Methods, apparatus, and media for obtaining target process windows of wafers

Abstract

Embodiments of this disclosure relate to methods, apparatus, and computer-readable storage media for acquiring a target process window of a wafer. The method for acquiring a process window of a wafer includes: determining a first process parameter range based on first process parameters associated with critical dimensions of the wafer, wherein the critical dimensions are critical dimensions of a plurality of cells in the wafer; determining a second process parameter range based on second process parameters associated with the critical dimensions and an image determination result, wherein the image determination result indicates the actual image quality of a corresponding cell among the plurality of cells; and determining a target process window based on the first and second process parameter ranges. Embodiments of this disclosure can obtain optimized process windows, improving the reliability of the process windows.

Description

Technical Field

[0001] Embodiments of this disclosure are primarily related to the fields of computers and semiconductors, and more specifically, to methods, apparatus, and computer-readable storage media for obtaining process windows of wafers. Background Technology

[0002] In semiconductor manufacturing, the process window size is often used to assess feasibility. The process window size depends on the critical dimensions measured under different focal lengths and exposure energies. These values ​​can be affected by factors such as measurement position offsets and chemical particles, leading to inaccurate measurements of the critical dimensions and an inability to accurately represent the process window.

[0003] Excessive deviation in measurement data can refer to data with significant deviations in critical dimensions, or data where the critical dimension values ​​are correct but the image data shows obvious flaws. Current technology primarily involves manually filtering out measurement data with excessive deviations, and then recalculating the process window using the filtered data. Summary of the Invention

[0004] According to an example embodiment of this disclosure, a scheme is provided for obtaining an optimized process window to improve the reliability of the process window.

[0005] According to a first aspect of this disclosure, a method for obtaining a target process window for a wafer is provided. The method includes: determining a first process parameter range corresponding to a first process window based on first process parameters associated with critical dimensions of the wafer, wherein the critical dimensions are critical dimensions of a plurality of cells in the wafer; determining a second process parameter range corresponding to a second process window based on second process parameters associated with the critical dimensions and image determination results of the plurality of cells, wherein the image determination results indicate the actual image quality of a corresponding cell among the plurality of cells; and determining a target process window based on the first process parameter range and the second process parameter range.

[0006] According to a second aspect of this disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor. The actions include: determining a first process parameter range corresponding to a first process window based on process parameters associated with critical dimensions of a plurality of cells in a wafer; determining a second process parameter range corresponding to a second process window based on process parameters associated with the critical dimensions and image determination results of the plurality of cells, the image determination results indicating the actual image quality of a corresponding cell among the plurality of cells; and determining a target process window based on the first process parameter range and the second process parameter range.

[0007] In some embodiments, the action further includes: for each of the plurality of units, matching the actual image features of the unit with standard image features; and determining an image determination result based on the matching result.

[0008] In some embodiments, determining the range of the second process parameters includes: determining a set of units from a plurality of units whose actual image quality is higher than a threshold based on the image determination result; and determining the range of the second process parameters based on the second process parameters corresponding to the critical dimensions of the set of units.

[0009] In some embodiments, determining the first process parameter range includes: fitting the critical dimensions of a plurality of units to obtain fitted critical dimensions; and determining the boundaries of the first process parameter range based on the first process parameters corresponding to the fitted critical dimensions.

[0010] In some embodiments, determining the range of second process parameters includes: fitting the critical dimensions of a plurality of units to obtain fitted critical dimensions; and determining the boundaries of the range of second process parameters based on the second process parameters corresponding to the fitted critical dimensions and the image determination results.

[0011] In some embodiments, the fitted critical dimension is also associated with at least one of the following: critical dimension consistency or target critical dimension.

[0012] In some embodiments, determining a target process window based on a first process parameter range and a second process parameter range includes: determining a first process window corresponding to the first process parameter range based on the boundary of the first process parameter range; determining a second process window corresponding to the second process parameter range based on the boundary of the second process parameter range; and determining a target process window based on the first process window and the second process window.

[0013] In some embodiments, determining the target process window based on a first process parameter range and a second process parameter range includes: determining a first region corresponding to the first process parameter range based on the boundary of the first process parameter range; determining a second region corresponding to the second process parameter range based on the boundary of the second process parameter range; and determining the target process window based on the overlapping region of the first region and the second region.

[0014] In some embodiments, the first region includes a first elliptical region, and the second region includes a second elliptical region, wherein the major axes of the first elliptical region and the second elliptical region are collinear.

[0015] In some embodiments, process parameters associated with the critical dimensions of multiple cells of the wafer include exposure energy and focal length.

[0016] According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to a first aspect of this disclosure.

