Chip defect detection method and system

Abstract

The invention relates to the technical field of chip detection, and discloses a chip defect detection method and system, and the method comprises the steps: obtaining chip manufacturing process parameters and chip function test parameters; calculating target data of each chip according to the chip manufacturing process parameters and pre-acquired chip manufacturing process parameters to obtain sample process deviation data; performing a first analysis operation to determine a process deviation characteristic; performing calculation operation according to the chip function test parameters to obtain chip function test deviation; performing second analysis operation to obtain function deviation characteristics; performing update calculation on a preset initial weight to obtain a process feature weight and a function feature weight; performing comprehensive analysis operation to obtain a comprehensive evaluation value; and judging the comprehensive evaluation value based on a preset first score and a preset second score to obtain an operation state detection result of the chip. According to the method, accurate and timely performance detection of the chip can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of chip detection, and particularly to a method and system for defect detection of chips. Background Art

[0002] At present, the manufacture of integrated circuit chips involves multiple complex technological processes, including photolithography, etching, ion implantation, etc. Any defect in the processing quality of these technological processes may lead to the failure of chip functions. Therefore, after the chips are manufactured, it is necessary to perform defect detection on the chips to ensure the performance and quality of the final products.

[0003] In the existing technology, mainly two means, namely failure analysis and yield test, are relied on. Failure analysis is to perform microscopic analysis on the chips through a microscope or an electron microscope to find out the specific technological processes that affect the chip functions. The yield test is to evaluate the qualified rate of the chips to detect the compliance of the entire manufacturing process.

[0004] Moreover, the manufacture of IC chips is becoming more and more complex, often requiring twenty or thirty different materials, and the feature size is constantly decreasing. Due to the natural differences between wafers and between chips, a large number of process parameter fluctuations often occur during the production control process. These fluctuations may come from cleaning machines, coating machines, lithography machines, etching plates, exposure plates, etc. These fluctuations may lead to the failure of component functions. These function failures may be obvious or weak, and they may cause delays or numerical offsets. However, in the existing technology, only logical test data is relied on for testing, and the detection accuracy is not high, which easily leads to the situation that the defects of the chips are not discovered in time and the product performance is unqualified. Summary of the Invention

[0005] The present invention provides a method and system for defect detection of chips to achieve accurate and timely performance detection of chips during the chip manufacturing process.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a method for defect detection of chips, including: Obtaining chip manufacturing process parameters and chip function test parameters; Calculating, according to the chip manufacturing process parameters and the target data of the chip manufacturing process parameters obtained in advance for each chip, the sample process deviation data of the chip manufacturing process parameters for each chip; Performing a first analysis operation according to the sample process deviation data to determine process deviation characteristics, wherein the process deviation characteristics include the maximum positive process deviation, the maximum negative process deviation, and the absolute process deviation; Performing a calculation operation according to the chip function test parameters to obtain a chip function test deviation; Perform a second analysis operation based on the chip function test deviation to obtain function deviation characteristics, where the function deviation characteristics include the maximum positive function deviation, the maximum negative function deviation, and the absolute function deviation; Update and calculate the preset initial weights according to the deviation characteristic correlation coefficient to obtain process characteristic weights and function characteristic weights; Perform a comprehensive analysis operation based on the process deviation characteristics, the function deviation characteristics, the process characteristic weights, and the function characteristic weights to obtain a comprehensive evaluation value; Judge the comprehensive evaluation value based on a preset first score and a preset second score to obtain the chip operation status detection result. In an alternative embodiment, calculate based on the chip manufacturing process parameters and the target data of the chip manufacturing process parameters obtained in advance for each chip to obtain the sample process deviation data of the chip manufacturing process parameters for each chip, including: Perform a subtraction calculation on the chip manufacturing process parameters and the target data of the chip manufacturing process parameters obtained in advance for each chip to obtain the sample process deviation data of the chip manufacturing process parameters for each chip; Among them, the calculation formula for the sample process deviation data is: Among them, is the sample process deviation data, representing the i-th value of the deviation of a certain chip in a certain process parameter, is the i-th value of the chip manufacturing process parameter, is the i-th value of the target data of the chip manufacturing process parameters for each chip.

