prone to nuisance defects as nuisance defects can be performed using region-based binning as described above (e.g., in which a region is defined as a region of a block that is prone to nuisance defects). The defects identified as nuisance defects can be eliminated from the inspection results in any suitable manner.

In some embodiments, classifying the defects includes determining a ratio of the numbers of the defects detected in at least two of the different types of blocks and classifying the defects in the at least two of the different types of blocks based on the ratio. In this manner, classification may be performed by defect ratio. The defect ratios that may be determined for classification may include, for example, cell/(SA+SWD+conjunction), cell/SA, cell/SWD, etc. Results of such classification may include a histogram such as that shown inFIG. 7. For example, as shown inFIG. 7, the frequency with which defects are detected for each of the different ratios (cell/(SA+SWD+conjunction), cell/SA, and cell/SWD) are shown in the histogram. Since random defects may be located relatively evenly throughout all of the different types of blocks while the numbers of systematic defects detected in the different types of blocks may vary dramatically, the ratios of the numbers of defects detected in at least two of the different types of blocks may effectively normalize for random defects while indicating which type of block or block exhibited systematic defects. For example, a relatively high ratio of defects detected in one type of block compared to one or more other types of blocks may be used to classify the defects detected in that one type of block as potentially systematic defects. In contrast, a relatively low ratio of defects detected in one type of block compared to one or more other types of blocks may be used to classify the defects detected in that one type of block as probable random defects.

In one embodiment, the method includes monitoring a ratio of the numbers of the defects detected in at least two of the different types of blocks. For example, the method may include monitoring any of the ratios described above. If a ratio shows an abnormality, that abnormality will indicate that systematic defects occurred versus random defects, which can be located everywhere regardless of block. In one such example, since different types of blocks such as those described herein can be found in the inspection data, the ratio of cell block defects versus other block defects can be monitored and an abnormality in that ratio will indicate a systematic defect occurrence. In addition, one of the ratios described above such as cell/(SA+SWD+conjunction) can be plotted as shown inFIG. 8as a function of time or wafer. A control limit for the ratio can also be shown in the plot, as shown inFIG. 8, such that a user can visually identify when the ratio goes above the control limit. A ratio above the control limit can indicate that a systematic defect causing mechanism is occurring, which could indicate to the user that there is a problem with the process and/or a process and design interaction issue. In this manner, the method may include monitoring region-based defect ratio, which could reduce the false excursion rate. For example, if the user cares about the defects in one type of block in the memory device area (e.g., a cell block), an excursion caused by a defect count increase in another type of block could be false. Therefore, by monitoring a defect ratio such as those described above (possibly at the same time as monitoring other things such as the total defect count), this false excursion rate could be reduced.

In a further embodiment, the method includes correlating the positions of the defects within the blocks with a bit map. The embodiments described herein provide better bit map correlation, which can speed up the learning cycle in the research and development stage. For example, the embodiments described herein provide improved coordinate accuracy for the determined positions of defects and specific information for defect coordinates thereby providing better hit map correlation to the inspection results. Improved bit map correlation will provide improved yield correlation, both of which provide significant value to memory device manufacturers. Correlating the positions of the defects within the blocks with a bit map may be performed in any suitable manner. Table 1 included below illustrates one example of results that may be produced by such correlating.

In another embodiment, classifying the defects includes correlating the positions of the defects within the blocks with a bit map and determining types of the defects based on results of the correlating step. For example, defects in the memory cell block may become row, column, and single or double bit failure type defects in a bit map. In addition, defects in the S/A block may become block fail, speed fail, etc. type defects in a bit map. In this manner, the types of the defects detected on the memory device area can be determined based on the types of failures that the defects will cause.

In some embodiments, classifying the defects includes correlating the positions of the defects within the blocks with a bit map, determining types of the defects based on results of the correlating step, and eliminating one or more of the types of the defects from results of inspection of the wafer. For example, by correlating the positions of the defects with a bit map, false defects, trivial defects, optical noise, etc. can be eliminated from the inspection results. In one such example, if some bins include false defects at a certain location in a block due to color variation, those bins can be sorted out of the inspection results. In the same manner, bins corresponding to trivial defects, optical noise, etc, can be filtered from the inspection results. Eliminating such defects may increase the clarity of the inspection results. In this manner, by identifying false and trivial defects, the sensitivity of the inspection process can be maximized and the inspection sensitivities for each block type can be optimized, both of which provide significant value to memory device manufacturers.