[0017] According to a fourth aspect of this disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the method according to a first aspect of this disclosure.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0020] Figure 1 A schematic block diagram of a process window system for acquiring a wafer is shown according to some embodiments of the present disclosure;

[0021] Figure 2 A schematic block diagram illustrating an image determination result is shown according to some embodiments of the present disclosure;

[0022] Figure 3 The integrated key dimensions according to some embodiments of this disclosure are shown, as well as according to Figure 3 A schematic diagram of the wafer for determining the image results;

[0023] Figure 4 An example diagram is shown illustrating the boundary of a first parameter range determined based on process parameters corresponding to fitted critical dimensions according to an embodiment of the present disclosure;

[0024] Figure 5 An example diagram is shown illustrating the range and boundaries of a second parameter determined based on process parameters corresponding to fitted critical dimensions and image determination results according to an embodiment of the present disclosure;

[0025] Figure 6 An embodiment based on this disclosure is shown. Figure 4 and Figure 5 Example diagram showing how the boundaries of the determined parameter range are used to define the target process window;

[0026] Figure 7 A flowchart illustrating an example method for obtaining a wafer process window according to some embodiments of this disclosure is shown; and

[0027] Figure 8 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0028] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0029] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0030] The lithography process window, also known as the lithography process tolerance, refers to the range of exposure dose and defocus amount required to ensure that the mask pattern can be correctly replicated onto the silicon wafer. Lithography engineers must ensure that there is sufficient process window for all patterns on the mask. The common practice is to first perform a focus energy matrix (FEM) to find the optimal exposure energy and focus value, and then use the FEM data for process window analysis. The measured patterns include those specified in the standard manual and those deemed worthy of monitoring by the lithography engineer.

[0031] As briefly mentioned above, current technologies primarily rely on manual methods to eliminate measurement data with excessive deviations. This manual process can only remove statistical outliers from the measurement data, while underlying problems are often embedded in the images and cannot be identified solely from machine measurement statistics. Furthermore, manual elimination depends entirely on the operator's subjective judgment, making process standardization difficult. Additionally, the sheer volume of image data makes manual processing extremely challenging, further impacting processing efficiency and accuracy.

[0032] According to embodiments of this disclosure, a scheme for acquiring a process window for a wafer is proposed. In this scheme, a first process parameter range corresponding to a first process window is determined based on process parameters associated with critical dimensions of multiple cells of the wafer. A second process parameter range corresponding to a second process window is determined based on process parameters associated with critical dimensions and image determination results of multiple cells, the image determination results indicating the actual image quality of a corresponding cell among the multiple cells. A target process window is determined based on the first and second process parameter ranges.

[0033] In embodiments of this disclosure, the image determination results obtained through imaging methods can be correlated with the determination of process parameters to obtain a second process parameter range that better reflects the process window. Based on this, by utilizing a first process parameter range correlated with critical dimensions and the obtained second process parameter range, a more optimized process window can be obtained. Therefore, embodiments of this disclosure can improve the reliability of the process window in semiconductor manufacturing processes.

[0034] The following will refer to Figures 1 to 8 The embodiments of this disclosure will be described in detail below.

[0035] Example systems and operations

[0036] Figure 1 A schematic block diagram of a process window system 100 for acquiring a wafer, according to some embodiments of the present disclosure, is shown. Figure 1 As shown, the system 100 (also referred to as "system 100") for acquiring the process window of a wafer may include a process parameter providing device 110, an image determination result providing device 130, a process parameter range determining device 150, a process parameter boundary determining device 170, and a target process window determining device 190.

[0037] In some embodiments, the aforementioned plurality of devices may be implemented in the same physical device, for example, in a... Figure 1 In the computing device 180 shown. In other embodiments, at least some of the above-described devices can be implemented in the same physical device. For example, the process parameter providing device 110, the image determination result providing device 130, the process parameter range determining device 150, and the process parameter boundary determining device 170 can be implemented in the same physical device (e.g., implemented in a computing device such as...). Figure 1 The target process window determination device 190 can be implemented in another physical device (e.g., in a different physical device than the computing device 180). In some embodiments, the above-described multiple devices can also be implemented in different physical devices.

[0038] The computing device 180 may be a server or any personal computer, or any other processor-enabled device capable of wired or wireless data communication, or any combination thereof. The computing device 180 may also be other computing devices, systems, and / or architectures, including those incapable of wired or wireless data communication, and this disclosure does not limit this.

[0039] Continue to refer Figure 1 The process parameter providing device 110 can provide process parameters to the process parameter range determining device 150, such that the process parameter range determining device 150 determines a first process parameter range based on the provided process parameters. In some embodiments, the process parameters for determining the first process parameter range can be exposure energy and focal length in a semiconductor manufacturing process, and the exposure energy and focal length can be associated with the critical dimensions of individual cells in a semiconductor wafer. Specifically, for each set of exposure energy and focal length parameters, a corresponding critical dimension can be obtained for the pattern formed on the wafer by the photolithography process. Subsequently, these critical dimension data can be measured by machine or manually to obtain measured critical dimension data or a set of critical dimension data.

[0040] It should be understood that process parameters can also be any other parameters in semiconductor manufacturing processes, and this disclosure does not limit this. It should also be understood that the measured key data or sets of key data can be stored in the database of the process parameter providing device 110, directly in the process parameter providing device 110, or in a separate database that the process parameter providing device 110 can access to obtain the key data or sets of key data and provide them to the process parameter range determining device 150.