[0007] Preferably, perform a first analysis operation based on the sample process deviation data to determine process deviation characteristics, including: Select the maximum value of the sample process deviation data as the maximum positive process deviation, and select the minimum value of the sample process deviation data as the maximum negative process deviation; Perform a calculation operation based on the maximum positive process deviation, the minimum negative process deviation, and the absolute process deviation to obtain process deviation characteristics; Among them, the absolute process deviation is obtained through the following calculation: Among them, is the absolute process deviation, is the sample process deviation data, representing the -th value of the deviation of a certain chip in a certain process parameter, represents the total number of the sample process deviation data; Among them, the calculation formula for the process deviation characteristics is: Among them, is the process deviation feature, is the absolute process deviation, is the maximum positive process deviation, is the minimum negative process deviation, , and are the preset initial weights of the process deviation feature.

[0008] Preferably, a calculation operation is performed according to the chip function test parameters to obtain a chip function test deviation, including: Among them, the chip function test deviation is obtained through the following formula: Among them, is the chip function test deviation, representing the i-th value of the deviation of a certain chip in function parameters, is the i-th value of the chip function test parameters; represents a logical function for converting the i-th value of the chip function test parameters into the calculation result of the chip function test deviation; is the logical function weight, is the logical function bias.

[0009] Preferably, a second analysis operation is performed according to the chip function test deviation to obtain a function deviation feature, including: Select the maximum value of the function test deviation as the maximum positive function deviation, and select the minimum value of the function test deviation as the minimum negative function deviation; A calculation operation is performed according to the maximum positive function deviation, the minimum negative function deviation and the absolute function deviation to obtain a function deviation feature; Among them, the absolute function deviation is obtained through the following calculation: Among them, is the absolute function deviation, is the chip function test result, representing the i-th value of the function test deviation of a certain chip, represents the total number of function test deviations; Among them, the calculation formula of the function deviation feature is: Among them, is the function deviation feature, is the absolute function deviation, is the maximum positive function deviation, is the minimum negative functional deviation, , and are the preset initial weights of the functional deviation characteristics.

[0010] Preferably, the preset initial weights are updated and calculated according to the deviation characteristic correlation coefficient to obtain the process characteristic weight and the functional characteristic weight, including: Calculate the deviation characteristic correlation coefficient between the process deviation characteristic and the functional deviation characteristic; Calculate the initial weight of the process deviation characteristic according to the deviation characteristic correlation coefficient to obtain the process characteristic weight; Calculate the initial weight of the functional deviation characteristic according to the deviation characteristic correlation coefficient to obtain the functional characteristic weight; Among them, the process characteristic weight is calculated by the following formula: Among them, is the process characteristic weight corresponding to the process deviation characteristic, is the preset initial weight of the process deviation characteristic, represents the total number of sample process deviation data, represents the deviation characteristic correlation coefficient; Among them, the functional characteristic weight is calculated by the following formula: Among them, is the functional characteristic weight corresponding to the functional deviation characteristic, is the preset initial weight of the functional deviation characteristic, represents the total number of functional test deviations, represents the deviation characteristic correlation coefficient.

[0011] Preferably, a comprehensive analysis operation is performed according to the process deviation characteristic, the functional deviation characteristic, the process characteristic weight and the functional characteristic weight to obtain a comprehensive evaluation value, including: Calculate according to the process deviation characteristic and the process characteristic weight to obtain a process evaluation value; Calculate according to the functional deviation characteristic and the functional characteristic weight to obtain a functional evaluation value; Calculate according to the process evaluation value and the functional evaluation value to obtain a comprehensive evaluation value; Among them, the process evaluation value is calculated by the following formula: Among them, is the process evaluation value, is the absolute process deviation, is the maximum positive process deviation, is the minimum negative process deviation, , and are the weights of the process characteristics; Among them, the function evaluation value is calculated by the following formula: Among them, is the function evaluation value, is the absolute function deviation, is the maximum positive function deviation, is the minimum negative function deviation, , and are the preset initial weights of the function deviation characteristics; Among them, the formula for calculating the comprehensive evaluation value is as follows: Among them, is the comprehensive evaluation value, is the weight of the process characteristics, is the weight of the function characteristics, is the process evaluation value, is the function evaluation value.

[0012] Preferably, the comprehensive evaluation value is judged according to the preset first score and the preset second score to obtain the operation state detection result of the chip, including: When the comprehensive evaluation value y≠0 and y<a1, output the operation state detection result that the chip operation state is good; When the comprehensive evaluation value y≠0 and a1≤y<a2, output the operation state detection result that the chip operation state is average; When the comprehensive evaluation value y≠0 and a1≤y≤1, output the operation state detection result that the chip operation state is poor; When the comprehensive evaluation value y = 0, output the operation state detection result that the chip does not meet the minimum requirements; where a1 and a2 are the first score and the second score respectively, and satisfy 0<a1<a2<1.

