Defect detection device and detection method for semiconductor chip

Abstract

The invention relates to the technical field of semiconductors, and discloses a defect detection device and method for a semiconductor chip. The defect detection device for the semiconductor chip comprises an image acquisition module which is used for acquiring a high-definition image of the semiconductor chip and transmitting the high-definition image to a defect identification module; the defect identification module is used for receiving the high-definition image, identifying the position of a potential defect in the high-definition image by adopting an image identification technology and automatically marking in the high-definition image; according to the invention, by using the high-definition image acquisition module, a high-resolution chip image can be obtained, which provides conditions for identifying tiny or low-contrast defects. And the defect identification module automatically marks the defect position by using an image recognition technology, so that the accuracy and the automation level of detection are improved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular to a defect detection device and a detection method for a semiconductor chip. Background Art

[0002] In the semiconductor manufacturing industry, chip quality control is critical. Due to the complexity of the chip manufacturing process, defects are inevitable. To ensure the performance of the final product, defect detection of semiconductor chips is an essential step. This usually involves detailed area scanning and high-resolution image acquisition, followed by professional analysis software and hardware systems to identify and evaluate defects.

[0003] At present, typical semiconductor chip defect detection methods include using automated microscopes and image processing software to obtain chip images and identify defects in the images through algorithms. These methods can automatically identify defects to a certain extent, but they still have shortcomings in accuracy, speed, and defect rating and classification. Traditional image processing software may not be able to accurately distinguish different defect types, and when processing a large number of chips, it is inefficient, and misjudgments and missed judgments often occur.

[0004] The technical challenges faced by existing defect detection technologies are mainly low recognition accuracy and lack of intelligent classification and processing functions. The evaluation of defect types and severity largely depends on the operator's experience, and lacks accurate and fast-response automated systems to support it. In addition, how to automatically generate detailed inspection reports from the large amount of image data obtained, and not only indicate the defect location, but also include the specific type of defects and severity evaluation, is a considerable challenge for existing technologies. Therefore, a more advanced inspection device is needed to improve the automation, accuracy and efficiency of inspection. Summary of the invention

[0005] The present invention provides a semiconductor chip defect detection device and a detection method, which are used to solve the above-mentioned technical problems.

[0006] A first aspect of the present invention provides a semiconductor chip defect detection device, the semiconductor chip defect detection device comprising: An image acquisition module, the image acquisition module is used to acquire a high-definition image of the semiconductor chip and transmit the high-definition image to the defect identification module; A defect identification module, the defect identification module is used to receive the high-definition image, use image recognition technology to identify the location of potential defects in the high-definition image and automatically mark the high-definition image; A data comparison module, the data comparison module is used to compare the marked defect characteristics with a preset defect characteristic standard library to determine the defect type and severity on the semiconductor chip; A classification processing module, the classification processing module is used to classify the semiconductor chips according to the determined defect type and severity, and output corresponding classification results; wherein the classification results are divided into three categories: to be re-inspected, defective and qualified; A report generation module, the report generation module is used to generate a test report according to the classification result; wherein the test report includes the defect type, defect location, and defect severity assessment of the semiconductor chip.

[0007] Optionally, in a first implementation of the first aspect of the present invention, the defect identification module is specifically configured to: Analyzing image features in the high-definition image using a deep learning algorithm to identify abnormal image features, wherein the abnormal image features correspond to microscopic cracks, stains, and foreign matter deposits on the semiconductor chip; Automatically identify and read the mark or serial number on the semiconductor chip using optical character recognition technology to obtain the semiconductor chip identity information, and associate the abnormal image features with the semiconductor chip identity information to obtain the semiconductor chip defect identification information; The defect identification information of the semiconductor chip is marked on the high-definition image in a visual manner to generate a defect map.

[0008] Optionally, in a second implementation of the first aspect of the present invention, the data comparison module is specifically used to: Extracting defect feature information from the automatically annotated high-definition image; wherein the defect feature information includes the geometric size, shape, color feature and position information of the defect; Calling a preset defect feature standard library; wherein the preset defect feature standard library contains multiple types of known defect feature parameters; Performing a multi-dimensional comparative analysis on the extracted defect feature information and known defect feature parameters in a preset defect feature standard library to determine the defect type on the semiconductor chip; The severity of the defect is determined using threshold decision logic, and the severity of the defect is classified into different levels; wherein the levels include at least slight, medium or severe.

[0009] Optionally, in a third implementation of the first aspect of the present invention, the semiconductor chip defect detection device further includes: an encryption module, which is used to encrypt the generated test report to obtain an encrypted test report, and store the encrypted test report in an encryption database associated with the semiconductor chip defect detection device.