[0041] It should also be understood that the process parameter providing device 110 is optional. In fact, the measured key data or sets of key data can be stored directly in the database of the process parameter providing device 150, directly in the process parameter providing device 110, or in a separate database that the process parameter range determining device 150 can access to obtain these measured key data or sets of key data. That is, any method that enables the process parameter range determining device 150 to obtain the measured key data or sets of key data is feasible, and this disclosure does not impose any limitations on this.

[0042] Continue to refer Figure 1In some embodiments, at the process parameter range determining device 150, after obtaining measured key data or a set of key data, a first process parameter range can be determined. The first process parameter range may correspond to a first process window. That is, the first process parameter range determined by the process parameter range determining device 150 can yield a first process window for the semiconductor wafer. In some embodiments, the first process window can be obtained via the FEM method mentioned above. Of course, the first process window can also be obtained by any other suitable means, and this disclosure does not limit this.

[0043] Continue to refer Figure 1 In some embodiments, the image determination result providing device 130 may provide the image determination result 131 to the process parameter range determining device 150, so that the process parameter range determining device 150 determines a second process parameter range based on the process parameters and the provided image determination result 131. The image determination result providing device 130 may obtain the process parameters from the process parameter providing device 110, or it may obtain them by accessing a server storing the process parameters, or by any other means capable of obtaining the process parameters, which is not limited in this disclosure.

[0044] In some embodiments, the image determination result 131 can indicate the actual image quality of corresponding cells among multiple cells of the wafer. That is, the image features actually formed on the wafer can be compared with standard image features obtained corresponding to the respective exposure energy and focal length to obtain the image determination result. The following will combine... Figure 2 The specific implementation process for obtaining image judgment results is described in detail.

[0045] Figure 2 A schematic block diagram illustrating an image determination result is shown, according to some embodiments of the present disclosure. According to one embodiment, such as... Figure 2 As shown, the image result determination device 200 can be included in or coupled to the image determination result providing device 130 to determine the actual image quality. Specifically, the image result determination device 200 may include a standard image feature model generation module 205, an actual image quality feature extraction module 245, and an image determination result output module 280.

[0046] In some embodiments, the standard image feature model generation module 205 acquires a template image at box 210. The template image corresponds to a flawless image that should be generated under specific exposure energy and focal length. After acquiring the template image at 210, preliminary processing can be performed on the template image at box 215 via a filtering step and at box 220 via an alignment step, facilitating feature extraction operations on the template image at box 225. In one embodiment, the feature extraction operation can be performed, for example, by one or more of the following: extracting color moments from the image grayscale values ​​of the SEM image; extracting the contour of the photoresist imaging pattern, which can be achieved through color histograms, color correlation maps, color moments, or color aggregation vectors; and extracting other features of the photoresist imaging pattern through texture features, such as obtaining the edge roughness (LER) feature through coarseness, obtaining the white band feature in the image through contrast, extracting the linear alignment feature through regularity; and extracting the distortion feature through regularity. It should be noted that the above methods are merely exemplary, and any suitable image feature extraction techniques or algorithms in the field can be used to achieve the image feature extraction process, such as HOG feature extraction, LBP feature extraction, and Haar feature extraction, etc. This disclosure does not limit the scope of the method.

[0047] After feature extraction at box 225, since the features of the template image belong to flawless ideal data, a feature model can be constructed at box 230 based on the extracted features.

[0048] It should be noted that the operations of boxes 215 and 220 are optional, and the template image 210 can also be directly used for feature extraction, as long as the establishment of the feature model can be effectively achieved.

[0049] In some embodiments, in the actual image quality feature extraction module 245, the image to be detected can be obtained at box 250, and then filtering and alignment operations are performed at boxes 255 and 260 respectively, so that the template image can be used for feature extraction at box 265. After feature extraction is performed at box 265, the features of the extracted actual image can be compared at box 270 based on the feature model of box 230, and the image judgment result is output at box 280 based on the comparison result. In one embodiment, the feature comparison can be based on the distance between feature vectors (e.g., Euclidean distance) or other feature comparison methods in the art, which are not limited in this disclosure.

[0050] It should be noted that the operations of boxes 255 and 260 are optional. The image to be detected 250 can also be directly subjected to feature extraction, as long as feature extraction can be effectively achieved.

[0051] In some embodiments, the image assessment result can be a quality rating such as Good (G), Medium (M), or Poor (B), or a specific score based on the actual image quality, or any other suitable assessment method. Furthermore, the three-level quality rating of "Good (G), Medium (M), and Poor (B)" is merely exemplary; more rating standards can be set, such as a five-level rating standard of "Excellent, Good, Fair, Average, and Poor," etc., and this disclosure does not limit this.

[0052] As mentioned earlier, in Figure 1 In the image determination result providing device 130 shown, the second process parameter range is determined based on the image determination results of critical dimensions and multiple units. Figure 4 This illustrates the integrated key dimensions according to some embodiments of the present disclosure, and according to... Figure 3 A schematic diagram of the wafer for determining the image results.

