阅读说明：本技术 用于获取晶圆的目标工艺窗口的方法、设备和介质 (Method, apparatus and medium for acquiring target process window of wafer ) 是由 不公告发明人 于 2021-08-19 设计创作，主要内容包括：本公开的实施例涉及用于获取晶圆的目标工艺窗口的方法、设备和计算机可读存储介质。用于获取晶圆的工艺窗口的方法包括：基于与晶圆的关键尺寸相关联的第一工艺参数,确定第一工艺参数范围,关键尺寸为晶圆中多个单元的关键尺寸；基于与关键尺寸和图像判定结果相关联的第二工艺参数,确定第二工艺参数范围,图像判定结果指示多个单元中的相应单元的实际图像质量；以及基于第一工艺参数范围和第二工艺参数范围,确定目标工艺窗口。本公开的实施例能够得到优化的工艺窗口,提升工艺窗口的可靠度。(Embodiments of the present disclosure relate to methods, apparatus, and computer-readable storage media for acquiring a target process window of a wafer. The method for acquiring the process window of the wafer comprises the following steps: determining a first process parameter range based on a first process parameter associated with a critical dimension of a wafer, wherein the critical dimension is a critical dimension of a plurality of units in the wafer; determining a second process parameter range based on a second process parameter associated with the critical dimension and an image decision result, the image decision result indicating an actual image quality of a respective cell of the plurality of cells; and determining a target process window based on the first process parameter range and the second process parameter range. The embodiment of the disclosure can obtain an optimized process window, and improve the reliability of the process window.)

1. A method for acquiring a target process window of a wafer, comprising:

determining a first process parameter range based on a first process parameter associated with a critical dimension of a wafer, the critical dimension comprising critical dimensions of a plurality of cells in the wafer;

determining a second process parameter range based on a second process parameter associated with the critical dimension and an image decision result, the image decision result indicating an actual image quality of a respective cell of the plurality of cells; and

determining the target process window based on the first process parameter range and the second process parameter range.

2. The method of claim 1, further comprising:

for each cell of the plurality of cells, matching actual image features of the cell to standard image features; and

determining the image determination result based on a result of the matching.

3. The method of claim 1, wherein determining the second process parameter range comprises:

determining a set of cells from the plurality of cells for which an actual image quality is higher than a threshold based on the image determination result; and

determining the second process parameter range based on the second process parameter corresponding to the critical dimension of the group of cells.

4. The method of claim 1, wherein determining the first process parameter range comprises:

fitting the critical dimensions of the plurality of cells to obtain fitted critical dimensions; and

determining a boundary of the first process parameter range based on the first process parameter corresponding to the fitted critical dimension.

5. The method of claim 4, wherein determining a second process parameter range comprises:

fitting the critical dimensions of the plurality of cells to obtain fitted critical dimensions; and

determining a boundary of the second process parameter range based on the second process parameter corresponding to the fitted critical dimension and the image determination result.

6. The method of claim 4 or 5, wherein the fitted critical dimensions are further associated with at least one of: critical dimension uniformity or target critical dimension.

7. The method of any of claim 1, wherein determining the target process window based on the first process parameter range and the second process parameter range comprises:

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.

8. The method, as recited in any of claims 1-5 or 7, wherein determining the target process window based on the first process parameter range and the second process parameter range, comprises:

determining a first area corresponding to the first process parameter range based on the boundary of the first process parameter range;

determining a corresponding second area in the second process parameter range based on the boundary of the second process parameter range; and

determining the target process window based on a coincident region of the first region and the second region.

9. The method of claim 8, wherein the first region comprises a first elliptical region and the second region comprises a second elliptical region, wherein major axes of the first and second elliptical regions are collinear.

10. The method of claim 1, wherein the process parameters associated with critical dimensions of a plurality of cells of the wafer comprise exposure energy and focus distance.

11. An electronic device, comprising:

a processor; and

a memory coupled with the processor, the memory having instructions stored therein that, when executed by the processor, cause the apparatus to perform acts comprising:

determining a first process parameter range based on a first process parameter associated with a critical dimension of a wafer, the critical dimension comprising critical dimensions of a plurality of cells in the wafer;

determining a second process parameter range based on a second process parameter associated with the critical dimension and an image decision result, the image decision result indicating an actual image quality of a respective cell of the plurality of cells; and

determining the target process window based on the first process parameter range and the second process parameter range.

12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of acquiring a semiconductor wafer according to any one of claims 1 to 10.

13. A computer program product comprising a computer program/instructions which, when executed by a processor, implement the method according to any one of claims 1-10.

Technical Field

Embodiments of the present disclosure relate generally to the field of computers and semiconductors, and more particularly, to a method, apparatus, and computer-readable storage medium for acquiring a process window of a wafer.