[0013] In a second aspect, the present invention provides a chip defect detection device, including: A data acquisition module, which acquires chip manufacturing process parameters and chip function test parameters; A process deviation module calculates the target data of each chip based on the chip manufacturing process parameters and the pre-acquired chip manufacturing process parameters, and obtains the sample process deviation data of the chip manufacturing process parameters for each chip; A process feature module performs a first analysis operation based on the sample process deviation data to determine process deviation features, where the process deviation features include the maximum positive process deviation, the maximum negative process deviation, and the absolute process deviation; A function deviation module performs a calculation operation based on the chip function test parameters to obtain the chip function test deviation; A function feature module performs a second analysis operation based on the chip function test deviation to obtain function deviation features, where the function deviation features include the maximum positive function deviation, the maximum negative function deviation, and the absolute function deviation; A weight update module updates and calculates the preset initial weights according to the deviation feature correlation coefficients to obtain the process feature weights and the function feature weights; A comprehensive evaluation module performs a comprehensive analysis operation based on the process deviation features, the function deviation features, the process feature weights, and the function feature weights to obtain a comprehensive evaluation value; A result output module judges the comprehensive evaluation value based on a preset first score and a preset second score to obtain the operation state detection result of the chip.

[0014] In a third aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute any one of the above-mentioned defect detection methods for a chip.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for detecting defects of a chip, which includes obtaining chip manufacturing process parameters and chip function test parameters; calculating, according to the chip manufacturing process parameters and the chip manufacturing process parameters obtained in advance, the target data of each chip to obtain the sample process deviation data of the chip manufacturing process parameters in each chip; performing a first analysis operation according to the sample process deviation data to determine process deviation characteristics, where the process deviation characteristics include the maximum positive process deviation, the maximum negative process deviation, and the absolute process deviation; performing a calculation operation according to the chip function test parameters to obtain a chip function test deviation; performing a second analysis operation according to the chip function test deviation to obtain function deviation characteristics, where the function deviation characteristics include the maximum positive function deviation, the maximum negative function deviation, and the absolute function deviation; updating and calculating a preset initial weight according to the deviation characteristic correlation coefficient to obtain a process characteristic weight and a function characteristic weight; performing a comprehensive analysis operation according to the process deviation characteristics, the function deviation characteristics, the process characteristic weight, and the function characteristic weight to obtain a comprehensive evaluation value; and judging the comprehensive evaluation value based on a preset first score and a preset second score to obtain a detection result of the operating state of the chip. By comprehensively analyzing the timely process monitoring and function test data of the chip, the present invention can achieve accurate and timely performance detection of the chip, which is beneficial to improving the chip manufacturing quality and reducing the production cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic flowchart of a method for detecting defects of a chip provided by the first embodiment of the present invention; Figure 2 is a schematic structural diagram of a device for detecting defects of a chip provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] Referring to Figure 1 , the first embodiment of the present invention provides a method for detecting defects of a chip, including the following steps: S11, obtaining chip manufacturing process parameters and chip function test parameters; S12, calculating, according to the chip manufacturing process parameters and the chip manufacturing process parameters obtained in advance, the target data of each chip to obtain the sample process deviation data of the chip manufacturing process parameters in each chip; S13. Perform a first analysis operation based on the sample process deviation data to determine process deviation characteristics, where the process deviation characteristics include the maximum positive process deviation, the maximum negative process deviation, and the absolute process deviation; S14. Perform a calculation operation based on the chip function test parameters to obtain the chip function test deviation; S15. Perform a second analysis operation based on the chip function test deviation to obtain function deviation characteristics, where the function deviation characteristics include the maximum positive function deviation, the maximum negative function deviation, and the absolute function deviation; S16. Update and calculate the preset initial weights according to the deviation characteristic correlation coefficients to obtain the process characteristic weights and the function characteristic weights; S17. Perform a comprehensive analysis operation based on the process deviation characteristics, the function deviation characteristics, the process characteristic weights, and the function characteristic weights to obtain a comprehensive evaluation value; S18. Judge the comprehensive evaluation value based on the preset first score and the preset second score to obtain the detection result of the chip's operating state.

[0019] In step S11, it is necessary to obtain the chip manufacturing process parameters and the chip function test parameters.

