CN118398515A - A detection process and detection system for GaAs etching process - Google Patents

Abstract

The invention belongs to the technical field of etching process supervision, and specifically is a detection process and a detection system for a GaAs etching process, wherein the detection system comprises an intelligent control platform, a light source module, a high-precision camera module, an image processing and analysis module and a display module; the invention illuminates the surface of a GaAs etching product to be detected by the light source module, the high-precision camera module captures the surface image of the GaAs etching product, the image processing and analysis module accurately processes and analyzes the image by a specific algorithm, and the display module displays the processing and analysis information of the corresponding GaAs etching product, which can meet the high-precision and high-efficiency requirements of GaAs etching process detection, and can analyze and accurately divide all etching equipment and all etching defects generated, which is conducive to managers to reasonably make corresponding improvement measures, effectively ensure the etching effect of GaAs etching, and has a high level of intelligence and automation.

Description

A detection process and detection system for GaAs etching process

Technical Field

The invention relates to the technical field of etching process supervision, in particular to a detection process and a detection system for a GaAs etching process.

Background technique

In the process of semiconductor device manufacturing, GaAs etching process is one of the key process steps. Its etching quality directly determines the performance and stability of the device. The traditional GaAs etching process quality detection method is often low in precision, low in efficiency and complex in operation. It is also impossible to analyze and accurately divide all etching equipment and all etching defects generated, which is not conducive to managers to reasonably make corresponding improvement measures, and it is difficult to effectively ensure the etching effect of GaAs etching. The level of intelligence and automation is low;

In view of the above technical defects, a solution is now proposed.

Summary of the invention

The purpose of the present invention is to provide a detection process and a detection system for a GaAs etching process, which solves the problems that the prior art is difficult to meet the high-precision and high-efficiency requirements of GaAs etching process detection, and is unable to accurately divide all etching equipment and all etching defects generated, which is not conducive to managers to reasonably make corresponding improvement measures, and is difficult to effectively ensure the etching effect of GaAs etching, and has a low level of intelligence and automation.

To achieve the above object, the present invention provides the following technical solutions:

A detection system for a GaAs etching process comprises an intelligent control platform, a light source module, a high-precision camera module, an image processing and analysis module and a display module; the light source module is used to emit a stable and uniform light source to illuminate the surface of a GaAs etching product to be detected, the high-precision camera module captures the surface image of the GaAs etching product, and sends the captured surface image of the GaAs etching product to the image processing and analysis module via the intelligent control platform;

The image processing and analysis module receives the surface image of the GaAs etching product captured by the high-precision camera module, and accurately processes and analyzes the image through a specific algorithm, extracts the etching depth and etching morphology parameters, and sends the corresponding processing and analysis information to the display module via the intelligent control platform. The display module displays the processing and analysis information of the corresponding GaAs etching product.

Furthermore, the specific operation process of the image processing and analysis module includes:

Receiving the surface image of the GaAs etched product sent by the image acquisition module, preprocessing the surface image, including image filtering, contrast enhancement and noise suppression;

Use specific image processing algorithms to extract features from the preprocessed images, including edge detection, threshold segmentation, and morphological analysis, to identify the boundaries of the etched areas in the GaAs etched products in the images, and preliminarily determine the depth and morphological features of the etching;

The depth measurement algorithm is used to calculate the etching depth based on optical principles by calculating the grayscale difference or phase difference between different pixels in the image.

The morphology recognition algorithm is used to extract and compare the texture and shape features in the image to identify the morphology features of the etched area, including the width, depth distribution and surface roughness of the etched groove;

The extracted key parameters of etching depth and etching morphology are sorted and output, and sent to the display module in the form of numerical values or images for display.

Furthermore, the intelligent control platform is communicated with the etching defect detection module, and the image processing and analysis module sends corresponding processing and analysis information to the etching defect detection module via the intelligent control platform. The etching defect detection module determines the etching defects existing in the corresponding GaAs etching products based on the corresponding processing and analysis information, and marks the corresponding GaAs etching products as non-defective products, low-defective products or high-defective products through etching defect summary analysis, and the marking information of the corresponding GaAs etching products is sent to the display module via the intelligent control platform for display.