Background

Feasibility is often assessed in semiconductor manufacturing by process window size. The size of the process window is determined by the critical dimension values measured under different conditions of focus and exposure energy, which may be biased by different effects of measurement position drift, chemical particles, etc. This makes the critical dimension measurements incorrect and does not represent the correct situation for the process window.

The measurement data with excessive deviation can be the data with excessive critical dimension deviation or the data with correct critical dimension value but obvious flaw in the image data. In the prior art, measurement data with excessive deviation is mainly eliminated manually, and then a process window is recalculated by using the filtered data.

Disclosure of Invention

According to an example embodiment of the present disclosure, a scheme for deriving an optimized process window while improving reliability of the process window is provided.

According to a first aspect of the present disclosure, a method for acquiring a target process window of a wafer is provided. The method comprises the following steps: determining a first process parameter range corresponding to a first process window based on a first process parameter associated with a critical dimension of a wafer, the critical dimension being a critical dimension of a plurality of cells in the wafer; determining a second process parameter range corresponding to a second process window based on a second process parameter associated with the critical dimension and an image decision result for the plurality of cells, the image decision result indicating an actual image quality for a respective cell of the plurality of cells; and determining a target process window based on the first process parameter range and the second process parameter range.

According to a second aspect of the present disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled with the processor, the memory having instructions stored therein that, when executed by the processor, cause the device to perform actions. The actions include: determining a first process parameter range corresponding to a first process window based on a process parameter associated with a critical dimension of a plurality of cells of a wafer; determining a second process parameter range corresponding to the second process window based on the process parameters associated with the critical dimension and the image decision results for the plurality of cells, the image decision results indicating actual image quality for respective ones of the plurality of cells; and determining a target process window based on the first process parameter range and the second process parameter range.

In some embodiments, the actions further comprise: for each of a plurality of cells, matching actual image features of the cell with standard image features; and determining an image determination result based on the result of the matching.

In some embodiments, determining the second process parameter range comprises: determining a set of cells of which actual image quality is higher than a threshold from among the plurality of cells based on the image determination result; and determining a second process parameter range based on a second process parameter corresponding to the critical dimension of the set of cells.

In some embodiments, determining the first process parameter range comprises: fitting the critical dimensions of the plurality of units to obtain fitted critical dimensions; and determining a boundary of a first process parameter range based on the first process parameter corresponding to the fitted critical dimension.

In some embodiments, determining the second process parameter range comprises: fitting the critical dimensions of the plurality of units to obtain fitted critical dimensions; and determining a boundary of a second process parameter range based on a second process parameter corresponding to the fitted critical dimension and the image determination result.

In some embodiments, the fitted critical dimensions are further associated with at least one of: critical dimension uniformity or target critical dimension.

In some embodiments, determining the target process window based on the first process parameter range and the second process parameter range comprises: 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.

In some embodiments, determining the target process window based on the first process parameter range and the second process parameter range comprises: determining a first area corresponding to the first process parameter range based on the boundary of the first process parameter range; determining a second area 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 overlapping area of the first area and the second area.

In some embodiments, the first region comprises a first elliptical region and the second region comprises a second elliptical region, wherein the major axes of the first and second elliptical regions are collinear.

In some embodiments, the process parameters associated with the critical dimensions of the plurality of cells of the wafer include exposure energy and focus.

According to a third aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to the first aspect of the present disclosure.

According to a fourth aspect of the present disclosure, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the method according to the first aspect of the present disclosure.

It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:

FIG. 1 illustrates a schematic block diagram of a process window system for acquiring wafers in accordance with some embodiments of the present disclosure;

FIG. 2 illustrates a schematic block diagram for obtaining image decision results, in accordance with some embodiments of the present disclosure;

FIG. 3 illustrates a schematic diagram of a wafer integrating critical dimensions and image determination results according to FIG. 3, in accordance with some embodiments of the present disclosure;

FIG. 4 illustrates an example diagram of a boundary of a first parameter range determined based on process parameters corresponding to a fitted critical dimension in accordance with an embodiment of the disclosure;

FIG. 5 illustrates an exemplary graph of a second parameter range and its boundaries determined based on the fitted critical dimensions and the process parameters corresponding to the image determination results, in accordance with an embodiment of the disclosure;

FIG. 6 illustrates an example graph of determining a target process window based on the boundaries of the parameter ranges determined in FIGS. 4 and 5 in accordance with an embodiment of the disclosure;

FIG. 7 illustrates a flow chart of an example method for acquiring a process window of a wafer, in accordance with some embodiments of the present disclosure; and

FIG. 8 illustrates a block diagram of a computing device capable of implementing various embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

In describing embodiments of the present disclosure, the terms "include" and its derivatives should be interpreted as being inclusive, i.e., "including but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

The lithography process window, also known as the lithography process margin, refers to the range of exposure and defocus that ensures that the mask pattern can be properly copied onto the silicon wafer. The lithography engineer is assured that there is an adequate process window for all patterns on the mask. It is common practice to first make a focus Energy matrix FEM (focus Energy matrix), find the optimal exposure Energy and focus values, and use the FEM data for process window analysis. The measured patterns include patterns specified in standard manuals and deemed worthwhile to be monitored by lithography engineers.