[0020] It should be noted that the chip manufacturing process parameters refer to the parameters corresponding to various process links involved in the chip manufacturing process. For example, in the lithography process of chip manufacturing, parameters such as the thickness of the photoresist, the exposure time, and the development time belong to the chip manufacturing process parameters; in the etching process, the flow rate of the etching gas, the etching rate, and the etching depth are also part of them. The chip function test parameters are the relevant parameters involved in the function test of the chip after the chip manufacturing is completed, such as the power consumption test parameters of the chip, that is, the relevant data of the electrical energy consumed by the chip in the normal working state; the signal transmission delay parameters of the chip, which refer to the time data consumed by the signal transmission between different modules inside the chip.

[0021] For the acquisition of chip manufacturing process parameters, in each process link of chip manufacturing, real-time monitoring and recording are carried out through special measuring instruments. Taking the ion implantation process of chips as an example, the ion implanter itself is equipped with high-precision sensors, which can measure parameters such as the dose and energy of implanted ions. These measurement data will be stored in a database connected to the manufacturing equipment for subsequent extraction and use. During the wafer manufacturing process, after each process is completed, relevant parameters of the wafer will be detected, such as the flatness and resistivity of the wafer. These data together constitute the chip manufacturing process parameters. The acquisition of chip function test parameters is obtained by using special chip test equipment after the chip is packaged. For example, an automatic test equipment (ATE) is used to comprehensively detect the various functions of the chip. When detecting the computing function of the chip, the ATE will input a series of specific test data to the chip, and then record data such as the results output by the chip and the time consumed to complete the operation. These data become part of the chip function test parameters.

[0022] In step S12, it is necessary to calculate the target data of each chip according to the chip manufacturing process parameters and the previously obtained chip manufacturing process parameters, and obtain the sample process deviation data of the chip manufacturing process parameters for each chip, including: Performing subtraction calculation on the chip manufacturing process parameters and the target data of the previously obtained chip manufacturing process parameters for each chip to obtain the sample process deviation data of the chip manufacturing process parameters for each chip; Among them, the calculation formula for the sample process deviation data is: Among them, is the sample process deviation data, representing the i-th value of the deviation of a certain chip in a certain process parameter, is the i-th value of the chip manufacturing process parameters, is the i-th value of the target data of the chip manufacturing process parameters for each chip.

[0023] Specifically, the target data of the chip is obtained through standardized tests or predefined target values. The target data of the chip is the expected value of the ideal chip performance and represents the indicators that the chip should achieve under normal production conditions. The sample process deviation data represents the difference between a certain specific chip and the target data in a certain process parameter. It reflects the degree of deviation of the chip from the ideal state during the manufacturing process and can be a positive deviation or a negative deviation.

[0024] For example, in the ion implantation process of chips, the dose of ion implantation is a key process parameter. Suppose the target value of the ion implantation dose in a specific area of a certain model of chip preset in advance is 1.5×10¹5 atoms / cm², while in the actual manufacturing process, the ion implantation dose of this area measured for a certain chip is 1.4×10¹ 5 atoms / cm². Then, according to the formula calculation, the sample process deviation data of this chip on this process parameter is - 1×10¹ 4 atoms / cm². Again, in the etching process of the chip, the target value of the etching depth is set to 500nm, and the actual measured etching depth of a certain chip is 510nm. At this time, the sample process deviation data is 10nm.

[0025] In step S13, it is necessary to perform a first analysis operation according to the sample process deviation data to determine the process deviation characteristics, including: Select the maximum value of the sample process deviation data as the maximum positive process deviation, and select the minimum value of the sample process deviation data as the maximum negative process deviation; Perform a calculation operation according to the maximum positive process deviation, the minimum negative process deviation and the absolute process deviation to obtain the process deviation characteristics; Among them, the absolute process deviation is obtained through the following calculation: Among them, is the absolute process deviation, is the sample process deviation data, representing the th value of the deviation of a certain chip on a certain process parameter, represents the total number of the sample process deviation data; Among them, the calculation formula of the process deviation characteristics is: Among them, is the process deviation characteristic, is the absolute process deviation, is the maximum positive process deviation, is the minimum negative process deviation, , and are the preset initial weights of the process deviation characteristics.