[0010] Optionally, in a fourth implementation of the first aspect of the present invention, the encryption module is specifically used to: Extracting defect detection data from the generated test report; wherein the defect detection data reflects the defect detection result of the semiconductor chip; performing a copy operation on the defect detection data to generate first data; Randomly selecting a preset encryption algorithm from a preset encryption algorithm library to perform an encryption operation on the first data to convert it into first encrypted data; Encoding the first encrypted data using a first-order coding model to convert it into first-order coded data; wherein the first-order coding model is used to encode the encrypted defect detection data; The first-order coded data is coded using a second-order coding model; wherein the second-order coding model is a progressive coding based on the first-order coding model, and is used to improve the redundancy and anti-interference capability of data transmission; According to a preset matching rule, different first-order encryption algorithms and second-order encryption algorithms are selected from the encryption algorithm library; wherein the preset matching rule defines a rule for matching a first-order encryption algorithm from the encryption algorithm library based on the first-order coded data and a rule for matching a second-order encryption algorithm from the encryption algorithm library based on the second-order coded data; A first-order encryption algorithm is applied to encrypt the first-order coded data to generate second encrypted data, a second-order encryption algorithm is applied to encrypt the second encrypted data to generate an encrypted test report, and the encrypted test report is stored in an encryption database associated with a defect detection device for a semiconductor chip.

[0011] A second aspect of the present invention provides a semiconductor chip defect detection method, the semiconductor chip defect detection method comprising: Acquire a high-definition image of the semiconductor chip and transmit the high-definition image to a defect identification module; Receiving the high-definition image, identifying the location of potential defects in the high-definition image using image recognition technology, and automatically marking the high-definition image; Comparing the marked defect features with a preset defect feature standard library to determine the defect type and severity on the semiconductor chip; Classifying the semiconductor chips according to the determined defect types and severity, and outputting corresponding classification results; wherein the classification results are divided into three categories: to be re-inspected, defective, and qualified; A test report is generated according to the classification result; wherein the test report includes the defect type, defect location, and defect severity assessment of the semiconductor chip.

[0012] Optionally, in a first implementation manner of the second aspect of the present invention, the semiconductor chip defect detection method further includes: encrypting the generated test report to obtain an encrypted test report, and storing the encrypted test report in an encrypted database associated with the semiconductor chip defect detection device.

[0013] Optionally, in a second implementation of the second aspect of the present invention, encrypting the generated test report to obtain the encrypted test report, and storing the encrypted test report in an encrypted database associated with the defect detection device of the semiconductor chip includes: Extracting defect detection data from the generated test report; wherein the defect detection data reflects the defect detection result of the semiconductor chip; performing a copy operation on the defect detection data to generate first data; Randomly selecting a preset encryption algorithm from a preset encryption algorithm library to perform an encryption operation on the first data to convert it into first encrypted data; Encoding the first encrypted data to convert it into first-order encoded data; Using a preset coding model to progressively encode the first-order coded data to obtain second-order coded data; According to a preset matching rule, different first-order encryption algorithms and second-order encryption algorithms are selected from the encryption algorithm library; wherein the preset matching rule defines a rule for matching a first-order encryption algorithm from the encryption algorithm library based on the first-order coded data and a rule for matching a second-order encryption algorithm from the encryption algorithm library based on the second-order coded data; A first-order encryption algorithm is applied to encrypt the first-order coded data to generate second encrypted data, a second-order encryption algorithm is applied to encrypt the second encrypted data to generate an encrypted test report, and the encrypted test report is stored in an encryption database associated with a defect detection device for a semiconductor chip.