Furthermore, the specific analysis process of etching defect summary analysis is as follows:

If there is no etching defect in the corresponding GaAs etching product, the corresponding GaAs etching product is marked as a normal product; if there is an etching defect in the corresponding GaAs etching product, the etching defect type existing in the corresponding GaAs etching product is obtained, and each etching defect type is set in advance to correspond to a set of preset defect weight values, and the preset defect weight values of all existing etching defect types are summed up to calculate the etching defect detection value;

The etching defect detection value is numerically compared with the preset etching defect detection threshold. If the etching defect detection value exceeds the preset etching defect detection threshold, the corresponding GaAs etching product is marked as a high-abnormal product; if the etching defect detection value does not exceed the preset etching defect detection threshold, the corresponding GaAs etching product is marked as a low-abnormal product.

Furthermore, the intelligent control platform is connected in communication with the etching equipment evaluation module, and the etching equipment evaluation module obtains all etching equipment used for the GaAs etching process, marks the corresponding etching equipment as i, and i is a natural number greater than 1; sets an evaluation period, collects the number of non-abnormal products, the number of low-abnormal products, and the number of high-abnormal products produced by etching equipment i in the detection period, and marks them as non-abnormal detection value, low-abnormal detection value, and high-abnormal detection value, respectively;

The etching equipment evaluation value is obtained by numerically calculating the no-abnormality detection value, the low-abnormality detection value and the high-abnormality detection value, and the etching equipment evaluation value is numerically compared with the preset etching equipment evaluation threshold. If the etching equipment evaluation value exceeds the preset etching equipment evaluation threshold, the corresponding etching equipment i is marked as a strong-tube device; if the etching equipment evaluation value does not exceed the preset etching equipment evaluation threshold, the corresponding etching equipment i is marked as a weak-tube device, and the marking information of the corresponding etching equipment i is sent to the management terminal via the intelligent control platform.

Furthermore, the intelligent control platform is communicated with the etching defect classification module, and the etching defect classification module obtains all etching defect types that need to be monitored, and marks the corresponding etching defect type as a defect k to be analyzed, where k is a natural number greater than 1; through precise classification of etching defects, the defect k to be analyzed is marked as a key defect or a low-frequency defect, and the marking information of the corresponding defect k to be analyzed is sent to the management terminal via the intelligent control platform.

Furthermore, the specific analysis process of accurate division of etching defects is as follows:

The number of GaAs etching products processed by the corresponding etching equipment i in the evaluation period is obtained and marked as the total number of etchings, the number of products with the defect k to be analyzed in the GaAs etching products processed by the etching equipment i is marked as the defect matching value, and the defect matching value is calculated by ratio with the corresponding total number of etchings to obtain the defect number share value; the defect number share values of the corresponding defect k to be analyzed in all etching equipment are obtained, and the mean and variance calculations are performed on all defect number share values to obtain the defect number inspection value and the defect number wave value;

The defect number inspection value and the defect number wave value are numerically compared with the corresponding preset defect number inspection threshold value and the preset defect number wave threshold value respectively. If the defect number inspection value exceeds the preset defect number inspection threshold value and the defect number wave value does not exceed the preset defect number wave threshold value, the defect k to be analyzed is marked as a breakthrough defect; if both the defect number inspection value and the defect number wave value do not exceed the corresponding preset threshold value, the defect k to be analyzed is marked as a low-frequency defect; in other cases, defect push analysis is performed.

Furthermore, the specific analysis process of defect push analysis is as follows:

The defect number percentage of the defect k to be analyzed in the etching device i is numerically compared with the corresponding preset defect number percentage threshold. If the defect number percentage exceeds the preset defect number percentage threshold, the corresponding etching device i is marked as the target device of the defect k to be analyzed; the number of target devices corresponding to the defect k to be analyzed is obtained and marked as the target number inspection value, and the defect inspection evaluation value is obtained by numerically calculating the target number inspection value and the defect number inspection value. The defect inspection evaluation value is numerically compared with the preset defect inspection evaluation threshold. If the defect inspection evaluation value exceeds the preset defect inspection evaluation threshold, the defect k to be analyzed is marked as a key defect; if the defect inspection evaluation value does not exceed the preset defect inspection evaluation threshold, the defect k to be analyzed is marked as a low-frequency defect.