[0026] It should be noted that the sample process deviation data is the result calculated by subtracting the chip manufacturing process parameters from the pre-set target data in step S12. It intuitively reflects the deviation degree of each process parameter relative to the ideal value during the actual chip manufacturing process. The maximum positive process deviation, that is, the maximum value in the sample process deviation data, represents the situation where the actual value of a certain process parameter exceeds the target value the most among all chip samples. For example, in the doping process of a chip, if the pre-set target value of the doping concentration is 1×10 18 atoms / cm³, and among many chip samples, in the deviation data obtained by subtracting the actual measured value of the doping concentration of a certain chip from the target value, the largest positive value is 5×10 16 atoms / cm³, then this 5×10 16 atoms / cm³ is the maximum positive process deviation of this process parameter. The maximum negative process deviation is the minimum value in the sample process deviation data, which reflects the situation where the actual value of a certain process parameter is lower than the target value the most. The absolute process deviation is the value obtained by summing and averaging the absolute values of all sample process deviation data. It measures the average level of process parameter deviation as a whole, is not affected by the positive or negative of the deviation, and can comprehensively reflect the stability of the process.

[0027] When determining the process deviation characteristics, first screen out the maximum and minimum values from the numerous sample process deviation data, and determine them as the maximum positive process deviation and the maximum negative process deviation respectively. Taking the lithography process in chip manufacturing as an example, assume that the thickness of the photoresist of a batch of chips is measured and the sample process deviation data is calculated. Among them, the largest positive deviation is 10nm, which is the maximum positive process deviation, indicating that the thickness of the photoresist of some chips exceeds the target value by up to 10nm; while the smallest negative deviation is -8nm, which is the maximum negative process deviation, meaning that the thickness of the photoresist of some chips is lower than the target value by up to 8nm. Then calculate the absolute process deviation according to the formula. For example, there are 100 chip samples of photoresist thickness deviation data . After adding the absolute values of these 100 data and then dividing by 100, the absolute process deviation of this process parameter is obtained . Then, according to the calculation formula of the process deviation characteristics where , and are the pre-set initial weights, and these weights can be set in advance according to factors such as the importance of the process and the impact on chip performance. For example, if the absolute process deviation of a certain process parameter has a greater impact on the overall performance of the chip, then may have a relatively high value; if the maximum positive process deviation has a more significant impact on certain specific performances, The value will be adjusted accordingly. Through such calculations, the absolute process deviation, the maximum positive process deviation, and the maximum negative process deviation are combined to obtain a process deviation characteristic value that can comprehensively characterize the process deviation situation. .

[0028] In step S14, calculation operations need to be performed according to the chip function test parameters to obtain the chip function test deviation, including: Among them, the chip function test deviation is obtained through the following formula: Among them, is the chip function test deviation, representing the i-th value of the deviation of a certain chip in function parameters, is the i-th value of the chip function test parameters; represents a logical function for converting the i-th value of the chip function test parameters into the calculation result of the chip function test deviation; is the logical function weight, is the logical function bias.

[0029] It should be noted that the chip function test parameters refer to various data indicators collected during the function test of the chip. For example, for a microprocessor chip, it may include parameters such as instruction execution speed, data reading accuracy, cache hit rate, etc.; for an image processing chip, it may cover parameters such as image resolution processing ability, color reproduction accuracy, image transmission rate, etc. These parameters reflect the performance of the chip in executing its intended functions during actual operation from different aspects. The chip function test deviation refers to the quantitative result of measuring the difference between the actual performance of the chip function and the expected standard. It is obtained through specific calculations and can intuitively present the deviation degree of the chip function. Whether it is a positive or negative deviation, it can be accurately represented, and its numerical value is directly related to the stability and reliability of the chip function. The logical function refers to a logical function for converting the i-th value of the chip function test parameters into the calculation result of the chip function test deviation. Specifically, this logical function may be a non-linear function used to perform a certain form of conversion or weighting on the input value to better reflect the deviation of the chip in function parameters. It is constructed based on the internal logical relationship of the chip function, as well as a large amount of experimental data and theoretical models. Different function test parameter values will trigger different logical functions, and this function reflects the logical behavior pattern that the chip should follow under specific function parameters, thereby affecting the calculation result of the final function test deviation. The logical function weight Refers to the relative importance of each logic function in the overall deviation calculation. For example, if a certain functional test parameter has a greater impact on the overall function of the chip, then the weight of the corresponding logic function will be higher, and it will have a more significant impact on the result when calculating the deviation, thus ensuring that the calculation result can accurately reflect the key characteristics of the chip function. Logic function bias Refers to a fixed constant that provides a basic offset in the formula. Its significance lies in compensating for possible systematic biases in the calculation model, making the calculated chip functional test deviation more in line with the actual physical meaning and engineering application scenarios.