[0053] exist Figure 3 In the schematic diagram of the image determination result of wafer pattern 300 shown, wafer pattern 300 is divided into multiple units. Each unit corresponds to the exposure energy setpoint on the X-axis (unit: mJ / cm). 2 And the focal length setpoint (in micrometers). Corresponding to the energy setting and focal length, the key dimensions of each unit can be obtained. For example, at an exposure energy of 31.1 mJ / cm²... 2 Furthermore, at a focal length of 0.005 micrometers, the critical dimension was measured to be 57.51 micrometers. In addition to the critical dimension, image determination results for each unit were also considered. Figure 3 The values ​​G, M, and B are shown in the example above. In the example where the critical dimension was measured to be 57.51 micrometers, the image determination result is G, which means the image determination result is good.

[0054] In addition, Figure 3 It can also be seen that the exposure energy is 36.1 mJ / cm. 2 Furthermore, when the focal length is 0.005 micrometers, the critical dimension is measured to be 38.62, and the image quality is ultimately rated as B, indicating that the image quality assessment result is poor.

[0055] Furthermore, the image determination result providing device 130 can also determine the critical dimension consistency of the critical dimensions of the wafer cells. As is well known to those skilled in the art, during semiconductor etching, because etching parameters (such as gas rate, bias power, etching mode, or gas ratio) need to be adjusted in the actual process, deviations may be found between the critical dimensions of the central portion and the critical dimensions of the edge portion of the wafer, affecting the critical dimension consistency. Therefore, critical dimension consistency is a concept referring to the deviation between the critical dimensions of the central portion and the critical dimensions of the edge portion of the wafer.

[0056] exist Figure 3 In the illustrated embodiment, for example, in the example of a cell with a critical size measured at 57.51 micrometers, the cell is shown in light gray, which indicates that its critical size is within the critical size conformance CDU, while in the example of a cell with a critical size measured at 38.62 micrometers, the cell is shown in dark gray, which indicates that its critical size exceeds the critical size conformance CDU. In this way, wafer diagram 300 fully illustrates the actual image quality of the corresponding cells among a plurality of cells.

[0057] It should be noted that, Figure 3 The image determination method described herein is merely exemplary, and those skilled in the art may adopt any other suitable determination method according to actual needs, without any limitation in this disclosure.

[0058] In some embodiments, when determining the second process parameter range based on image judgment results and process parameters, a group of cells with actual image quality higher than a threshold can be identified from multiple cells of the wafer, and the second process parameter range is determined based on the process parameters corresponding to the critical dimensions of this group of cells. The actual image quality can be quantified to obtain a score, and the threshold can be set according to the actual required image quality score. For example, when the score corresponding to the standard image quality is 55, the threshold can be set to 45. In this way, the target cell can be considered to have good image quality only if its actual image quality score is not lower than 45. It should be noted that the above method is merely exemplary, and any other suitable way of setting the threshold is feasible; this disclosure does not limit this.

[0059] In some embodiments, a weighted calculation of the feature vector matrix can be used to obtain the overall image quality score. In one embodiment, as mentioned above, since one or more of the color moment features, line edge roughness (LER) features, and photoresist features need to be considered during feature extraction, the actual image quality can be calculated based on the aforementioned feature vector matrix. In such an embodiment, different weights can be assigned to the photoresist feature vector, color moment feature vector, and line edge roughness (LER) feature vector, and then a weighted calculation can be performed to obtain the overall image quality score. In some embodiments, the process of quantifying image quality can be calculated using a method similar to Peak Signal-to-Noise Ratio (PSNR). Specifically, it can be calculated, for example, using the following equation:

[0060]

[0061] In equation (1), the distance is the distance between eigenvectors, such as Euclidean distance. The eigenvectors can be one or more of the following: photoresist eigenvectors, color moment eigenvectors, and line edge roughness (LER) eigenvectors, as described above.

[0062] It should be noted that the above-described methods for calculating the overall image quality score based on the feature vector matrix and similar to the peak signal-to-noise ratio (PSNR) are exemplary. Those skilled in the art can also determine the actual image quality score using any other suitable method, and this disclosure does not impose any limitations on this.

[0063] In some embodiments, the threshold can also be a grade value. Specifically, the threshold can directly correspond to the grade value after determining the actual image quality. For example, for an embodiment where the image quality is determined to be "Good Quality G, Medium Quality M, or Poor Quality B", the threshold can be set to "Higher than M". That is, the group of cells in a wafer whose actual image quality is determined to be higher than the threshold only includes the "Good Quality" cells. In some embodiments, the determination of the actual image quality can be implemented based on the feature vector matrix method described above or any other suitable method.

[0064] In other embodiments, the threshold may also be based on a matching degree (similarity) value. For example, the threshold may be set to a matching degree (similarity) of not less than 95%. In such embodiments, if the matching degree between the actual image features and the standard image features is higher than 95%, the range of the second process parameters is determined based on such a set of image units. It should be understood that the threshold can be set by a technician according to actual needs, or it can be dynamically set according to the overall image quality assessment, and this disclosure does not impose any limitations on this.

[0065] Continue to refer Figure 1 In some embodiments, after determining the first parameter range and the second parameter range, the process parameter range determining device 150 can directly obtain an optimized target process window via the target process window determining device 190 based on these two parameter ranges. Specifically, in this embodiment, the optimized target process window can be obtained based on a comparison of the first and second parameter ranges. This is because the second parameter range more accurately reflects the image quality level, and by comparing the two, an optimized process window can be obtained, improving the reliability of the process window in the semiconductor process.