Further, the intelligent control platform sends the weak tube device to the etching defect classification module, and the etching defect classification module marks the number of low-frequency defects whose defect count exceeds the corresponding preset defect count threshold in the corresponding weak tube device as a low-frequency detection value, and marks the low-frequency defects whose defect count exceeds the corresponding preset defect count threshold in the corresponding weak tube device as a target defect, marks the defect count corresponding to the target defect in the corresponding weak tube device as a defect count excess value compared to the corresponding preset defect count threshold, calculates the average of all defect count excess values in the corresponding weak tube device to obtain a defect excess value, and marks the defect count excess value with the largest value as a defect excess amplitude value;

The etching tube evaluation value is obtained by numerically calculating the etching equipment evaluation value, low-frequency detection value, defect excess value and defect excess amplitude value of the corresponding weak tube equipment, and the etching tube evaluation value is numerically compared with the preset etching tube evaluation threshold. If the etching tube evaluation value exceeds the preset etching tube evaluation threshold, the corresponding weak tube equipment is marked as a concern device, and the concern device is sent to the management terminal via the intelligent control platform.

Furthermore, the present invention also proposes a detection process for a GaAs etching process, comprising the following steps:

Step 1: The light source module emits a stable and uniform light source to illuminate the surface of the GaAs etched product to be inspected;

Step 2: The high-precision camera module captures the surface image of the GaAs etched product, and sends the captured surface image of the GaAs etched product to the image processing and analysis module;

Step 3: The image processing and analysis module uses a specific algorithm to accurately process and analyze the surface image of the GaAs etched product and extract the etching depth and etching morphology parameters;

Step 4: The image processing and analysis module sends the corresponding processing and analysis information to the display module, and the display module displays the processing and analysis information of the corresponding GaAs etching product.

Compared with the prior art, the present invention has the following beneficial effects:

1. In the present invention, the surface of the GaAs etching product to be inspected is illuminated by the light source module, the high-precision camera module captures the surface image of the GaAs etching product, the image processing and analysis module accurately processes and analyzes the image through a specific algorithm, the display module displays the processing and analysis information of the corresponding GaAs etching product, and the quality of the corresponding GaAs etching product is graded by summarizing and analyzing the etching defects, which can meet the high-precision and high-efficiency requirements of GaAs etching process detection;

2. In the present invention, the etching processing performance of all etching equipment is analyzed through the etching equipment evaluation module to determine the strong tube equipment and the weak tube equipment, and all etching defect types are analyzed to determine the key defects or low-frequency defects, and it is judged whether the corresponding weak tube equipment is the equipment of concern. All etching equipment and all etching defects generated can be analyzed and accurately divided, which is conducive to the management personnel to reasonably make corresponding improvement measures, effectively ensure the etching effect of GaAs etching, and have a high level of intelligence and automation.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

FIG1 is a system block diagram of Embodiment 1 of the present invention;

FIG2 is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention;

FIG3 is a flow chart of a method according to Embodiment 4 of the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1: As shown in FIG1 , a detection system for a GaAs (gallium arsenide) etching process proposed by the present invention comprises an intelligent control platform, a light source module, a high-precision camera module, an image processing and analysis module, and a display module; the light source module is used to emit a stable and uniform light source to illuminate the surface of the GaAs etching product to be detected, the high-precision camera module captures the surface image of the GaAs etching product, and sends the captured surface image of the GaAs etching product to the image processing and analysis module via the intelligent control platform;

The image processing and analysis module receives the surface image of the GaAs etching product captured by the high-precision camera module, and accurately processes and analyzes the image through a specific algorithm, extracts the etching depth and etching morphology parameters, and sends the corresponding processing and analysis information to the display module through the intelligent control platform. The display module displays the processing and analysis information of the corresponding GaAs etching product; the specific operation process of the image processing and analysis module is as follows:

The surface image of the GaAs etched product sent by the image acquisition module is received. It should be noted that the surface image of the GaAs etched product is represented in the form of a pixel matrix, and each pixel contains information such as color and brightness. Since the image is affected by factors such as uneven illumination and noise interference, the image is preprocessed before further analysis to eliminate these unfavorable factors. The preprocessing steps may include image filtering, contrast enhancement, noise suppression, etc.;

The pre-processed image is subjected to feature extraction using specific image processing algorithms, including edge detection, threshold segmentation, and morphological analysis. Through these algorithms, the boundaries of the GaAs etching area in the image can be identified, and the etching depth and morphological features can be preliminarily determined;

In order to measure the etching depth more accurately, a depth measurement algorithm can be used, which is based on optical principles and infers the etching depth by calculating the grayscale difference or phase difference between different pixels in the image;

For the analysis of etching morphology, a morphology recognition algorithm can be used. This algorithm extracts and compares the texture and shape features in the image to identify the morphological features of the etching area, including the width, depth distribution and surface roughness of the etching groove. These morphological features are of great significance for evaluating the quality and stability of the etching process.

The extracted key parameters such as etching depth and etching morphology are sorted and output, and sent to the display module in the form of numerical values or images for display, so that operators can observe and judge the quality of the etching process. In summary, the image processing and analysis module plays a vital role in the GaAs etching process detection system. It accurately processes and analyzes the image through specific algorithms, extracts key etching depth and etching morphology parameters, and provides strong support for evaluating and optimizing the etching process.

Further, the intelligent control platform is connected to the etching defect detection module by communication, and the image processing and analysis module sends the corresponding processing and analysis information to the etching defect detection module via the intelligent control platform. The etching defect detection module determines the etching defects existing in the corresponding GaAs etching product based on the corresponding processing and analysis information, and marks the corresponding GaAs etching product as a non-defective product, a low-defective product or a high-defective product through etching defect summary analysis, and the marking information of the corresponding GaAs etching product is sent to the display module via the intelligent control platform for display, so that the inspection personnel can grasp the quality level of each GaAs etching product in detail, which is conducive to the classification output of GaAs etching products and reduces the difficulty of operation for the inspection personnel; the specific analysis process of the etching defect summary analysis is as follows:

If there is no etching defect in the corresponding GaAs etching product, the corresponding GaAs etching product is marked as a normal product; if there is an etching defect in the corresponding GaAs etching product, the etching defect type existing in the corresponding GaAs etching product is obtained, and each etching defect type is set in advance to correspond to a set of preset defect weight values, wherein the values of the preset defect weight values are all greater than zero, and the greater the adverse effect of the corresponding etching defect type on the quality of the GaAs etching product, the greater the value of the preset defect weight value adapted thereto;

The preset defect weight values of all etching defect types existing in the corresponding GaAs etching product are summed up to calculate the etching defect detection value; the etching defect detection value is numerically compared with the preset etching defect detection threshold; if the etching defect detection value exceeds the preset etching defect detection threshold, it indicates that the etching quality of the corresponding GaAs etching product is extremely poor, and the corresponding GaAs etching product is marked as a high-abnormality product; if the etching defect detection value does not exceed the preset etching defect detection threshold, it indicates that the etching quality of the corresponding GaAs etching product is poor, and the corresponding GaAs etching product is marked as a low-abnormality product.