As briefly mentioned above, the current technology mainly eliminates the measurement data with excessive deviation by manual means. In the manual elimination process, only statistically abnormal values can be eliminated from the measurement data, and hidden problems are often included in the image and cannot be obtained from the statistical data measured by the machine. Moreover, the manual exclusion method can only depend on the subjective behavior of the operator, and the process is difficult to standardize. In addition, because the data volume is very large when processing the image data, it is very difficult to manually process a large amount of data, further affecting the processing efficiency and the troubleshooting accuracy.

According to an embodiment of the present disclosure, a solution for acquiring a process window of a wafer is provided. In the approach, a first process parameter range corresponding to a first process window is determined based on a process parameter associated with a critical dimension of a plurality of cells of a wafer. A second process parameter range corresponding to a second process window is determined based on the process parameters associated with the critical dimension and the image decision results for the plurality of cells, the image decision results indicating actual image quality for the respective cells of the plurality of cells. A target process window is determined based on the first process parameter range and the second process parameter range.

In the embodiment of the disclosure, the image determination result obtained by the imaging method may be associated with the determination of the process parameter, so as to obtain a second process parameter range that can better embody the process window. On the basis, a more optimized process window can be obtained by utilizing the first process parameter range and the obtained second process parameter range which are related to the critical dimension. Therefore, the embodiment of the disclosure can improve the reliability of the process window in the semiconductor manufacturing process.

Embodiments of the present disclosure will be described in detail below with reference to fig. 1 to 8.

Example systems and operations

Fig. 1 illustrates a schematic block diagram of a process window system 100 for acquiring wafers in accordance with some embodiments of the present disclosure. As shown in fig. 1, the system 100 for acquiring a process window of a wafer (also referred to as "system 100") 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.

In some embodiments, multiple apparatuses described above may be implemented in the same physical device, for example, in a computing device 180 as shown in fig. 1. In other embodiments, at least a portion of the devices may 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, the process parameter boundary determining device 170 may be implemented in the same physical device (for example, implemented in the computing device 180 shown in fig. 1), and the target process window determining device 190 may be implemented in another physical device (for example, implemented in another physical device different from the computing device 180). In some embodiments, the above-mentioned apparatuses may also be respectively implemented in different physical devices.

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. Computing device 180 may also be other computing devices, systems, and/or architectures that include devices that are not capable of wired or wireless data communication, as the present disclosure is not limited in this respect.

With continued reference to fig. 1, the process parameter providing device 110 may provide the process parameter to the process parameter range determining device 150 such that the process parameter range determining device 150 determines the first process parameter range based on the provided process parameter. In some embodiments, the process parameter determining the first process parameter range may be an exposure energy and a focal length in a semiconductor fabrication process, and the exposure energy and the focal length may be associated with a critical dimension of each cell in the semiconductor wafer. Specifically, for each set of exposure energy and focus parameters, the corresponding critical dimension is obtained for the pattern formed on the wafer by the photolithography process. These critical dimension data may then be measured, either by machine or manually, to obtain measured critical dimension data or a formed set of critical dimension data.

It should be understood that the process parameter may be any other parameter in the semiconductor manufacturing process, and the disclosure is not limited thereto. It should also be understood that the measured key data or set of key data may be stored in a 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 may access to obtain the key data or set of key data and provide to the process parameter range determining device 150.

It should also be appreciated that the process parameter provider 110 is optional. In fact, the measured key data or set of key data may also 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 may access to retrieve these measured key data or set of key data. That is, any manner of enabling the process parameter range determining device 150 to obtain the measured critical data or set of critical data is possible and the disclosure is not limited thereto.

With continued reference to FIG. 1, in some embodiments, at the process parameter range determination device 150, a first process parameter range may be determined after the process parameter range determination device 150 obtains the measured key data or set of key data. 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 apparatus 150 may obtain the first process window of the semiconductor wafer. In some embodiments, the first process window may be obtained via the FEM approach mentioned above. Of course, the first process window may be obtained in any other suitable manner, which is not limited by this disclosure.

With continued reference to fig. 1, in some embodiments, the image decision result providing device 130 may provide the image decision result 131 to the process parameter range determining device 150 to cause the process parameter range determining device 150 to determine a second process parameter range based on the process parameter and the provided image decision result 131. The image determination result providing device 130 may obtain the process parameters from the process parameter providing device 110, or may obtain the process parameters by accessing a server storing the process parameters, or any other manner capable of obtaining the process parameters, which is not limited by the present disclosure.