[0014] Optionally, in a third implementation manner of the second aspect of the present invention, encoding the first encrypted data to convert it into first-order encoded data includes: Performing data conversion on the first encrypted data to obtain an initial encrypted character combination; Segmenting the initial encrypted character combination to obtain multiple encrypted character sets; Selecting multiple encryption characters at preset positions in the encryption character set to obtain an identification encryption character combination; Obtain a preset standard coding map; and scan the coding characters in the standard coding map, match the identification encryption character combination with the coding characters in the standard coding map, and if the match is successful, mark the successfully matched encryption character corresponding to the identification encryption character combination as the first coding character; The coded characters other than the first coded characters in the preset standard coding map are used as second coded characters, priority is constructed for the second coded characters to obtain frequency priorities of the second coded characters, and a coding sequence number is reallocated to each second coded character according to the frequency priorities of the second coded characters; The preset random number set is sorted in ascending order to obtain an ascending random number set, and after the second code character is reallocated to the code sequence number, the code sequence number identical to the random number is matched in sequence based on the random number in the ascending random number set, and the first code character corresponding to the matched code sequence number is sequentially added to the standard code map and placed before the second code character; Integrate all the coding characters and the newly allocated coding numbers, regenerate an optimized standard coding map, obtain a target coding map, and encode the first encrypted data according to the target coding map to convert it into first-order coding data. In the technical solution provided by the present invention, the beneficial effects are as follows: the present invention provides a defect detection device and a detection method for a semiconductor chip, wherein the defect detection device for a semiconductor chip comprises: an image acquisition module, wherein the image acquisition module is used to acquire a high-definition image of a semiconductor chip and transmit the high-definition image to a defect identification module; a defect identification module, wherein the defect identification module is used to receive the high-definition image, use image recognition technology to identify the location of potential defects in the high-definition image and automatically mark the high-definition image; the present invention can obtain a high-resolution chip image by using the high-definition image acquisition module, which provides conditions for identifying tiny or low-contrast defects. The defect identification module further uses image recognition technology to automatically mark the defect location, thereby improving the accuracy and automation level of detection. The data comparison module compares the defect features actually detected with the preset defect feature standard library, and can quickly and accurately identify the defect type and severity, thereby improving the detection efficiency. The classification processing module classifies the chips according to the detection results, which can not only reduce the influence of manual intervention and subjective judgment, but also quickly process large batches of chips and optimize the production process. The test results are divided into three categories: waiting for re-inspection, defective products, and qualified products. This is conducive to the effective allocation of resources in the subsequent process, such as focusing on more detailed inspections of chips to be re-inspected, thereby reducing the risk of defective products entering the market. The report generation module can generate a test report containing detailed information on defects based on the classification results, which is convenient for quality control personnel to conduct in-depth analysis and provide data support for improving the manufacturing process. Through automated and high-precision inspections, the device helps to improve product quality control of semiconductor chips during the manufacturing process, reduce the outflow of defective products, and improve the reliability of the final product and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1A schematic diagram of an embodiment of a semiconductor chip defect detection device according to an embodiment of the present invention; Figure 2 Schematic diagram of an embodiment of a semiconductor chip defect detection method in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] Embodiments of the present invention provide a defect detection device and a detection method for a semiconductor chip. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of a semiconductor chip defect detection device in an embodiment of the present invention includes: An image acquisition module, the image acquisition module is used to acquire a high-definition image of the semiconductor chip and transmit the high-definition image to the defect identification module; A defect identification module, the defect identification module is used to receive the high-definition image, use image recognition technology to identify the location of potential defects in the high-definition image and automatically mark the high-definition image; A data comparison module, the data comparison module is used to compare the marked defect characteristics with a preset defect characteristic standard library to determine the defect type and severity on the semiconductor chip; A classification processing module, the classification processing module is used to classify the semiconductor chips according to the determined defect type and severity, and output corresponding classification results; wherein the classification results are divided into three categories: to be re-inspected, defective and qualified; A report generation module, the report generation module is used to generate a test report according to the classification result; wherein the test report includes the defect type, defect location, and defect severity assessment of the semiconductor chip.

[0018] Specifically, in an embodiment of the present invention, a defect detection device for a semiconductor chip is used to automatically identify, classify and report defects on the chip. The following is a description of the functions and implementation process of each module in the embodiment: Image acquisition module Purpose: This module is responsible for capturing high-definition images of semiconductor chips. These images provide a detailed view of the chip surface for subsequent defect analysis.

[0019] Implementation process: Usually a high-resolution camera is used to shoot, and the chips are photographed one by one under controlled lighting conditions, and the image quality is ensured to meet the requirements of subsequent processing. After that, the image data will be transmitted to the defect identification module for analysis.

[0020] Defect Identification Module Function: Receive high-definition images transmitted by the image acquisition module, use image recognition technology to analyze these images to determine the location of defects, and automatically mark them in the images.

[0021] Implementation: Deep learning, pattern recognition or other image processing techniques are applied to identify possible defects in the image. This module may contain a pre-trained algorithm that can identify various defects such as cracks, scratches, stains on the chip and mark their specific locations on the image.

[0022] Data comparison module Function: Compare the defect characteristics marked by the defect identification module with a preset defect feature standard library to determine the defect type and severity on the chip.

[0023] Implementation process: Match defect features, such as size and shape, with various known defects in the standard library to classify the defects and assess their severity.

[0024] Classification processing module Purpose: Classify each analyzed chip based on the type and severity of the defect and give corresponding processing suggestions. These classifications may include waiting for re-inspection, defective and qualified.

[0025] Implementation process: Set thresholds or rules to determine classification criteria. For example, small defects can be classified as pending re-inspection for further analysis; medium defects are classified as defective products; and chips with no or only minor defects are considered qualified products.

[0026] Report generation module Function: Generate a detailed defect detection report based on the classification results.

[0027] Implementation process: The generated report may contain a detailed description of the location, type and severity of the defect and provide an easy-to-interpret file format such as PDF or a visual interface for operators or production management systems.

[0028] The entire process realizes automated quality control in the semiconductor chip production process, and closed-loop management from defect detection to classification and disposal. The automated detection and reporting process not only improves efficiency, reduces the cost and error of manual detection, but also increases the transparency and traceability of the production process.