Embodiment 2: As shown in FIG2 , the difference between this embodiment and embodiment 1 is that the intelligent control platform is connected in communication with the etching equipment evaluation module, the etching equipment evaluation module obtains all etching equipment used for the GaAs etching process, marks the corresponding etching equipment as i, and i is a natural number greater than 1; sets an evaluation period, preferably, the detection period is seven days; collects the number of non-differential products, the number of low-differential products and the number of high-differential products produced by the etching equipment i within the detection period and marks them as non-differential detection values, low-differential detection values and high-differential detection values respectively;

The no-abnormality detection value FRi, the low-abnormality detection value FKi and the high-abnormality detection value FLi are numerically calculated by the formula FYi=（a2*FKi+a3*FLi）/（a1*FRi+0.926）to obtain the etching equipment evaluation value FYi, wherein a1, a2 and a3 are preset proportional coefficients, and a3＞a2＞a1＞0; and the larger the value of the etching equipment evaluation value FYi, the worse the etching processing performance of the etching equipment i in the evaluation period;

The etching equipment evaluation value FYi is numerically compared with the preset etching equipment evaluation threshold. If the etching equipment evaluation value FYi exceeds the preset etching equipment evaluation threshold, it indicates that the etching processing performance of the etching equipment i during the evaluation period is poor, and the operation supervision of the etching equipment i needs to be strengthened, then the corresponding etching equipment i is marked as a strong-tube equipment; if the etching equipment evaluation value FYi does not exceed the preset etching equipment evaluation threshold, it indicates that the etching processing performance of the etching equipment i during the evaluation period is good, then the corresponding etching equipment i is marked as a weak-tube equipment, and the marking information of the corresponding etching equipment i is sent to the management terminal via the intelligent control platform, and the management personnel promptly strengthen the operation supervision of the strong-tube equipment, conduct cause investigation and analysis, and make reasonable improvement measures to ensure the etching effect of the corresponding strong-tube equipment.

Embodiment 3: As shown in FIG2, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the intelligent control platform is connected in communication with the etching defect classification module, and the etching defect classification module obtains all etching defect types that need to be monitored, and marks the corresponding etching defect type as a defect to be analyzed k, and k is a natural number greater than 1; through the precise classification of etching defects, the defect to be analyzed k is marked as a tough defect or a low-frequency defect, and the marking information of the corresponding defect to be analyzed k is sent to the management terminal via the intelligent control platform, which can accurately provide the management personnel with tough targets. When the management personnel receive the tough defect, they will promptly analyze the cause and focus on the tough defect, so as to solve the corresponding defects of the GaAs etching product as soon as possible, which is conducive to ensuring the etching effect of the GaAs etching product; the specific analysis process of precise classification of etching defects is as follows:

The number of GaAs etching products processed by the corresponding etching equipment i in the evaluation period is obtained and marked as the total number of etchings, the number of products with the defect k to be analyzed in the GaAs etching products processed by the etching equipment i is marked as the defect matching value, and the defect matching value is calculated by ratio with the corresponding total number of etchings to obtain the defect number share value; the defect number share values of the corresponding defect k to be analyzed in all etching equipment are obtained, and the mean and variance calculations are performed on all defect number share values to obtain the defect number inspection value and the defect number wave value;

The defect number inspection value and the defect number wave value are numerically compared with the corresponding preset defect number inspection threshold value and the preset defect number wave threshold value respectively. If the defect number inspection value exceeds the preset defect number inspection threshold value and the defect number wave value does not exceed the preset defect number wave threshold value, it indicates that the defect k to be analyzed is distributed in large quantities in the GaAs etching products processed by all etching equipment, and the defect k to be analyzed is marked as a key defect; if the defect number inspection value and the defect number wave value do not exceed the corresponding preset threshold value, it indicates that the defect k to be analyzed is distributed in small quantities in the GaAs etching products processed by all etching equipment, and the defect k to be analyzed is marked as a low-frequency defect;

In other cases, defect push analysis is performed, specifically: the defect number percentage value of the defect k to be analyzed in the etching device i is numerically compared with the corresponding preset defect number percentage threshold value. If the defect number percentage value exceeds the preset defect number percentage threshold value, it indicates that the defect k to be analyzed is distributed in large quantities in the GaAs etching products processed by the etching device i, and the corresponding etching device i is marked as the target device of the defect k to be analyzed; the number of target devices corresponding to the defect k to be analyzed is obtained and marked as the target number inspection value;