In some embodiments, the image determination 131 may indicate an actual image quality of a respective cell of the plurality of cells of the wafer. That is, the image features actually formed on the wafer may be compared based on the obtained standard image features corresponding to the respective exposure energies and focal lengths to obtain the image determination result. The following describes a specific implementation process of the determination result of the acquired image in detail with reference to fig. 2.

Fig. 2 illustrates a schematic block diagram for obtaining image decision results, in accordance with some embodiments of the present disclosure. According to one embodiment, as shown in fig. 2, the image result determination device 200 may be included in the image determination result providing device 130 or coupled with the image determination result providing device 130 to determine the actual image quality. Specifically, the image result determination apparatus 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.

In some embodiments, the standard image feature model generation module 205 acquires a template image at block 210 that corresponds to an inpainted image that should be generated at a particular exposure energy and focus. After the template image is acquired at 210, the template image may be pre-processed via a filtering step at 215, via an alignment step at 220, to facilitate feature extraction operations on the template image at 225. In one embodiment, the feature extraction operation may be performed, for example, by one or more of: extracting color moments from the image gray value of the SEM image; the contour extraction of the imaging graph of the photoresist can be realized by a color histogram, a color correlation graph, a color moment or a color aggregation vector; and other features of the imaged pattern of the photoresist can be extracted by texture features, such as features that obtain edge roughness LER by coarseness, features that obtain white band in the image by contrast, and features that extract alignment by regularity; and extracting features of distortion through regularity. It should be noted that the above manners are merely exemplary, and any suitable image feature technical means or algorithm in the art may also be adopted to implement the image feature extraction process, such as HOG feature extraction, LBP feature extraction, Haar feature extraction, and the like, which is not limited by the present disclosure.

After feature extraction at block 225, a feature model may be constructed at block 230 based on the extracted features because the features of the template image belong to flawless ideal data.

It should be noted that the operations of the blocks 215 and 220 are optional, and the template image 210 may also be directly subjected to feature extraction as long as the establishment of the feature model can be effectively achieved.

In some embodiments, in the actual image quality feature extraction module 245, the image to be detected may be obtained at block 250, and then filtering and alignment operations may be performed at blocks 255 and 260, respectively, to facilitate feature extraction operations of the template image at block 265. After performing feature extraction at block 265, the extracted features of the actual image may be feature compared at block 270 based on the feature model of block 230, and an image decision result may be output at block 280 based on the comparison result. In one embodiment, the feature comparison may be based on a distance between feature vectors (e.g., euclidean distance, etc.) or other feature comparison means known in the art, and the present disclosure is not limited thereto.

It should be noted that the operations of the blocks 255 and 260 are optional, and the image 250 to be detected may also be directly subjected to feature extraction as long as the feature extraction can be effectively implemented.

In some embodiments, the image determination result may 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 determination. In addition, the 3-level quality ratings of "good quality G, medium quality M, and poor quality B" are only exemplary, and more rating criteria, for example, 5-level rating criteria of "good, general, poor", and the like, may be set, and the present disclosure does not limit this.

As described above, in the image determination result providing apparatus 130 shown in fig. 1, the second process parameter range is determined based on the critical dimension and the image determination results of the plurality of cells. Fig. 4 illustrates a schematic diagram of a wafer integrating critical dimensions according to some embodiments of the present disclosure and the image determination result according to fig. 3.

In the schematic diagram of the image determination result of the wafer map 300 shown in fig. 3, the wafer map 300 is divided into a plurality of units. Each cell corresponding to an exposure energy set point (in mJ/cm) for the X-axis²) And a focus set point (in microns). The critical dimensions of each cell can be obtained corresponding to the energy setting and focal length. For example, at an exposure energy of 31.1mJ/cm²And a focal length of 0.005 microns, the critical dimension was measured to be 57.51 microns. Meanwhile, on the basis of the critical dimension, image determination results 131 for images of respective cells are also combined, as shown at G, M and B in fig. 3. In the example described above in which the critical dimension was measured to be 57.51 micrometers, the image determination result was G, representing that the image determination result was good.

In addition, it can also be seen in FIG. 3 that the exposure energy was 36.1mJ/cm²And the focal length is 0.005 microns, the critical dimension is measured as 38.62, and the image quality is finally judged as B, which represents that the image judgment result is poor.

In addition, the image determination result providing device 130 may further determine the cd uniformity of the cd of the unit of the wafer. As is well known to those skilled in the art, during semiconductor etching, since the etching parameters (such as gas rate, bias power, etching mode or gas ratio) need to be adjusted in the actual process, it is observed that there is a deviation between the critical dimension of the central portion and the critical dimension of the edge portion of the wafer, which affects the uniformity of the critical dimension. Therefore, cd uniformity is a concept of the deviation between the cd at the center portion and the cd at the edge portion of the wafer.