[0030] Taking an audio processing chip as an example, its chip functional test parameters may include the distortion degree, signal-to-noise ratio, frequency response range, etc. of the audio signal. When testing the parameter of the distortion degree of the audio signal, assuming that the standard distortion degree is 0.1%, and the actually measured value is 0.15%, then when calculating the functional test deviation, is 0.15%, through the corresponding logic test function and the set logic function weight as well as the logic function bias , according to the formula: Calculate the test deviation of this audio processing chip in terms of the function of the distortion degree of the audio signal. Different types of audio processing chips may have different settings of logic function weights due to differences in design goals and application scenarios. For example, for professional audio recording chips, the weight of the distortion degree of the audio signal may be relatively high because low distortion is crucial for high-quality audio recording; while for some ordinary consumer-grade audio playback chips, the weight of the frequency response range may be relatively more prominent to meet the auditory needs of different users for different music styles.

[0031] In step S15, it is necessary to perform a second analysis operation based on the chip functional test deviation to obtain functional deviation characteristics, including: Select the maximum value of the functional test deviation as the maximum positive functional deviation, and select the minimum value of the functional test deviation as the minimum negative functional deviation; Perform a calculation operation based on the maximum positive functional deviation, the minimum negative functional deviation, and the absolute functional deviation to obtain the functional deviation characteristics; Among them, the absolute functional deviation is obtained through the following calculation: Among them, is the absolute functional deviation, is the chip functional test result, representing the i-th value of the chip in the functional test deviation, Represents the total number of functional test deviations; Among them, the calculation formula for the functional deviation feature is: Among them, is the functional deviation feature, is the absolute functional deviation, is the maximum positive functional deviation, is the minimum negative functional deviation, , and are the preset initial weights of the functional deviation feature.

[0032] Specifically, the functional deviation feature is an index that comprehensively reflects the functional deviation status of the chip and is obtained by weighted calculation of the absolute functional deviation, the maximum positive functional deviation, and the maximum negative functional deviation. It integrates various deviation information, more comprehensively reflects the deviation characteristics of the chip function in different dimensions and their mutual relationships, thus providing a quantitative and representative characteristic value for the comprehensive evaluation of the chip.

[0033] In step S16, it is necessary to update and calculate the preset initial weights according to the deviation feature correlation coefficient to obtain the process feature weight and the functional feature weight, including: Calculate the deviation feature correlation coefficient between the process deviation feature and the functional deviation feature; Calculate the initial weight of the process deviation feature according to the deviation feature correlation coefficient to obtain the process feature weight; Calculate the initial weight of the functional deviation feature according to the deviation feature correlation coefficient to obtain the functional feature weight; Among them, the process feature weight is calculated by the following formula: Among them, is the process feature weight corresponding to the process deviation feature, is the preset initial weight of the process deviation feature, represents the total number of sample process deviation data, represents the deviation feature correlation coefficient; Among them, the functional feature weight is calculated by the following formula: Among them, is the functional feature weight corresponding to the functional deviation feature, is the preset initial weight of the functional deviation feature, represents the total number of functional test deviations, represents the deviation feature correlation coefficient.

[0034] It should be noted that the correlation coefficient of deviation features refers to the degree of correlation between process deviation features and functional deviation features. This coefficient can be calculated by statistical methods, and its value range is [-1, 1], where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation. The preset initial weight of process deviation features refers to the coefficient preset before the weight update calculation to measure the importance of process deviation features in the comprehensive evaluation. These initial weights are set based on certain experience, theoretical analysis, or preliminary experimental data, but may not fully and accurately reflect the actual situation of the chip during actual operation. For example, during the initial setting, a relatively fixed weight value may be given to the process deviation features in some aspects based on the general understanding of similar chips in the past. However, as the in-depth detection of the specific chip progresses, adjustments need to be made according to the actual correlation between its process and functional deviations. The process feature weight refers to the weight value obtained after calculation and update with the correlation coefficient of deviation features, which is used to accurately reflect the actual contribution degree of process deviation features to the overall evaluation of the chip in the comprehensive analysis operation. It is no longer a simple initial preset value, but a dynamically adjusted result considering the correlation between process deviation features and functional deviation features, making the weight allocation of process deviation features in the final evaluation more reasonable.