The defect inspection value QXk is obtained by numerically calculating the target number inspection value QPk and the defect number inspection value QSk through the formula QXk=kp1*QPk+kp2*QSk, wherein kp1 and kp2 are preset proportional coefficients, and the values of kp1 and kp2 are both positive numbers; and the larger the value of the defect inspection value QXk is, the higher the occurrence frequency of the defect k to be analyzed in the etching process of all etching equipment is relatively;

The defect inspection value QXk is numerically compared with the preset defect inspection threshold. If the defect inspection value QXk exceeds the preset defect inspection threshold, it indicates that the occurrence frequency of the defect k to be analyzed in the etching process of all etching equipment is relatively high, and the defect k to be analyzed is marked as a key defect; if the defect inspection value QXk does not exceed the preset defect inspection threshold, it indicates that the occurrence frequency of the defect k to be analyzed in the etching process of all etching equipment is relatively low, and the defect k to be analyzed is marked as a low-frequency defect.

Further, the intelligent control platform sends the weak tube device to the etching defect classification module, and the etching defect classification module marks the number of low-frequency defects whose defect count percentage exceeds the corresponding preset defect count percentage threshold in the corresponding weak tube device as a low-frequency detection value, and marks the low-frequency defects whose defect count percentage exceeds the corresponding preset defect count percentage threshold in the corresponding weak tube device as a target defect, marks the defect count percentage of the corresponding target defect in the corresponding weak tube device compared with the corresponding preset defect count percentage threshold as a defect count excess value, calculates the average of the defect count excess values of all target defects in the corresponding weak tube device to obtain a defect excess value, and marks the defect count excess value with the largest value as a defect excess amplitude value;

The etching equipment evaluation value FYi, low-frequency detection value TYi, defect excess value TSi and defect excess amplitude value TFi of the corresponding weak tube equipment are numerically calculated by the formula WXi=eq1*FYi+eq2*TYi+（eq3*TSi+eq4*TFi）/2 to obtain the etching tube evaluation value WXi, wherein eq1, eq2, eq3 and eq4 are preset proportional coefficients, and the values of eq1, eq2, eq3 and eq4 are all positive numbers; and the larger the value of the etching tube evaluation value WXi is, the more attention should be paid to the operation status of the corresponding weak tube equipment;

The etching tube evaluation value WXi is numerically compared with the preset etching tube evaluation threshold. If the etching tube evaluation value WXi exceeds the preset etching tube evaluation threshold, it indicates that it is necessary to pay special attention to the operating status of the corresponding weak tube equipment. The corresponding weak tube equipment is marked as a focus equipment, and the focus equipment is sent to the management terminal via the intelligent control platform. After receiving the focus equipment, the management personnel will focus on its etching processing performance in the future, and promptly inspect and repair the corresponding focus equipment to ensure its subsequent etching effect and realize targeted equipment management and control.

Embodiment 4: As shown in FIG3 , the difference between this embodiment and Embodiment 1, Embodiment 2, and Embodiment 3 is that a detection process of a GaAs etching process proposed by the present invention comprises the following steps:

Step 1: The light source module emits a stable and uniform light source to illuminate the surface of the GaAs etched product to be inspected;

Step 2: The high-precision camera module captures the surface image of the GaAs etched product, and sends the captured surface image of the GaAs etched product to the image processing and analysis module;

Step 3: The image processing and analysis module uses a specific algorithm to accurately process and analyze the surface image of the GaAs etched product and extract the etching depth and etching morphology parameters;

Step 4: The image processing and analysis module sends the corresponding processing and analysis information to the display module, and the display module displays the processing and analysis information of the corresponding GaAs etching product.

The working principle of the present invention is as follows: when in use, the surface of the GaAs etching product to be detected is illuminated by the light source module, the high-precision camera module captures the surface image of the GaAs etching product, the image processing and analysis module accurately processes and analyzes the image through a specific algorithm, the display module displays the processing and analysis information of the corresponding GaAs etching product, the etching defect detection module determines the etching defects existing in the corresponding GaAs etching product based on the corresponding processing and analysis information, and the corresponding GaAs etching product is quality graded by summarizing and analyzing the etching defects, which can meet the high-precision and high-efficiency requirements of GaAs etching process detection, and the etching processing performance of all etching equipment is analyzed by the etching equipment evaluation module to determine the strong tube equipment and the weak tube equipment, and all etching defect types are analyzed by the etching defect classification module to determine the key defects or low-frequency defects, and it is judged whether the corresponding weak tube equipment is the concerned equipment, which can analyze and accurately classify all etching equipment and all etching defects generated, which is conducive to the management personnel to reasonably make corresponding improvement measures, effectively ensure the etching effect of GaAs etching, and has a high level of intelligence and automation.