In the embodiment shown in fig. 3, for example, in the example of a cell having a critical dimension measured as 57.51 microns, the cell is shown in light gray, which may indicate that its critical dimension is in the cd uniformity CDU, while in the example of a cell having a critical dimension measured as 38.62, the cell is shown in dark gray, which indicates that its critical dimension exceeds the cd uniformity CDU. As such, the wafer map 300 fully illustrates the actual image quality of the respective cells of the plurality of cells.

It should be noted that the image determination method in fig. 3 is only an example, and those skilled in the art may also adopt any other suitable determination method according to actual needs, and the disclosure does not limit this.

In some embodiments, in determining the second process parameter range based on the image determination result and the process parameter, a set of cells of the wafer having an actual image quality higher than a threshold value may be determined, and the second process parameter range may be determined based on the process parameter corresponding to the critical dimension of the set of cells. The actual image quality may be quantized to obtain a score, and the threshold may be set according to the actually required image quality score. For example, when the score corresponding to the standard image quality is 55, the threshold value may be set to 45, and thus the target unit may be considered as good image quality only if the score of the actual image quality is not lower than 45. It should be noted that the above-mentioned manner is only exemplary, and any other suitable manner of setting the threshold is possible, and the disclosure is not limited thereto.

In some embodiments, a feature vector matrix weighting calculation may be used to derive a score for the overall quality of the image. In one embodiment, as described previously, since one or more of the color moment features, line edge roughness LER features, and photoresist features need to be considered in the feature extraction process, the actual quality of the image can be calculated based on the above feature vector matrix. In such embodiments, weighting scores based on different proportions may be assigned to the photoresist feature vector, the color moment feature vector, and the line edge roughness LER feature vector, and then a weighting calculation may be performed to obtain a score of the overall quality of the image. In some embodiments, the process specific procedure for quantifying image quality may be calculated by similar to peak signal-to-noise ratio PSNR. Specifically, it can be calculated, for example, by the following equation:

here, in equation (1), the distance is a distance between feature vectors, such as a euclidean distance or the like. The feature vector may be, for example, one or more of a photoresist feature vector, a color moment feature vector, and a line edge roughness LER feature vector as previously described.

It should be noted that, the above manner of calculating the score based on the eigenvector matrix and the manner similar to the peak signal-to-noise ratio PSNR to obtain the integrated quality score of the image is exemplary, and those skilled in the art may also determine the score of the actual image quality according to any other suitable manner, which is not limited by the disclosure.

In some embodiments, the threshold may also be a rank value. Specifically, the threshold value may directly correspond to the gradation value after the actual image quality is determined. For example, for embodiments where the image determination result is "good G, medium M, or poor B," the threshold may be set to "higher than M," that is, a group of cells of the wafer for which the actual image quality determined is higher than the threshold includes only "good" cells. In some embodiments, the determination of actual image quality may be based on the eigenvector matrix approach described above or any other suitable approach.

In other embodiments, the threshold may be based on a matching degree (similarity) value. For example, the threshold may be set to not less than 95% matching degree (similarity). In such an embodiment, if the actual image features match the standard image features by more than 95%, the second process parameter range is determined based on such a set of image elements. It should be understood that the threshold may be set by a skilled person according to actual needs, or may be dynamically set according to the overall image quality determination condition, which is not limited by the present disclosure.

With continued reference to fig. 1. In some embodiments, the process parameter range determining device 150 may obtain the optimized target process window directly via the target process window determining device 190 based on the two parameter ranges of the first parameter range and the second parameter range after determining the first parameter range and the second parameter range. In this embodiment, in particular, the optimized target process window may be derived based on a comparison of the first parameter range and the second parameter range. Therefore, the second parameter range can reflect the image quality level more truly, and the optimized process window can be obtained through comparison of the second parameter range and the image quality level, so that the reliability of the process window in the semiconductor process is improved.

In some embodiments, with continued reference to fig. 1, the system 100 may also preferably include a process parameter boundary determination device 170. In the process parameter boundary determining means 170, the boundary of the first parameter range and the boundary of the second parameter range may be determined. In such an embodiment, the optimized target process window may be obtained by the target process window determination device 190 based on the determined boundaries of the first parameter range and the second parameter range. It should be noted that the above examples are merely illustrative, and the determination of the boundary of the first parameter range and the boundary of the second parameter range need not be implemented in a single process parameter boundary determination device 170, but may be implemented in two separate devices, which is not limited by the present disclosure.

An exemplary embodiment of determining the boundary of the first parameter range is described in detail below in conjunction with fig. 4. FIG. 4 illustrates an example graph of a boundary of a first parameter range determined based on process parameters corresponding to a fitted critical dimension according to an embodiment of this disclosure.

In some embodiments, as shown in FIG. 4, the first parameter range boundary diagram 400 may include an X-axis representing focal length and a Y-axis representing exposure energy. In fig. 4, the critical dimensions of a plurality of cells in the wafer may be fit to obtain a fitted critical dimension, and a boundary 410 and a boundary 420 of the first process parameter range may be determined based on the process parameters corresponding to the fitted critical dimension. That is, the boundary 410 and the area within the boundary 420 include the first parameter range.