[0029] In the embodiments of the present invention, the beneficial effects are: Automated identification and labeling: By using image recognition technology, the defect identification module can automatically identify the location of potential defects in high-definition images of semiconductor chips and automatically label them in the images, thereby reducing manual intervention and improving the efficiency and accuracy of defect identification.

[0030] Efficient data comparison: The data comparison module can compare the marked defect features with the preset defect feature standard library, so as to quickly and accurately determine the defect type and severity on the semiconductor chip, improving the efficiency and accuracy of defect detection.

[0031] Automatic classification and report generation: The classification processing module automatically classifies semiconductor chips according to the determined defect type and severity, and outputs the corresponding classification results. At the same time, the report generation module can generate a test report based on the classification results, including the defect type, location and severity assessment, thus realizing the automation of the defect detection process.

[0032] Improve production efficiency and quality control: Through the automated defect detection process, production efficiency can be improved, errors caused by human factors can be reduced, and defects can be quickly and accurately evaluated and classified, which helps to improve the quality control level of semiconductor chip production.

[0033] In summary, this semiconductor chip defect detection device can realize automated defect detection, classification and report generation, improve production efficiency and quality control level, and improve the efficiency and quality of semiconductor chip production.

[0034] Another embodiment of the semiconductor chip defect detection device in the embodiment of the present invention includes: The data comparison module is specifically used for: Extracting defect feature information from the automatically annotated high-definition image; wherein the defect feature information includes the geometric size, shape, color feature and position information of the defect; Calling a preset defect feature standard library; wherein the preset defect feature standard library contains multiple types of known defect feature parameters; Performing a multi-dimensional comparative analysis on the extracted defect feature information and known defect feature parameters in a preset defect feature standard library to determine the defect type on the semiconductor chip; The severity of the defect is determined using threshold decision logic, and the severity of the defect is classified into different levels; wherein the levels include at least slight, medium or severe.

[0035] Specifically, in the embodiment of the present invention, the data comparison module plays a vital role in the defect detection device of the semiconductor chip, and is responsible for accurately analyzing the characteristics of the defects and determining the level. The following is a detailed description of the implementation of the data comparison module function: Extract defect feature information Function: This module is responsible for extracting the feature information of defects from the automatically annotated high-definition chip images.

[0036] Specific implementation process: Use image processing techniques, such as edge detection, feature point matching, and region segmentation, to identify the defect areas marked in the image.

[0037] Extract the geometric size (such as length, width, area), shape (such as circle, crack, spot), color characteristics (such as brightness, hue, saturation) of each defect and its specific location information on the chip.

[0038] Calling the defect feature standard library Function: The data comparison module needs to access various known defect feature parameters contained in the defect feature standard library.

[0039] Specific implementation process: During initialization, a standard library containing characteristic parameters of various types of known defects is loaded or called. This may be a data set in the form of a database, which stores characteristic parameters of a large number of defect samples.

[0040] Conduct multi-dimensional comparative analysis Function: Determine the type of actual defect on the chip by performing a comprehensive comparative analysis with the contents of the feature standard library.

[0041] Specific implementation process: Use data analysis algorithms, such as pattern recognition, machine learning classifiers, etc. to match the extracted defect feature information with the parameters in the standard library.

[0042] Through multi-dimensional comparison, the similarity between the defect and the known defect types is determined.

[0043] The final defect type is determined based on the comparison results of various dimensions.

[0044] Defect grading using threshold decision logic Function: Classify the severity of defects according to their characteristic details.

[0045] Specific implementation process: Set threshold decision logic that classifies defects as minor, moderate, or severe based on the severity of defect characteristics (e.g., size, degree of color difference, etc.).

[0046] Expert systems, decision trees, or specialized algorithms may be applied to define thresholds and levels of severity.

[0047] Threshold decision logic is applied to each defect to classify the defect into the corresponding severity level.

[0048] In the embodiments of the present invention, the beneficial effects are: Refined defect feature extraction: The data comparison module extracts the geometric size, shape, color characteristics and location information of defects from high-definition images through image processing technology, achieving high-precision recognition and extraction of defect features, effectively improving the accuracy of detection results.

[0049] Multi-dimensional comparative analysis: The data comparison module can perform comparative analysis on multi-dimensional defect feature information and known defect feature parameters in the preset defect feature standard library, so as to determine the type of actual defects on the semiconductor chip and ensure the reliability and accuracy of the detection results.

[0050] Defect grading: By using threshold judgment logic, the data comparison module can classify defects into different levels according to their severity, such as minor, moderate or severe, which helps to determine the importance and impact of the defects and provides an important reference for subsequent processing.

[0051] Improve production efficiency and quality control: Refined defect identification and severity classification, as well as detailed inspection reports generated by the report generation module, can help production managers make quick decisions and take appropriate measures, thereby improving production efficiency and quality control.