The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims (10)

1. A detection system for GaAs etching process, characterized in that it comprises an intelligent control platform, a light source module, a high-precision camera module, an image processing and analysis module and a display module; the light source module is used to emit a stable and uniform light source to illuminate the surface of the GaAs etching product to be detected, the high-precision camera module captures the surface image of the GaAs etching product, and sends the captured surface image of the GaAs etching product to the image processing and analysis module via the intelligent control platform; The image processing and analysis module receives the surface image of the GaAs etching product captured by the high-precision camera module, and accurately processes and analyzes the image through the depth measurement algorithm and the morphology recognition algorithm, extracts the etching depth and etching morphology parameters, and sends the corresponding processing and analysis information to the display module via the intelligent control platform. The display module displays the processing and analysis information of the corresponding GaAs etching product. 2. A GaAs etching process detection system according to claim 1, characterized in that the specific operation process of the image processing and analysis module includes: Receiving the surface image of the GaAs etching product sent by the image acquisition module, and preprocessing the surface image; Use specific image processing algorithms to extract features from the preprocessed images, including edge detection, threshold segmentation, and morphological analysis, to identify the boundaries of the etched areas in the GaAs etched products in the images, and preliminarily determine the depth and morphological features of the etching; The depth measurement algorithm is used to calculate the etching depth based on optical principles by calculating the grayscale difference or phase difference between different pixels in the image. The morphology recognition algorithm is used to extract and compare the texture and shape features in the image to identify the morphology features of the etching area; The extracted key parameters of etching depth and etching morphology are sorted and output, and sent to the display module in the form of numerical values or images for display. 3. A GaAs etching process detection system according to claim 2, characterized in that the intelligent control platform is communicatively connected to the etching defect detection module, the image processing and analysis module sends the corresponding processing and analysis information to the etching defect detection module via the intelligent control platform, the etching defect detection module determines the etching defects existing in the corresponding GaAs etching products based on the corresponding processing and analysis information, and marks the corresponding GaAs etching products as non-defective products, low-defective products or high-defective products through etching defect summary analysis, and the marking information of the corresponding GaAs etching products is sent to the display module via the intelligent control platform for display. 4. A detection system for GaAs etching process according to claim 3, characterized in that the specific analysis process of etching defect summary analysis is as follows: If there is no etching defect in the corresponding GaAs etching product, the corresponding GaAs etching product is marked as a normal product; if there is an etching defect in the corresponding GaAs etching product, the etching defect type existing in the corresponding GaAs etching product is obtained, and each etching defect type is set in advance to correspond to a set of preset defect weight values, and the preset defect weight values of all existing etching defect types are summed up to calculate the etching defect detection value; If the etching defect detection value exceeds the preset etching defect detection threshold, the corresponding GaAs etching product is marked as a high-abnormal product; if the etching defect detection value does not exceed the preset etching defect detection threshold, the corresponding GaAs etching product is marked as a low-abnormal product. 5. A detection system for a GaAs etching process according to claim 3, characterized in that the intelligent control platform is communicatively connected with the etching equipment evaluation module, the etching equipment evaluation module obtains all etching equipment used for the GaAs etching process, marks the corresponding etching equipment as i, and i is a natural number greater than 1; an evaluation period is set, and the number of non-abnormal products, the number of low-abnormal products and the number of high-abnormal products produced by the etching equipment i in the detection period are collected and marked as non-abnormal detection value, low-abnormal detection value and high-abnormal detection value respectively; The etching equipment evaluation value is obtained by numerically calculating the no-abnormality detection value, the low-abnormality detection value and the high-abnormality detection value. If the etching equipment evaluation value exceeds the preset etching equipment evaluation threshold, the corresponding etching equipment i is marked as a strong-tube device; if the etching equipment evaluation value does not exceed the preset etching equipment evaluation threshold, the corresponding etching equipment i is marked as a weak-tube device, and the marking information of the corresponding etching equipment i is sent to the management terminal via the intelligent control platform. 