In some embodiments, fitting to the critical dimensions may be performed by the following equation:

wherein, C is a constant and belongs to a fixed coefficient column, and the coefficient column can be predetermined according to the complexity of the fitted function curve; i and j correspond to their respective coordinate positions of the cells in the wafer, i and j being integers, i representing the cell in the ith row in the wafer, j representing the cell in the jth column in the wafer, one cell being lockable by the coordinate positions of i row and j column, E ═ 1+ 1/exposure energy, and F_kIs the focal length.

Using the above equation, fitting the measured critical dimensions, the curve boundary 410 and the curve boundary 420 for the first process parameter range may be determined. It should be noted that the above fitting manner is only exemplary, and any other suitable fitting equation and technical means may be adopted to implement the fitting of the measured critical dimension, which is not limited by the present disclosure.

In some embodiments, the fitted critical dimension may also be associated with at least one of a critical dimension uniformity CDU or a target critical dimension TCD when determining the curve boundary 410 and the curve boundary 420 for the first process parameter range. In one embodiment, the following condition may also be satisfied for the curve boundary 410 and the curve boundary 420 of the first process parameter range:

{ boundary } { x | x ∈ FittedCD, TargetCD- (TargetCD × CDU) < x < TargetCD + (TargetCD × CDU) } (3)

Where x is the value of the boundary of the first parameter range, FittedCD is the fitted critical dimension, targetcd (tcd) is the target critical dimension, and CDU is the critical dimension uniformity.

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 satisfies the above-mentioned calculation conditions of the target critical dimension TCD and the cd uniformity CDU. By limiting the above conditions, the boundary of the first parameter set can be accurately obtained.

It should be appreciated that the above conditions regarding the target critical dimension TCD and the CD uniformity CDU are merely exemplary, and any other conditions related to the target critical dimension TCD and the CD uniformity CDU may be set to determine the boundary 410 and the boundary 420 of the first process parameter range, which is not limited by the present disclosure.

Fig. 5 is a diagram illustrating an example of a second parameter range and its boundaries determined based on the fitted cd and the process parameter corresponding to the image determination result, according to an embodiment of the disclosure. In some embodiments, as shown in FIG. 5, second parameter range boundary diagram 500 may include an X-axis representing focal length and a Y-axis representing exposure energy. In fig. 5, the critical dimensions of the plurality of cells in the wafer may be fitted to obtain fitted critical dimensions, and the boundaries 510 and 520 of the second process parameter range are determined based on the process parameters corresponding to the fitted critical dimensions and the image determination results.

In this embodiment, fitting the critical dimensions of the plurality of cells in the wafer may take a fitting consistent with the fitting of critical dimensions shown in fig. 4. That is, the manner in which fig. 5 calculates the second parameter range boundary may take the same fitting manner as equation (2). In addition, it should be understood that the fitting process in fig. 5 is not necessarily performed, and the fitting result in fig. 4 can be directly used, which can reduce the amount of calculation and reduce the calculation pressure of the system 100. It should also be understood that any other suitable fitting other than equation (2) may be employed, and the present disclosure is not limited thereto.

In some embodiments, in determining the boundaries 510 and 520 of the second process parameter range, the following conditions may also be satisfied:

{ boundary } { x | x ∈ fitredcd, "good quality" image determination result & TargetCD- (TargetCD × CDU) < x < TargetCD + (TargetCD × CDU) } (4)

Wherein x is the value of the boundary of the second parameter range, FittedCD is the fitted critical dimension, targetcd (tcd) is the target critical dimension, and CDU is the critical dimension uniformity.

It can be seen that the image decision result for "good quality" is also incorporated in equation (4) compared to equation (3). As previously described, the image decision results are from the actual image quality before fitting the critical dimensions. Therefore, in determining the boundaries 510 and 520 of the second process parameter range in equation (4), not only the above-mentioned calculation conditions that the boundaries of the second process parameter range need to be included in the fitted critical dimension FittedCD and the target critical dimension TCD and the cd uniformity CDU, but also the condition that the image determination result is "good quality" needs to be taken into account. By limiting the above conditions, the boundaries 510 and 520 of the second process parameter range can be more precise and reflect the actual process window. In this embodiment, referring to equation (4), the determination of the value x of the boundary of the second parameter range needs to satisfy three conditions, that is, x is to be included in the fitted critical dimension FittedCD, and needs to satisfy both the condition that the image determination result is "good quality" and the calculation conditions of the target critical dimension TCD and the critical dimension uniformity CDU. Because the three conditions need to be satisfied simultaneously, the present disclosure does not limit the priority of mathematical computation of the three conditions, because the computation results obtained by the three conditions are consistent no matter what priority they are based on when the three conditions are satisfied simultaneously, and those skilled in the art can adjust the computation order accordingly according to the specific algorithm and the actual application scenario.