[0052] Another embodiment of the semiconductor chip defect detection device in the embodiment of the present invention includes: The semiconductor chip defect detection device further includes: an encryption module, which is used to encrypt the generated test report to obtain the encrypted test report, and store the encrypted test report in an encryption database associated with the semiconductor chip defect detection device.

[0053] Another embodiment of the semiconductor chip defect detection device in the embodiment of the present invention includes: the encryption module is specifically used for: Extracting defect detection data from the generated test report; wherein the defect detection data reflects the defect detection result of the semiconductor chip; performing a copy operation on the defect detection data to generate first data; Randomly selecting a preset encryption algorithm from a preset encryption algorithm library to perform an encryption operation on the first data to convert it into first encrypted data; Encoding the first encrypted data using a first-order coding model to convert it into first-order coded data; wherein the first-order coding model is used to encode the encrypted defect detection data; The first-order coded data is coded using a second-order coding model; wherein the second-order coding model is a progressive coding based on the first-order coding model, and is used to improve the redundancy and anti-interference capability of data transmission; According to a preset matching rule, different first-order encryption algorithms and second-order encryption algorithms are selected from the encryption algorithm library; wherein the preset matching rule defines a rule for matching a first-order encryption algorithm from the encryption algorithm library based on the first-order coded data and a rule for matching a second-order encryption algorithm from the encryption algorithm library based on the second-order coded data; A first-order encryption algorithm is applied to encrypt the first-order coded data to generate second encrypted data, a second-order encryption algorithm is applied to encrypt the second encrypted data to generate an encrypted test report, and the encrypted test report is stored in an encryption database associated with a defect detection device for a semiconductor chip.

[0054] Specifically, in the embodiment of the present invention, the encryption module of the semiconductor chip defect detection device is responsible for protecting and encrypting the defect detection data in the detection report. The following are the implementation details of the encryption module function: Extracting defect detection data Specific role: Extract important defect detection data from the test report, which describes the defect detection results of semiconductor chips in detail, including defect type, location, size and other information.

[0055] Performing a copy operation Specific function: In order to perform the encryption operation, a copy operation is first performed on the defect detection data to generate a copy of the original data, which is called the first data.

[0056] Perform cryptographic operations Specific function: Select a preset encryption algorithm (such as AES, RSA, Blowfish, etc.) to perform an encryption operation on the first data, thereby increasing the security of the data. The data generated after the encryption operation is the first encrypted data.

[0057] First-order coding Specific role: The first-order encoding model is used to process the first encrypted data and encode it into first-order encoded data. First-order encoding adds an extra layer of protection to data encryption, which can be a basic encoding method such as Base64 or a more complex encoding technology.

[0058] Second-order coding Specific role: The second-order coding model is a further process based on the first-order coding model. Its purpose is to enhance the security of data transmission, improve redundancy and anti-interference capabilities, and ensure the integrity of data in an insecure network.

[0059] Select encryption algorithm based on matching rules Specific role: According to preset rules, based on the characteristics of first-order and second-order coded data, the appropriate first-order and second-order encryption algorithms are matched from the encryption algorithm library. For example, the most suitable encryption method is selected according to the data size, sensitivity, etc.

[0060] Apply encryption algorithm Specific functions: The first-order encryption algorithm is applied to further encrypt the first-order coded data to generate second encrypted data.

[0061] Then, the second encrypted data is finally encrypted using a second-order encryption algorithm to generate a final encryption detection report.

[0062] Storage encryption detection report What it does: Securely stores encrypted inspection reports in an encrypted database associated with the defect inspection device. This database is designed to store sensitive information and ensure that data remains secure even during transmission or storage.

[0063] Through the above steps, the encryption module ensures that the data generated from the defect detection device remains confidential and secure during processing, transmission and storage, making it difficult for the defect detection results to be obtained and tampered with by unauthorized personnel, thereby protecting the sensitive information of the enterprise and customers.

[0064] In the embodiments of the present invention, the beneficial effects are: Data security assurance: Through encryption operations and encoding processing, the encryption module ensures that the defect detection data extracted from the generated inspection report remains confidential and secure during processing, transmission and storage. This helps prevent sensitive information from being leaked by unauthorized access, thereby protecting the sensitive information of enterprises and customers.

[0065] Protection of sensitive information: The selection and application of encryption algorithms in the encryption algorithm library will be based on preset matching rules. The most appropriate encryption method can be selected based on the characteristics and sensitivity of the data, thereby protecting information security while ensuring the integrity and confidentiality of the data during transmission.

[0066] Improve data transmission security: By further encrypting the data using a second-order coding model, the redundancy and anti-interference ability of the data can be improved during the data transmission process, thereby improving the security and reliability of data transmission.

[0067] Compliance and Trust: Encrypted test reports are stored in an encrypted database designed to store sensitive information.