In another embodiment, the method includes determining one or more problems with one or more processes used to form the memory device area on the wafer based on results of classifying the defects. For example, the parameters reported by the inspection system used to perform the method can be collected and monitored for statistical process control (SPC) applications. In addition, as described above, classifying the defects may include rapidly binning large defect quantities according to whether the defects fail on the edge or the interior of a block. Results of such binning can be used to identify specific process problems (e.g., lithography-related process problems), thereby providing value to memory device manufacturers. Furthermore, as described above, the defects may be classified based on the types of blocks in which the defects are located. In this manner, the defect classification results may indicate which types of blocks the defects are located in. That information as well as the positions of the different types of blocks within the memory device area can be used to identify defects that fall on blocks located at the edge of the die or reticle shot. That information can be used to identify lithographic problems of a lithography scanner and polymer-induced defects of dry etching systems thereby providing value to memory device manufacturers.

In a further embodiment, the method includes determining one or more parameters of one or more processes to be performed on the memory device area based on the positions of the defects. For example, the coordinates of the positions of the defects determined as described herein may be used to drive additional processes performed on the memory device area such as critical dimension scanning electron microscopy (CDSEM) review and failure analysis (FA) diagnosis.

In one embodiment, the method includes determining one or more parameters of one or more processes to be performed on the memory device area based on distribution of the defects on the memory device area and the positions of the defects. For example, the correct places for CD measurement can be selected using the defect distribution on the memory block of the inspection map and the x, y coordinates that are reported by the embodiments described herein.

In another embodiment, the method includes altering one or more parameters of a process used by the inspection system to generate the inspection data based on results of classifying the defects such that at least two of the different types of blocks in the memory device area are inspected with different sensitivities. In this manner, the inspection sensitivities can be optimized for each block type, which can provide significant value to memory device manufacturers. For example, since portions of the inspection data that correspond to different types of blocks can be identified using the methods described herein (by determining the positions of inspection data acquired for the memory device area in design or inspection data space), different sensitivities (e.g., different thresholds) can be used to detect defects in different types of blocks. Appropriate sensitivities for detecting defects in different types of blocks can be determined using results of the method embodiments described herein (e.g., by using defect classification results and inspection data corresponding to different types of defects, the inspection sensitivities for different types of blocks can be set such that certain types of defects are detected while other types of defects are not) or in any other mariner.

In one embodiment, the method is performed by the inspection system during an inspection process performed by the inspection system for the wafer on which the memory device area is formed. In this manner, the method may be performed on-tool. As such, the embodiments described herein provide the benefit of an on-tool solution for classifying defects detected in a memory device area of a wafer. In addition, the embodiments described herein provide a complementary solution to DBB for memory customers, so the embodiments described herein can share the following on-tool benefits DBB. For example, the embodiments described herein provide anew technology to bin defects detected in a memory device area of a wafer better. Similar to iDO and integrated automatic defect classification (iADC), on-tool binning is more efficient than off-tool binning. With on-tool binning, users could discover and monitor systematic defects immediately after wafer inspection is completed. In addition, with improved coordinate accuracy from the extended platform (XP) package that is commercially available from KLA-Tencor, Milpitas, Calif. for BF inspection systems, which is a hardware update kit to improve coordinate accuracy, on-tool could output “near to perfect” design coordinate information for every defect. These coordinates could advantageously be used to drive CDSEM review and FA diagnosis. Furthermore, as soon as the inspection is finished, the results (e.g., one or more KLARF's) with all bin information can be output to a network for subsequent actions. In this manner, there will be no additional waiting time for results. Moreover, an algorithm configured to perform the methods described herein can be integrated into the iDO application on the fly on inspection systems (such as BF tools, for example, the 28xx tools that are commercially available from KLA-Tencor), and the combined outcome will be obtained when an inspection is finished.

Each of the embodiments of the method described above may include any other step(s) of any method(s) described herein. In addition, each of the embodiments of the method described above may be performed by any system embodiments described herein.