It should be noted that, the equation (4) adopts the condition that the quality is determined to be "good" in combination with the image, however, in the process of other processes, the condition of "medium quality" or "poor quality" may be considered exclusively, as long as the boundary of the second process parameter range can be drawn accurately, which is not limited by the present disclosure.

Fig. 5 also shows curves 410 and 420 for the boundaries of the first process parameter in fig. 4. By comparison, it can be seen that there is a region where the curves 510 and 520 of the boundary of the second process parameter partially overlap the curves 410 and 420 of the boundary of the first process parameter, whereas the range becomes irregular and smaller for the curves 510 and 520 of the boundary of the second process parameter combined with the image determination quality condition. In this way, the actual process window situation is more accurately represented.

After the process parameter boundary determining device 170 determines the boundary of the first process parameter range and the boundary of the second process parameter range, a first region defining the first process window within the first process parameter range may be determined based on the boundary of the first process parameter range in the target process window determining device 190, and a second region defining the second process window within the second process parameter range and a coinciding region of the first region and the second region may be determined based on the boundary of the second process parameter range to determine an optimized target process window.

FIG. 6 illustrates an example graph of determining a target process window based on the boundaries of the first parameter range and the second parameter range determined in FIGS. 4 and 5, according to an embodiment of the disclosure. In some embodiments, as shown in FIG. 6, the target process window schematic 600 may include an X-axis representing focus distance and a Y-axis representing exposure energy. Where the boundaries 410 and 420 of the first process parameter range as shown in figure 4 and the boundaries 510 and 520 of the second process parameter range as shown in figure 5 have been shown.

In some embodiments, the first region 630 of the first process window and the second region 610 of the second process window within the second process parameter range may be determined based on the following. It should be noted that the first region 630 of the first process window may be the largest area of the first process parameter range boundaries 410 and 420 identified in FIG. 4, and the second region 610 of the second process window may be the largest area of the second process parameter range boundaries 510 and 520 identified in FIG. 5.

It should be noted that the first region 630 and the second region 610 shown in fig. 6 may be elliptical regions, or may be any other suitable shape regions, such as rectangular regions, which is not limited by the present disclosure.

The determination process of the first region 630 and the second region 610 determined as the elliptical regions will be described below in an exemplary manner. In some embodiments, as shown in fig. 6, the central critical dimension of the ellipse may be determined by the first region 630 and the second region 610, and the central critical dimension is taken as the center point of the ellipse. In some embodiments, the center critical dimension may be the fitted critical dimension fitededcd that is closest to the target critical dimension TCD. Corresponding to fig. 6, the elliptical center point of the first region 630 may be the first point 635, and correspondingly, the elliptical center point of the second region 610 may be the second point 615.

In such embodiments, after the first region 630 and the second region 610 center points are determined, the major and minor axes of the elliptical first region 630 and the elliptical second region 610 may be determined by the following equations:

the major axis of the first ellipse region is the boundary maximum focal length of the first parameter range-the boundary minimum focal length of the first parameter range (5)

As can be seen from equation (5), the long axis of the first region 630 can be obtained by the difference between the maximum focal length and the minimum focal length of the boundaries 410 and 420 of the first parameter range.

As can be seen from equation (6), the short axis of the first region 630 can be obtained by the ratio of the difference between the maximum exposure energy and the minimum exposure energy of the boundaries 410 and 420 of the first parameter range to the standard exposure energy. That is, the short axis of the first region 630 corresponds to exposure latitude. In the example shown in FIG. 6, the standard exposure energy is approximately 31.1mJ/cm²。

As is well known in the art, exposure latitude means that the optical exposure system can produce a range of exposure energies that meet the design layout requirements. Typically defined using a selected range of exposure energies in which the amount of change in critical dimension values detected by the exposure results is within +/-10%. That is, if the line width variation of the exposure pattern is small under the condition that the resist deviates from the optimal exposure dose, the resist has larger exposure latitude. Generally, the greater the exposure latitude, the greater the development latitude.

After the center point, and the major and minor axes of the ellipse of the first region 630 are obtained, the first region 630 can be drawn. Similarly, the major and minor axes of the elliptical second region 610 may be determined by the following equations.

The major axis of the second ellipse region is the boundary maximum focal length of the second parameter range-the boundary minimum focal length of the second parameter range (7)

As can be seen from equation (7), the long axis of the second region 610 can be obtained by the difference between the maximum focal length and the minimum focal length of the boundaries 510 and 520 of the second parameter range.

As can be seen from equation (8), the short axis of the second region 610 can be obtained by the ratio of the difference between the maximum exposure energy and the minimum exposure energy of the boundaries 510 and 520 of the second parameter range to the standard exposure energy. In the example shown in FIG. 6, the standard exposure energy is approximately 31.1mJ/cm²。

In this embodiment, after the center point of the ellipse of the second region 610, and the major and minor axes are obtained, the second region 610 can be drawn. Further, the target process window may be determined based on the coincident region of the first region 630 and the second region 610.