[0035] In step S17, a comprehensive analysis operation needs to be performed based on the process deviation features, the functional deviation features, the process feature weight, and the functional feature weight to obtain a comprehensive evaluation value, including: Calculate a process evaluation value according to the process deviation features and the process feature weight; Calculate a functional evaluation value according to the functional deviation features and the functional feature weight; Calculate a comprehensive evaluation value according to the process evaluation value and the functional evaluation value; Among them, the process evaluation value is calculated by the following formula: Among them, is the process evaluation value, is the absolute process deviation, is the maximum positive process deviation, is the minimum negative process deviation, 、 and are the process feature weights; Among them, the functional evaluation value is calculated by the following formula: Among them, is the function evaluation value, is the absolute function deviation, is the maximum positive function deviation, is the minimum negative function deviation, , and are the preset initial weights of the function deviation characteristics; Among them, the calculation formula of the comprehensive evaluation value is as follows: Among them, is the comprehensive evaluation value, is the process characteristic weight, is the function characteristic weight, is the process evaluation value, is the function evaluation value.

[0036] It should be noted that the process evaluation value is obtained by summing the absolute process deviation, the maximum positive process deviation, and the maximum negative process deviation after multiplying them by their corresponding process characteristic weights respectively. It is a quantitative evaluation of the overall level of the chip process from the perspective of process deviation characteristics, considering the weighted distribution of different types of process deviations. For example, the absolute process deviation reflects the overall average level of process deviation, and the maximum positive and negative process deviations highlight the extreme cases of process deviation. The process evaluation value calculated by combining the three with the process characteristic weights can comprehensively show the status of the chip process. The function evaluation value is obtained by multiplying the absolute function deviation, the maximum positive function deviation, and the maximum negative function deviation by their respective function characteristic weights and then accumulating them. It quantitatively measures the comprehensive performance of the chip function from the aspect of function deviation characteristics in the form of weighted summation, reflecting the comprehensive deviation degree of the chip in the function dimension and the relative importance of each function deviation type. The comprehensive evaluation value is the final result obtained by weighting and summing the process evaluation value and the function evaluation value again according to their corresponding weights. It integrates the key information of the chip in terms of process and function, eliminates the possible one-sidedness of separate evaluations of the two, and provides an intuitive quantitative index for the overall quality evaluation of the chip.

[0037] In step S17, it is necessary to judge the comprehensive evaluation value according to the preset first score and the preset second score to obtain the operation state detection result of the chip, including: When the comprehensive evaluation value y≠0 and y < a1, output the operation state detection result that the chip operation state is good; When the comprehensive evaluation value y≠0 and a1 ≤ y < a2, output the operation state detection result that the chip operation state is average; When the comprehensive evaluation value y≠0 and a1≤y≤1, output the operation status detection result indicating that the chip operation status is poor. When the comprehensive evaluation value y = 0, output the operation status detection result indicating that the chip does not meet the minimum requirements; where a1 and a2 are the first score and the second score respectively, and satisfy 0<a1<a2<1.

[0038] Specifically, in this step, the comprehensive evaluation value y is compared with the preset first score a1 and second score a2 to determine the operation status of the chip. According to different evaluation results, the system will output the corresponding operation status detection results. Determining the values of the first score a1 and the second score a2 involves understanding the chip performance requirements, statistical analysis of historical test data, and consideration of industry standards. In specific operations, a certain amount of historical test results can be collected, the distribution of these data can be analyzed, and then a1 and a2 can be set according to the statistical characteristics of the data (such as percentiles) to ensure that they can reasonably distinguish the performance levels of the chips. At the same time, these values can also be adjusted according to expert experience and actual production requirements to ensure their effectiveness and accuracy in actual applications.

[0039] The following describes the working process of the present invention with a relatively common scenario as an example. Please also refer to Figure 2 , which is Figure 1 a schematic diagram of the working scenario of the method of

[0040] In a smart phone chip manufacturing enterprise, a newly developed high-end smart phone chip is about to be put into mass production. During the chip production process, first execute S11 to obtain chip manufacturing process parameters, such as lithography accuracy, doping concentration, etc., and chip function test parameters, such as data processing speed, graphics rendering ability, etc.

[0041] Then in step S12, the obtained chip manufacturing process parameters are subjected to complex calculations with the standard manufacturing process parameters of the same type of chips pre-stored in the database for the target data of each chip, so as to obtain sample process deviation data. Subsequently, in S13, based on these sample process deviation data, a first analysis operation is carried out to determine the process deviation characteristics. For example, it is found that the maximum positive process deviation of a certain batch of chips appears in lithography accuracy, which is slightly higher than the standard accuracy and may have a potential impact on certain performances of the chips; the maximum negative process deviation is in the aspect of doping concentration, which is lower than the standard value, and this may lead to fluctuations in the power consumption control of the chips; the absolute process deviation reflects the average degree of deviation of the overall process parameters from the standard.