[0066] In some embodiments, continue to refer to Figure 1The system 100 may also preferably include a process parameter boundary determination device 170. In the process parameter boundary determination device 170, the boundaries of a first parameter range and a second parameter range can be determined. In such an embodiment, an optimized target process window can be obtained by the target process window determination device 190 based on the determined boundaries of the first and second parameter ranges. It should be noted that the above example is merely illustrative; the determination of the boundaries of the first and second parameter ranges does not necessarily need to be implemented in a single process parameter boundary determination device 170, but can also be implemented in two separate devices, and this disclosure does not impose any limitations on this.

[0067] The following will combine Figure 4 This section details exemplary implementations for determining the boundaries of the first parameter range. Figure 4 An example diagram is shown illustrating the boundary of a first parameter range determined based on process parameters corresponding to fitted critical dimensions according to an embodiment of the present disclosure.

[0068] In some embodiments, as Figure 4 As shown, the schematic diagram 400 of the first parameter range boundary may include an X-axis representing focal length and a Y-axis representing exposure energy. Figure 4 In this process, the critical dimensions of multiple cells in the wafer can be fitted to obtain fitted critical dimensions, and the boundaries 410 and 420 of a first process parameter range can be determined based on the process parameters corresponding to the fitted critical dimensions. That is, the region within the boundaries 410 and 420 includes the first parameter range.

[0069] In some embodiments, fitting of key dimensions can be performed using the following equation:

[0070]

[0071] Where C is a constant and belongs to a fixed coefficient series, which can be predetermined based on the complexity of the fitted function curve; i and j correspond to the corresponding coordinate positions of the cells in the wafer, i and j are integers, i represents the cell in the i-th row of the wafer, and j represents the cell in the j-th column of the wafer. A cell can be locked by the coordinate positions of the i-th row and j-th column, E = 1 + 1 / exposure energy, and F k It is the focal length.

[0072] By fitting the measured critical dimensions using the above equations, the curve boundaries 410 and 420 of the first process parameter range can be determined. It should be noted that the above fitting method is merely exemplary; any other suitable fitting equations and techniques can be used to fit the measured critical dimensions, and this disclosure does not impose any limitations on this.

[0073] In some embodiments, when determining the curve boundaries 410 and 420 of the first process parameter range, the fitted critical dimension may also be associated with at least one of critical dimension consistency (CDU) or target critical dimension (TCD). In one embodiment, the curve boundaries 410 and 420 of the first process parameter range may also satisfy the following condition:

[0074] {Boundary}={x|x∈FittedCD, TargetCD-(TargetCD*CDU)<x<TargetCD+(TargetCD*CDU)} (3)

[0075] Where x is the value of the boundary of the first parameter range, FittedCD is the fitted key dimension, TargetCD (TCD) is the target key dimension, and CDU is the key dimension consistency.

[0076] It can be seen that in equation (3), the boundary of the first process parameter range needs to be included in the fitted critical dimension FittedCD, and simultaneously satisfy the above calculation conditions of the target critical dimension TCD and the critical dimension consistency CDU. By limiting the above conditions, the boundary of the first parameter set can be accurately obtained.

[0077] It should be understood that the above conditions regarding the target critical dimension TCD and critical dimension consistency CDU are merely exemplary, and any other conditions related to the target critical dimension TCD and critical dimension consistency CDU can be set to determine the boundaries 410 and 420 of the first process parameter range, which is not limited in this disclosure.

[0078] Figure 5 An example diagram is shown illustrating the range and boundaries of a second parameter determined based on process parameters corresponding to fitted key dimensions and image determination results according to embodiments of the present disclosure. In some embodiments, such as Figure 5 As shown, the schematic diagram 500 of the second parameter range boundary may include an X-axis representing focal length and a Y-axis representing exposure energy. Figure 5 In this process, the critical dimensions of multiple cells in the wafer can be fitted to obtain the fitted critical dimensions, and the boundaries 510 and 520 of the second process parameter range can be determined based on the process parameters corresponding to the fitted critical dimensions and the image determination results.

[0079] In this embodiment, fitting the critical dimensions of multiple cells in the wafer can be done using the following methods: Figure 4 The fitting method shown is consistent with the fitting method for the key dimensions. That is to say, Figure 5The method for calculating the range boundary of the second parameter can be the same as the fitting method in equation (2). Furthermore, it should be understood that... Figure 5 The fitting process in the code is not mandatory; it can be used directly. Figure 4 The fitted result can reduce the amount of computation and alleviate the computational pressure on system 100. It should also be understood that any other suitable fitting method different from equation (2) can be used, and this disclosure does not limit it.

[0080] In some embodiments, when determining the boundaries 510 and 520 of the second process parameter range, the following conditions may also be met:

[0081] {boundary} = {x|x∈FittedCD, “good quality” image determination result &TargetCD-(TargetCD*CDU)<x<TargetCD+(TargetCD*CDU)}} (4)

[0082] Where x is the value of the boundary of the second parameter range, FittedCD is the fitted key dimension, TargetCD (TCD) is the target key dimension, and CDU is the key dimension consistency.