It can be seen that, in the above determination of 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 may be collinear. By such an arrangement, the most optimal and largest target process window can be obtained. It should be understood that different technical means can be adopted to determine the overlapping area of the first area 630 and the second area 610, and the disclosure is not limited thereto.

Example method

Fig. 7 illustrates a flow diagram of an example method for acquiring a process window of a wafer, in accordance with some embodiments of the present disclosure. For example, the method 700 may be performed by at least a portion of the system 100 (e.g., the process parameter providing device 110, the image decision result providing device 130, the process parameter range determining device 150, the process parameter boundary determining device 170, and the target process window determining device 190) as shown in FIG. 1. Method 700 is described below in conjunction with fig. 1. It is to be understood that method 700 may also include additional blocks not shown and/or may omit certain blocks shown. The scope of the present disclosure is not limited in this respect.

At block 710, a first process parameter range is determined based on a process parameter associated with a critical dimension of a wafer, the critical dimension being a critical dimension of a plurality of cells in the wafer. For example, the process parameter providing device 110 may provide the process parameter to the process parameter range determining device 150 such that the process parameter range determining device 150 determines the first process parameter range based on the provided process parameter.

In some embodiments, the process parameter determining the first process parameter range may be an exposure energy and a focal length in a semiconductor fabrication process, and the exposure energy and the focal length may be associated with a critical dimension of each cell in a semiconductor chip wafer. Specifically, for each set of exposure energy and focus parameters, the corresponding critical dimension is obtained for the pattern formed on the wafer by the photolithography process. These critical dimension data may then be measured, either by machine or manually, to obtain measured critical dimension data or a formed set of critical dimension data.

It should be understood that the process parameter may be any other parameter in the semiconductor manufacturing process, and the disclosure is not limited thereto. It should also be understood that the measured key data or set of key data may be stored in a 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 may access to obtain the key data or set of key data and provide to the process parameter range determining device 150.

It should also be appreciated that the process parameter provider 110 is optional. In fact, the measured key data or set of key data may also 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 may access to retrieve these measured key data or set of key data. That is, any manner of enabling the process parameter range determining device 150 to obtain the measured critical data or set of critical data is possible and the disclosure is not limited thereto.

With continued reference to FIG. 1, in some embodiments, at the process parameter range determination device 150, a first process parameter range may be determined after the process parameter range determination device 150 obtains the measured key data or set of key data. 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 of the semiconductor wafer can be obtained. In some embodiments, the first process window may be obtained via the FEM approach mentioned above. Of course, the first process window may be obtained in any other suitable manner, which is not limited by this disclosure.

In some embodiments, determining the first process parameter range comprises: fitting the critical dimensions of the plurality of units to obtain fitted critical dimensions; and determining a boundary of the first process parameter range based on the process parameter corresponding to the fitted critical dimension.

At block 720, a second process parameter range is determined based on the process parameters associated with the critical dimension and the image decision result, the image decision result indicating the actual image quality of the respective cell of the plurality of cells.

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

In some embodiments, the image determination may indicate an actual image quality of a respective cell of the plurality of cells of the wafer. That is, the image features actually formed on the wafer may be compared based on the obtained standard image features corresponding to the respective exposure energies and focal lengths to obtain the image determination result.

In some embodiments, the actual image features of the cells may be matched with the standard image features for each of the plurality of cells, and based on the result of the matching, an image decision result may be determined.

In some embodiments, determining the second process parameter range may include: determining a set of cells of which actual image quality is higher than a threshold from among the plurality of cells based on the image determination result; and determining a second process parameter range based on the process parameter corresponding to the critical dimension of the group of cells.

At block 730, a target process window may be determined based on the first process parameter range and the second process parameter range. In some embodiments, to determine the target process window, a first region within a first process parameter range that defines a first process window may be determined based on a boundary of the first process parameter range; determining a second region within the first process parameter range that defines a second process window based on a boundary of the second process parameter range; and determining a target process window based on the overlapping area of the first area and the second area.

Example apparatus

Fig. 8 illustrates a schematic block diagram of an example device 800 that may be used to implement embodiments of the present disclosure. For example, one or more of the devices in system 100 as shown in fig. 1 may be implemented by apparatus 800. As shown, device 800 includes a Central Processing Unit (CPU)801 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)802 or loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processing unit 801 performs the various methods and processes described above, such as the method 700. For example, in some embodiments, the method 700 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM 802 and/or communications unit 809. When loaded into RAM 803 and executed by CPU 801, a computer program may perform one or more of the steps of method 700 described above. Alternatively, in other embodiments, CPU 801 may be configured to perform method 700 by any other suitable means (e.g., by way of firmware).

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.