Any of the methods described herein may include storing results of one or more steps of one or more methods described herein in a storage medium. The results may include any of the results described herein. The results may be stored in any manner known in the art. In addition, the storage medium may include any storage medium described herein or any other suitable storage medium known in the art. After the results have been stored, the results can be accessed in the storage medium and used by any of the method or system embodiments described herein or any other method or system. Furthermore, the results may be stored “permanently,” “semi-permanently,” temporarily, or for some period of time. For example, the storage medium may be random access memory (RAM), and the results may not necessarily persist indefinitely in the storage medium. In addition, the results of any of the step(s) of any of the method(s) described herein can be stored using systems and methods such as those described in commonly owned U.S. patent application Ser. No. 12/234,201 by Bhaskar et al. filed Sep. 19, 2008, which published on Mar. 26, 2009 as U.S. Patent Application Publication No. 2009/0080759, and which is incorporated by reference as if fully set forth herein.

Another embodiment relates to a computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for classifying defects detected in a memory device area on a wafer. One such embodiment is illustrated inFIG. 9. In particular, as shown inFIG. 9, computer-readable medium20includes program instructions22executable on computer system24. The computer-implemented method for which the program instructions are executable is the computer-implemented method described above. For example, the computer-implemented method includes using the computer system to perform the following steps. The method includes determining positions of inspection data acquired for the memory device area by an inspection system. The memory device area includes different types of blocks. The inspection data includes data for defects detected in the memory device area. The method also includes determining positions of the defects with respect to a predetermined location within the blocks in which the defects are located based on the positions of the inspection data. In addition, the method includes classifying the defects based on the positions of the defects within the blocks. Each of the steps of the method described above may be performed as described further herein. The computer-implemented method may include any other step(s) of any other embodiment(s) described herein.

Program instructions22implementing methods such as those described herein may be stored on computer-readable medium20. The computer-readable medium may be a storage medium such as a read-only memory, a random access memory, a magnetic or optical disk, or a magnetic tape. In addition, the computer-readable medium may include any other suitable computer-readable medium known in the art.

Computer system24may take various forms, including a personal computer system, mainframe computer system, workstation, image computer, parallel processor, or any other device known in the art. In general, the term “computer system” may be broadly defined to encompass any device having one or more processors, which executes instructions from a memory medium. The computer system may be included in an inspection system. The inspection system may be configured as described herein.

An additional embodiment relates to a system configured to classify defects detected in a memory device area on a wafer. One embodiment of such a system is shown inFIG. 10. As shown inFIG. 10, the system includes inspection subsystem26configured to acquire inspection data for the memory device area formed on the wafer. Inspection subsystem26may include any suitable inspection subsystem such as those included in commercially available inspection systems the 28xx series tools, which are commercially available from KLA-Tencor). In addition, the inspection subsystem may be an inspection subsystem configured for BF inspection of a wafer and/or DF inspection of a wafer. The inspection subsystem may also be configured to acquire the inspection data by scanning light over the memory device area and detecting light reflected and/or scattered from the memory device area. Furthermore, the inspection subsystem may be configured for patterned wafer inspection. Moreover, an existing inspection system may be modified (e.g., a computer subsystem of the inspection system may be modified) such that the existing inspection system, including its inspection subsystem, can be configured and used as a system described herein. In addition, the inspection subsystem may be configured to perform any step(s) of any method(s) described herein. As described further above, the memory device area includes different types of blocks, and the inspection data includes data for defects detected in the memory device area.

As shown inFIG. 10, the system also includes computer subsystem28. The computer subsystem is configured to determine positions of the inspection data. The computer subsystem is also configured to determine positions of the defects with respect to a predetermined location within the blocks in which the defects are located based on the positions of the inspection data. In addition, the computer subsystem is configured to classify the defects based on the positions of the defects within the blocks. The computer subsystem may be configured to determine the positions of the inspection data, to determine the positions of the defects, and to classify the defects as described further herein. Furthermore, the computer subsystem may be configured to perform any step(s) of any method(s) described herein. The computer subsystem may be further configured as described above with respect to computer system24shown inFIG. 9. The embodiment of the system described above may be further configured as described herein.