[0083] It can be seen that, compared with equation (3), equation (4) also incorporates the image judgment result of "good quality". As mentioned above, the image judgment result comes from the actual image quality before fitting the key size. Therefore, when determining the boundaries 510 and 520 of the second process parameter range in equation (4), not only is the calculation condition that the boundary of the second process parameter range needs to be included in the fitted key size FittedCD and the target key size TCD and critical size consistency CDU mentioned above considered, but also the condition that the image judgment result is "good quality" needs to be combined. With the above conditions, the boundaries 510 and 520 of the second process parameter range can be more accurate and better reflect the actual process window situation. In this embodiment, referring to equation (4), the determination of the value x of the boundary of the second parameter range needs to meet three conditions, namely, x must be included in the fitted key size FittedCD, and it needs to simultaneously meet the condition that the image judgment result is "good quality" and the calculation condition of the target key size TCD and critical size consistency CDU. Because the above three conditions need to be met simultaneously, this disclosure does not limit the priority of the mathematical calculations of the three conditions. This is because when the three conditions are met simultaneously, the calculation results are consistent regardless of the priority. Those skilled in the art can adjust the calculation order accordingly based on the specific algorithm and the actual application scenario.

[0084] It should be noted that Equation (4) uses the condition of determining the quality as "good quality" in combination with the image. However, in other processes, the conditions of "medium quality" or "poor quality" can also be considered by exclusion, as long as the boundary of the second process parameter range can be accurately drawn. This disclosure does not limit this.

[0085] Figure 5 It also shows Figure 4 The curves 410 and 420 represent the boundaries of the first process parameter. A comparison shows that the curves 510 and 520 representing the boundaries of the second process parameter partially overlap with the curves 410 and 420 representing the boundaries of the first process parameter. However, for the curves 510 and 520 representing the boundaries of the second process parameter, which incorporate image-based quality judgment conditions, their range becomes irregular and smaller. This more accurately reflects the actual process window situation.

[0086] After the process parameter boundary determination device 170 determines the boundary of the first process parameter range and the boundary of the second process parameter range, the target process window determination device 190 can determine the first region that defines the first process window within the first process parameter range based on the boundary of the first process parameter range, and can determine the second region that defines the second process window within the second process parameter range based on the boundary of the second process parameter range, as well as the overlapping region of the first region and the second region, to determine the optimized target process window.

[0087] Figure 6 An embodiment based on this disclosure is shown. Figure 4 and Figure 5 An example diagram is used to determine the target process window by defining the boundaries of the first and second parameter ranges. In some embodiments, such as... Figure 6 As shown, the target process window schematic 600 may include an X-axis representing focal length and a Y-axis representing exposure energy. This has already been shown as... Figure 4 The boundaries 410 and 420 of the first process parameter range shown, and as... Figure 5 The boundaries of the second process parameter range shown are 510 and 520.

[0088] In some embodiments, the first region 630 of the first process window and the second region 610 of the second process window, which defines the second process window within the range of the second process parameters, can be determined based on the following methods. It should be noted that the first region 630 of the first process window can be... Figure 4 The process window with the largest area among the determined first process parameter range boundaries 410 and 420, and the second region 610 of the second process window can be... Figure 5 The largest process window within the boundaries 510 and 520 of the determined second process parameter range.

[0089] It should be noted that, Figure 6 The first region 630 and the second region 610 shown can be elliptical regions or regions of any other suitable shape, such as rectangular regions, and this disclosure does not limit them.

[0090] The following is an exemplary procedure for determining the first region 630 and the second region 610, which are elliptical regions. In some embodiments, such as Figure 6 As shown, the center critical dimension of the ellipse can be determined by the first region 630 and the second region 610, and this center critical dimension is used as the center point of the ellipse. In some embodiments, the center critical dimension can be a fitted critical dimension FittedCD that is closest to the target critical dimension TCD. Corresponding to Figure 6 The center point of the ellipse in the first region 630 can be the first point 635, and correspondingly, the center point of the ellipse in the second region 610 can be the second point 615.

[0091] In such an embodiment, after the center points of the first region 630 and the second region 610 are determined, the major and minor axes of the elliptical first region 630 and the elliptical second region 610 can be determined by the following equations:

[0092] Major axis of the first elliptical region = maximum focal length of the boundary of the first parameter range - minimum focal length of the boundary of the first parameter range (5)

[0093] It can be seen from equation (5) that the major axis of the first region 630 can be obtained by the difference between the maximum and minimum focal lengths of the first parameter range boundaries 410 and 420.

[0094]

[0095] Equation (6) shows that the minor axis of the first region 630 can be obtained by the ratio of the difference between the maximum and minimum exposure energy at the boundaries 410 and 420 of the first parameter range to the standard exposure energy. In other words, the minor axis of the first region 630 corresponds to the exposure latitude. Figure 6 In the example shown, the standard exposure energy is approximately 31.1 mJ / cm². 2 .