[0068] Another embodiment of the semiconductor chip defect detection device in the embodiment of the present invention includes: the encoding of the first encrypted data and converting it into first-order encoded data includes: Performing data conversion on the first encrypted data to obtain an initial encrypted character combination; Segmenting the initial encrypted character combination to obtain multiple encrypted character sets; Selecting multiple encryption characters at preset positions in the encryption character set to obtain an identification encryption character combination; Obtain a preset standard coding map; and scan the coding characters in the standard coding map, match the identification encryption character combination with the coding characters in the standard coding map, and if the match is successful, mark the successfully matched encryption character corresponding to the identification encryption character combination as the first coding character; The coded characters other than the first coded characters in the preset standard coding map are used as second coded characters, priority is constructed for the second coded characters to obtain frequency priorities of the second coded characters, and a coding sequence number is reallocated to each second coded character according to the frequency priorities of the second coded characters; The preset random number set is sorted in ascending order to obtain an ascending random number set, and after the second code character is reallocated to the code sequence number, the code sequence number identical to the random number is matched in sequence based on the random number in the ascending random number set, and the first code character corresponding to the matched code sequence number is sequentially added to the standard code map and placed before the second code character; Integrate all the coding characters and the newly allocated coding numbers, regenerate an optimized standard coding map, obtain a target coding map, and encode the first encrypted data according to the target coding map to convert it into first-order coding data. Specifically, in an embodiment of the present invention, another embodiment of a semiconductor chip defect detection device involves a process of encoding first encrypted data and converting it into first-order encoded data. Specifically, the following detailed steps are included: Data conversion Function: Perform preliminary data conversion on the first encrypted data to generate an initial encrypted character combination.

[0069] Implementation process: The first encrypted data is converted into binary or hexadecimal to obtain an initial encrypted character combination.

[0070] For example, the first encrypted data is converted into corresponding characters at every 8-bit binary number to generate an initial character sequence.

[0071] Character segmentation Function: Split the initial encrypted character combination to generate multiple encrypted character sets.

[0072] Implementation process: According to the preset segmentation rules, the initial encrypted character combination is segmented into several character sets, each set containing a fixed number of characters.

[0073] For example, the initial character sequence is divided into several character sets according to the rule of 4 characters per group.

[0074] Generation of encrypted character combinations Function: Select characters at specific positions from the encrypted character set to generate a combination of encrypted characters.

[0075] Implementation process: According to the preset position rules, characters at specific positions are selected from each character set to form a new combination.

[0076] For example, the encrypted character combination is identified from the first character and the last character of each character set.

[0077] Standard coding map acquisition and matching Function: Get the preset standard coding map, scan the coded characters in it, and match them with the identification encryption character combination.

[0078] Implementation process: A standard coding map is pre-stored, which includes a plurality of preset coding characters and their corresponding coding serial numbers.

[0079] The characters in the identification encryption character combination are matched one by one in the standard encoding map to find the corresponding encoding characters.

[0080] If the match is successful, the matched coded character is marked as the first coded character.

[0081] For example, the characters in the identification encryption character combination find the corresponding characters in the standard encoding map and record their encoding serial numbers.

[0082] The priority of the second coded character is constructed Function: Construct priority for the coded characters except the first coded character in the standard coding spectrum.

[0083] Implementation process: The remaining coded characters are sorted according to their frequency of occurrence to construct a priority table.

[0084] Reassign code numbers to each character based on priority, ensuring that high-frequency characters have lower code numbers.

[0085] For example, the remaining characters are sorted by statistical frequency, and the most frequent character is assigned the lowest encoding sequence number.

[0086] Random number set sorting and coded character integration Function: Sort the preset random number set and integrate the coded characters based on the sorted random number set.

[0087] Implementation process: The preset random number set is sorted in ascending order to obtain a sorted random number set.

[0088] According to the sorted random number set, characters with the same coding number are matched in sequence, added to the standard coding map, and ensured to be arranged before the second coding character.

[0089] For example, after the random number set is sorted, the characters of the corresponding serial numbers are inserted into the appropriate positions in the coding map.

[0090] Optimized standard coding map generation Function: Integrate all coding characters and reallocated coding numbers to generate an optimized standard coding map.

[0091] Implementation process: All first coded characters and second coded characters and their reallocated coded serial numbers are integrated to generate a new coding map.

[0092] The characters in the new atlas are arranged according to optimized coding sequences to ensure an efficient and reliable coding process.

[0093] For example, in the generated optimized coding map, characters are sorted according to the new coding sequence numbers to facilitate coding conversion.

[0094] Encoding conversion Function: According to the target coding map, the first encrypted data is converted into first-order coded data.

[0095] Implementation process: The generated target coding map is used to convert the first encrypted data into corresponding first-order coded data.

[0096] The encoding process maps and converts the characters and encoding numbers in the optimized graph to ensure the consistency and security of data encoding.

[0097] For example, the characters in the initial encrypted character combination are converted into corresponding first-order coding data through the target coding map.