6. A GaAs etching process detection system according to claim 5, characterized in that the intelligent control platform is communicatively connected with the etching defect classification module, the etching defect classification module obtains all etching defect types that need to be monitored, and marks the corresponding etching defect type as a defect to be analyzed k, where k is a natural number greater than 1; the etching defects are accurately divided to mark the defect to be analyzed k as a key defect or a low-frequency defect, and the marking information of the corresponding defect to be analyzed k is sent to the management terminal via the intelligent control platform. 7. A detection system for GaAs etching process according to claim 6, characterized in that the specific analysis process of accurate classification of etching defects is as follows: The number of GaAs etching products processed by the corresponding etching equipment i in the evaluation period is obtained and marked as the total number of etchings, the number of products with the defect k to be analyzed in the GaAs etching products processed by the etching equipment i is marked as the defect matching value, and the defect matching value is calculated by ratio with the corresponding total number of etchings to obtain the defect number share value; the defect number share values of the corresponding defect k to be analyzed in all etching equipment are obtained, and the mean and variance calculations are performed on all defect number share values to obtain the defect number inspection value and the defect number wave value; If the defect number inspection value exceeds the preset defect number inspection threshold and the defect number wave value does not exceed the preset defect number wave threshold, the defect k to be analyzed is marked as a breakthrough defect; if both the defect number inspection value and the defect number wave value do not exceed the corresponding preset threshold, the defect k to be analyzed is marked as a low-frequency defect; in other cases, defect push analysis is performed. 8. A detection system for GaAs etching process according to claim 7, characterized in that the specific analysis process of defect push analysis is as follows: The defect number percentage of the defect k to be analyzed in the etching device i is numerically compared with the corresponding preset defect number percentage threshold. If the defect number percentage exceeds the preset defect number percentage threshold, the corresponding etching device i is marked as the target device of the defect k to be analyzed; the number of target devices corresponding to the defect k to be analyzed is obtained and marked as the target number inspection value, and the defect inspection evaluation value is obtained by numerically calculating the target number inspection value and the defect number inspection value. If the defect inspection evaluation value exceeds the preset defect inspection evaluation threshold, the defect k to be analyzed is marked as a key defect; if the defect inspection evaluation value does not exceed the preset defect inspection evaluation threshold, the defect k to be analyzed is marked as a low-frequency defect. 9. A detection system for a GaAs etching process according to claim 6, characterized in that the intelligent control platform sends the weak tube device to the etching defect classification module, the etching defect classification module marks the number of low-frequency defects whose defect number percentage value in the corresponding weak tube device exceeds the corresponding preset defect number percentage threshold as a low-frequency detection value, and marks the low-frequency defects whose defect number percentage value in the corresponding weak tube device exceeds the corresponding preset defect number percentage threshold as a target defect, marks the defect number percentage value corresponding to the target defect in the corresponding weak tube device compared to the corresponding preset defect number percentage threshold as a defect number excess value, calculates the mean of all defect number excess values in the corresponding weak tube device to obtain a defect excess value, and marks the defect number excess value with the largest value as a defect excess amplitude value; The etching tube evaluation value is obtained by numerically calculating the etching equipment evaluation value, low-frequency detection value, defect excess value and defect excess amplitude value of the corresponding weak tube equipment. If the etching tube evaluation value exceeds the preset etching tube evaluation threshold, the corresponding weak tube equipment will be marked as a focus device, and the focus device will be sent to the management terminal via the intelligent control platform. 10. A detection process for a GaAs etching process, characterized in that the detection process adopts the detection system for a GaAs etching process as claimed in any one of claims 1 to 9.