[0096] As is well known in the art, exposure latitude refers to the range of exposure energy that an optical exposure system can generate to meet the requirements of a design layout. It is typically defined by the range of exposure energy selection where the change in critical dimension values ​​detected by exposure results is within + / - 10%. In other words, if the linewidth change of the exposed pattern is relatively small when the resist deviates from the optimal exposure dose, it indicates that the resist has a large exposure latitude. Generally, the greater the exposure latitude, the greater the development latitude.

[0097] After obtaining the center point, major axis, and minor axis of the ellipse of the first region 630, the first region 630 can be drawn. Similarly, the major axis and minor axis of the second ellipse 610 can be determined by the following equation.

[0098] The major axis of the second elliptical region = the maximum focal length of the boundary of the second parameter range - the minimum focal length of the boundary of the second parameter range (7)

[0099] It can be seen from equation (7) that the major axis of the second region 610 can be obtained by the difference between the maximum and minimum focal lengths of the boundaries 510 and 520 of the second parameter range.

[0100]

[0101] Equation (8) shows that the minor axis of the second region 610 can be obtained by the ratio of the difference between the maximum and minimum exposure energies of the second parameter range boundaries 510 and 520 to the standard exposure energy. Figure 6 In the example shown, the standard exposure energy is approximately 31.1 mJ / cm². 2 .

[0102] In this embodiment, after obtaining the center point, major axis, and minor axis of the ellipse of the second region 610, the second region 610 can be drawn. Furthermore, the target process window can be determined based on the overlapping area of ​​the first region 630 and the second region 610.

[0103] It can be seen that when determining the first region 630 and the second region 610, the major axes of the ellipse of the first region 630 and the ellipse of the second region 610 can be collinear. This arrangement yields the most optimized and largest target process window. It should be understood that different technical means can also be used to determine the overlapping area of ​​the first region 630 and the second region 610, and this disclosure does not impose any limitations on this.

[0104] Example Method

[0105] Figure 7 A flowchart illustrating an example method for obtaining a wafer process window according to some embodiments of the present disclosure is shown. For example, method 700 may be performed by, for example... Figure 1 At least a portion of the system 100 shown (e.g., process parameter providing device 110, image determination result providing device 130, process parameter range determining device 150, process parameter boundary determining device 170, and target process window determining device 190) is used to perform this function. The following is in conjunction with... Figure 1Method 700 is described herein. It should be understood that method 700 may also include additional boxes not shown and / or certain boxes shown may be omitted. The scope of this disclosure is not limited in this respect.

[0106] In block 710, a first process parameter range is determined based on process parameters associated with critical dimensions of the wafer, where critical dimensions are the critical dimensions of multiple cells in the wafer. For example, process parameter providing device 110 may provide process parameters to process parameter range determining device 150 such that process parameter range determining device 150 determines the first process parameter range based on the provided process parameters.

[0107] In some embodiments, the process parameters determining the first process parameter range may be the exposure energy and focal length in a semiconductor manufacturing process, and the exposure energy and focal length may be associated with the critical dimensions of individual cells in a semiconductor chip wafer. Specifically, for each set of exposure energy and focal length parameters, a corresponding critical dimension can be obtained for the pattern formed on the wafer by the photolithography process. Subsequently, these critical dimension data can be measured by machine or manually to obtain measured critical dimension data or a set of critical dimension data.

[0108] It should be understood that process parameters can also be any other parameters in semiconductor manufacturing processes, and this disclosure does not limit this. It should also be understood that the measured key data or sets of key data can be stored in the database of the process parameter providing device 110, directly in the process parameter providing device 110, or in a separate database that the process parameter providing device 110 can access to obtain the key data or sets of key data and provide them to the process parameter range determining device 150.

[0109] It should also be understood that the process parameter providing device 110 is optional. In fact, the measured key data or sets of key data can be stored directly in the database of the process parameter providing device 150, directly in the process parameter providing device 110, or in a separate database that the process parameter range determining device 150 can access to obtain these measured key data or sets of key data. That is, any method that enables the process parameter range determining device 150 to obtain the measured key data or sets of key data is feasible, and this disclosure does not impose any limitations on this.

[0110] Continue to refer Figure 1In some embodiments, at the process parameter range determining device 150, after obtaining measured key data or a set of key data, a first process parameter range can be determined. The first process parameter range may correspond to a first process window. That is, based on the first process parameter range determined by the process parameter range determining device 150, a first process window for the semiconductor wafer can be obtained. In some embodiments, the first process window can be obtained via the FEM method mentioned above. Of course, the first process window can also be obtained by any other suitable means, and this disclosure does not limit this.

[0111] In some embodiments, determining the first process parameter range includes: fitting the critical dimensions of a plurality of units to obtain fitted critical dimensions; and determining the boundaries of the first process parameter range based on the process parameters corresponding to the fitted critical dimensions.

[0112] In box 720, a second range of process parameters is determined based on process parameters associated with critical dimensions and image determination results, the image determination results indicating the actual image quality of the corresponding unit among multiple units.

[0113] In some embodiments, the image determination result providing device 130 may provide the image determination result to the process parameter range determining device 150, so that the process parameter range determining device 150 determines a second process parameter range based on the process parameters and the provided image determination result. The image determination result providing device 130 may obtain the process parameters from the process parameter providing device 110, or it may obtain them by accessing a server storing the process parameters, or by any other means capable of obtaining the process parameters, which is not limited in this disclosure.