[0098] Through the above detailed steps, the semiconductor chip defect detection device in the embodiment of the present invention can efficiently encode the first encrypted data and generate first-order encoded data. Each step is closely connected, and advanced encoding technology and data processing methods are used to ensure the security and reliability of the encoded data, which is suitable for data encryption and processing applications in semiconductor chip detection.

[0099] The above is a description of the semiconductor chip defect detection device according to the embodiment of the present invention. The following is a description of the semiconductor chip defect detection method according to the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a method for detecting a defect of a semiconductor chip includes: Step 101: Acquire a high-definition image of a semiconductor chip, and transmit the high-definition image to a defect identification module; Step 102: receiving the high-definition image, identifying the location of potential defects in the high-definition image using image recognition technology, and automatically marking the location of potential defects in the high-definition image; Step 103: Compare the marked defect features with a preset defect feature standard library to determine the defect type and severity on the semiconductor chip; Step 104: classifying the semiconductor chips according to the determined defect types and severity, and outputting corresponding classification results; wherein the classification results are divided into three categories: to be re-inspected, defective, and qualified; Step 105: Generate a test report based on the classification result; wherein the test report includes the defect type, defect location, and defect severity assessment of the semiconductor chip.

[0100] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0102] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A semiconductor chip defect detection device, It is characterized in that The semiconductor chip defect detection device comprises: An image acquisition module, the image acquisition module is used to acquire a high-definition image of the semiconductor chip and transmit the high-definition image to the defect identification module; A defect identification module, the defect identification module is used to receive the high-definition image, use image recognition technology to identify the location of potential defects in the high-definition image and automatically mark the high-definition image; A data comparison module, the data comparison module is used to compare the marked defect characteristics with a preset defect characteristic standard library to determine the defect type and severity on the semiconductor chip; A classification processing module, the classification processing module is used to classify the semiconductor chips according to the determined defect type and severity, and output corresponding classification results; wherein the classification results are divided into three categories: to be re-inspected, defective and qualified; A report generation module, the report generation module is used to generate a test report according to the classification result; wherein the test report includes the defect type, defect location, and defect severity assessment of the semiconductor chip.

2. The semiconductor chip defect detection device according to claim 1, It is characterized in that The defect identification module is specifically used for: Analyzing image features in the high-definition image using a deep learning algorithm to identify abnormal image features, wherein the abnormal image features correspond to microscopic cracks, stains, and foreign matter deposits on the semiconductor chip; Automatically identify and read the mark or serial number on the semiconductor chip using optical character recognition technology to obtain the semiconductor chip identity information, and associate the abnormal image features with the semiconductor chip identity information to obtain the semiconductor chip defect identification information; The defect identification information of the semiconductor chip is marked on the high-definition image in a visual manner to generate a defect map.

3. The semiconductor chip defect detection device according to claim 1, It is characterized in that The data comparison module is specifically used for: Extracting defect feature information from the automatically annotated high-definition image; wherein the defect feature information includes the geometric size, shape, color feature and position information of the defect; Calling a preset defect feature standard library; wherein the preset defect feature standard library contains multiple types of known defect feature parameters; Performing a multi-dimensional comparative analysis on the extracted defect feature information and known defect feature parameters in a preset defect feature standard library to determine the defect type on the semiconductor chip; The severity of the defect is determined using threshold decision logic, and the severity of the defect is classified into different levels; wherein the levels include at least slight, medium or severe.

4. The semiconductor chip defect detection device according to claim 1, It is characterized in that The semiconductor chip defect detection device further includes: an encryption module, which is used to encrypt the generated test report to obtain the encrypted test report, and store the encrypted test report in an encryption database associated with the semiconductor chip defect detection device.

5. The semiconductor chip defect detection device according to claim 4, It is characterized in that The encryption module is specifically used for: Extracting defect detection data from the generated test report; wherein the defect detection data reflects the defect detection result of the semiconductor chip; performing a copy operation on the defect detection data to generate first data; Randomly selecting a preset encryption algorithm from a preset encryption algorithm library to perform an encryption operation on the first data to convert it into first encrypted data; Encoding the first encrypted data to convert it into first-order encoded data; Using a preset coding model to progressively encode the first-order coded data to obtain second-order coded data; According to a preset matching rule, different first-order encryption algorithms and second-order encryption algorithms are selected from the encryption algorithm library; wherein the preset matching rule defines a rule for matching a first-order encryption algorithm from the encryption algorithm library based on the first-order coded data and a rule for matching a second-order encryption algorithm from the encryption algorithm library based on the second-order coded data; A first-order encryption algorithm is applied to encrypt the first-order coded data to generate second encrypted data, a second-order encryption algorithm is applied to encrypt the second encrypted data to generate an encrypted test report, and the encrypted test report is stored in an encryption database associated with a defect detection device for a semiconductor chip.

6. The semiconductor chip defect detection device according to claim 5, It is characterized in that The encoding of the first encrypted data to convert it into first-order encoded data includes: Performing data conversion on the first encrypted data to obtain an initial encrypted character combination; Segmenting the initial encrypted character combination to obtain multiple encrypted character sets; Selecting multiple encryption characters at preset positions in the encryption character set to obtain an identification encryption character combination; Obtain a preset standard coding map; and scan the coding characters in the standard coding map, match the identification encryption character combination with the coding characters in the standard coding map, and if the match is successful, mark the successfully matched encryption character corresponding to the identification encryption character combination as the first coding character; The coded characters other than the first coded characters in the preset standard coding map are used as second coded characters, priority is constructed for the second coded characters to obtain frequency priorities of the second coded characters, and a coding sequence number is reallocated to each second coded character according to the frequency priorities of the second coded characters; The preset random number set is sorted in ascending order to obtain an ascending random number set, and after the second code character is reallocated to the code sequence number, the code sequence number identical to the random number is matched in sequence based on the random number in the ascending random number set, and the first code character corresponding to the matched code sequence number is sequentially added to the standard code map and placed before the second code character; Integrate all the coding characters and the newly allocated coding numbers, regenerate an optimized standard coding map, obtain a target coding map, and encode the first encrypted data according to the target coding map to convert it into first-order coding data.

7. A method for detecting defects in semiconductor chips, It is characterized in that The semiconductor chip defect detection method comprises: Acquire a high-definition image of the semiconductor chip and transmit the high-definition image to a defect identification module; Receiving the high-definition image, identifying the location of potential defects in the high-definition image using image recognition technology, and automatically marking the high-definition image; Comparing the marked defect features with a preset defect feature standard library to determine the defect type and severity on the semiconductor chip; Classifying the semiconductor chips according to the determined defect types and severity, and outputting corresponding classification results; wherein the classification results are divided into three categories: to be re-inspected, defective, and qualified; A test report is generated according to the classification result; wherein the test report includes the defect type, defect location, and defect severity assessment of the semiconductor chip.

8. The semiconductor chip defect detection method according to claim 7, It is characterized in that The semiconductor chip defect detection method further includes: encrypting the generated detection report to obtain the encrypted detection report, and storing the encrypted detection report in an encryption database associated with the semiconductor chip defect detection device.

9. The semiconductor chip defect detection method according to claim 8, It is characterized in that The step of encrypting the generated test report to obtain the encrypted test report and storing the encrypted test report in an encrypted database associated with the defect detection device of the semiconductor chip includes: Extracting defect detection data from the generated test report; wherein the defect detection data reflects the defect detection result of the semiconductor chip; performing a copy operation on the defect detection data to generate first data; Randomly selecting a preset encryption algorithm from a preset encryption algorithm library to perform an encryption operation on the first data to convert it into first encrypted data; Encoding the first encrypted data to convert it into first-order encoded data; Using a preset coding model to progressively encode the first-order coded data to obtain second-order coded data; According to a preset matching rule, different first-order encryption algorithms and second-order encryption algorithms are selected from the encryption algorithm library; wherein the preset matching rule defines a rule for matching a first-order encryption algorithm from the encryption algorithm library based on the first-order coded data and a rule for matching a second-order encryption algorithm from the encryption algorithm library based on the second-order coded data; A first-order encryption algorithm is applied to encrypt the first-order coded data to generate second encrypted data, a second-order encryption algorithm is applied to encrypt the second encrypted data to generate an encrypted test report, and the encrypted test report is stored in an encryption database associated with a defect detection device for a semiconductor chip.

10. The semiconductor chip defect detection method according to claim 9, It is characterized in that The encoding of the first encrypted data to convert it into first-order encoded data includes: Performing data conversion on the first encrypted data to obtain an initial encrypted character combination; Segmenting the initial encrypted character combination to obtain multiple encrypted character sets; Selecting multiple encryption characters at preset positions in the encryption character set to obtain an identification encryption character combination; Obtain a preset standard coding map; and scan the coding characters in the standard coding map, match the identification encryption character combination with the coding characters in the standard coding map, and if the match is successful, mark the successfully matched encryption character corresponding to the identification encryption character combination as the first coding character; The coded characters other than the first coded characters in the preset standard coding map are used as second coded characters, priority is constructed for the second coded characters to obtain frequency priorities of the second coded characters, and a coding sequence number is reallocated to each second coded character according to the frequency priorities of the second coded characters; The preset random number set is sorted in ascending order to obtain an ascending random number set, and after the second code character is reallocated to the code sequence number, the code sequence number identical to the random number is matched in sequence based on the random number in the ascending random number set, and the first code character corresponding to the matched code sequence number is sequentially added to the standard code map and placed before the second code character; Integrate all the coding characters and the newly allocated coding numbers, regenerate an optimized standard coding map, obtain a target coding map, and encode the first encrypted data according to the target coding map to convert it into first-order coding data.

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