[0114] In some embodiments, the image determination result can indicate the actual image quality of a corresponding cell among multiple cells of the wafer. That is, the image features actually formed on the wafer can be compared with standard image features obtained with corresponding exposure energy and focal length to obtain the image determination result.

[0115] In some embodiments, for each of the multiple units, the actual image features of the unit can be matched with standard image features, and the image determination result can be determined based on the matching result.

[0116] In some embodiments, determining the second process parameter range may include: determining a set of units from a plurality of units whose actual image quality is higher than a threshold based on image determination results; and determining the second process parameter range based on process parameters corresponding to the critical dimensions of the set of units.

[0117] In box 730, a target process window can be determined based on a first process parameter range and a second process parameter range. In some embodiments, to determine the target process window, a first region defining the first process window within the first process parameter range can be determined based on the boundary of the first process parameter range; a second region defining the second process window within the first process parameter range can be determined based on the boundary of the second process parameter range; and the target process window can be determined based on the overlapping region of the first region and the second region.

[0118] Example device

[0119] Figure 8 A schematic block diagram of an example device 800 that can be used to implement embodiments of the present disclosure is shown. For example, such as Figure 1 One or more devices in the system 100 shown can be implemented by device 800. As shown, device 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 802 or loaded from storage unit 808 into random access memory (RAM) 803. Various programs and data required for the operation of device 800 can also be stored in RAM 803. CPU 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0120] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] Processing unit 801 executes the various methods and processes described above, such as method 700. For example, in some embodiments, method 700 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by CPU 801, one or more steps of method 700 described above may be performed. Alternatively, in other embodiments, CPU 801 may be configured to execute method 700 by any other suitable means (e.g., by means of firmware).

[0122] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0123] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0126] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for obtaining a target process window for a wafer, comprising: A range of first process parameters is determined based on first process parameters associated with critical dimensions of the wafer, the critical dimensions including the critical dimensions of multiple cells in the wafer; Based on a second process parameter associated with the critical dimension and the image determination result, a range of second process parameters is determined, wherein the image determination result indicates the actual image quality of the corresponding unit among the plurality of units; as well as Determining the target process window based on the first process parameter range and the second process parameter range includes: determining a first process window corresponding to the first process parameter range based on the boundary of the first process parameter range; determining a second process window corresponding to the second process parameter range based on the boundary of the second process parameter range; and determining the target process window based on the first process window and the second process window.

2. The method according to claim 1, further comprising: For each of the plurality of units, the actual image features of the unit are matched with standard image features; as well as Based on the matching results, the image judgment result is determined.

3. The method according to claim 1, wherein determining the range of the second process parameters includes: Based on the image determination result, a group of units whose actual image quality is higher than the threshold is determined from the plurality of units; as well as The range of the second process parameters is determined based on the second process parameters corresponding to the key dimensions of the set of units.

4. The method according to claim 1, wherein determining the first process parameter range includes: The key dimensions of the plurality of units are fitted to obtain fitted key dimensions; as well as Based on the first process parameters corresponding to the fitted critical dimensions, the boundaries of the range of the first process parameters are determined.

5. The method according to claim 4, wherein determining the range of the second process parameters includes: The key dimensions of the plurality of units are fitted to obtain fitted key dimensions; as well as Based on the second process parameters corresponding to the fitted key dimensions and the image determination results, the boundaries of the range of the second process parameters are determined.

6. The method of claim 4 or 5, wherein the fitted critical dimension is further associated with at least one of the following: critical dimension consistency or target critical dimension.

7. The method according to any one of claims 1-5, wherein determining the target process window based on the first process parameter range and the second process parameter range comprises: Based on the boundary of the first process parameter range, a first region corresponding to the first process parameter range is determined; Based on the boundaries of the second process parameter range, the corresponding second region within the second process parameter range is determined; as well as The target process window is determined based on the overlapping area of ​​the first region and the second region.

8. The method of claim 7, wherein the first region includes a first elliptical region and the second region includes a second elliptical region, wherein the major axes of the first elliptical region and the second elliptical region are collinear.

9. The method of claim 1, wherein the process parameters associated with the critical dimensions of the plurality of cells of the wafer include exposure energy and focal length.

10. An electronic device, comprising: processor; as well as A memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor, the actions including: A range of first process parameters is determined based on first process parameters associated with critical dimensions of the wafer, the critical dimensions including critical dimensions of multiple cells in the wafer; Based on second process parameters associated with the key dimensions and image determination results, a range of second process parameters is determined, wherein the image determination results indicate the actual image quality of a corresponding unit among the plurality of units; and Determining a target process window based on the first process parameter range and the second process parameter range includes: determining a first process window corresponding to the first process parameter range based on the boundary of the first process parameter range; determining a second process window corresponding to the second process parameter range based on the boundary of the second process parameter range; and determining the target process window based on the first process window and the second process window.

11. A computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the method according to any one of claims 1-9.

12. A computer program product comprising a computer program / instructions that, when executed by a processor, implement the method according to any one of claims 1-9.

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