[0042] Then in S14, calculation operations are performed using the chip function test parameters to obtain the chip function test deviation. For example, in the function test of data processing speed, the test deviations of different chips are different. Then in step S15, based on these chip function test deviations, a second analysis operation is carried out to obtain the function deviation characteristics. It is found that the maximum positive function deviation of some chips is reflected in their extremely strong graphics rendering ability, far exceeding the expected standard. However, there is a maximum negative function deviation in multitasking, with a slightly slower response speed. The absolute function deviation comprehensively reflects the overall deviation in terms of function.

[0043] In S16, the deviation feature correlation coefficient between the process deviation characteristics and the function deviation characteristics is calculated, and based on this, the preset initial weights are updated and calculated to obtain the process feature weights and function feature weights applicable to this batch of chips. For example, if it is found that the process deviation has a greater impact on the graphics rendering function, the relevant process feature weights will be adjusted accordingly.

[0044] In step S17, a comprehensive analysis operation is carried out based on the determined process deviation characteristics, function deviation characteristics, and the calculated process feature weights and function feature weights to obtain a comprehensive evaluation value. Finally, in S18, the enterprise sets specific first and second scores, and based on these scores, the comprehensive evaluation value is judged. If the comprehensive evaluation value meets the standard of good operating status, this batch of chips can smoothly enter the next packaging process, ready for mass production and application in high-end smartphones; if the evaluation result is of average operating status, the chips need to be further optimized or additional detection links need to be added; and if the evaluation result is of poor operating status or does not meet the minimum requirements, then this batch of chips will be isolated, the root cause of the problem will be re-analyzed and improved or directly scrapped, so as to ensure that the chips finally applied in smartphones can all have high performance and reliability, providing users with a smooth and efficient smartphone usage experience.

[0045] In summary, the present invention discloses a method for detecting defects of a chip, including the following steps: obtaining chip manufacturing process parameters and chip function test parameters; calculating the sample process deviation data of the chip manufacturing process parameters for each chip; performing a first analysis operation to determine the process deviation characteristics; calculating the chip function test deviation; performing a second analysis operation to obtain the function deviation characteristics; updating and calculating the process feature weights and function feature weights; performing a comprehensive analysis operation to obtain a comprehensive evaluation value; and judging the detection result of the operating status of the chip based on the preset first and second scores. By accurately obtaining the chip manufacturing process parameters and function test parameters, and deeply analyzing and calculating their weights, the present invention realizes the accurate detection of chip defects and the comprehensive evaluation of the operating status. This not only improves the efficiency and accuracy of chip detection, but also is beneficial to improving the overall performance and quality of electronic products, ensuring the user experience.

[0046] Reference Figure 2 , the second embodiment of the present invention provides a method for detecting defects of a chip, including: A data acquisition module for acquiring chip manufacturing process parameters and chip function test parameters; A process deviation module for calculating the target data of each chip according to the chip manufacturing process parameters and the previously acquired chip manufacturing process parameters, and obtaining the sample process deviation data of the chip manufacturing process parameters for each chip; A process feature module for performing a first analysis operation according to the sample process deviation data to determine process deviation features, where the process deviation features include the maximum positive process deviation, the maximum negative process deviation, and the absolute process deviation; A function deviation module for performing a calculation operation according to the chip function test parameters to obtain a chip function test deviation; A function feature module for performing a second analysis operation according to the chip function test deviation to obtain function deviation features, where the function deviation features include the maximum positive function deviation, the maximum negative function deviation, and the absolute function deviation; A weight update module for updating and calculating a preset initial weight according to a deviation feature correlation coefficient to obtain a process feature weight and a function feature weight; A comprehensive evaluation module for performing a comprehensive analysis operation according to the process deviation features, the function deviation features, the process feature weight, and the function feature weight to obtain a comprehensive evaluation value; A result output module for judging the comprehensive evaluation value based on a preset first score and a preset second score to obtain a detection result of the operating state of the chip.

[0047] It should be noted that a chip defect detection device provided in an embodiment of the present invention is used to execute all the process steps of the method for detecting defects of a chip in the above embodiment, and the working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0048] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0049] The electronic device may be a computing device such as a desktop computer, notebook, palm computer, and smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may also include input / output devices, network access devices, a bus, etc.

[0050] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and lines.

[0051] The memory can be used to store the computer program and / or module. The processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0052] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0053] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0054] The above-described specific embodiments have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A chip defect detection method, characterized in that: Executed by a computer, including: Obtain chip manufacturing process parameters and chip function test parameters; Calculate according to the chip manufacturing process parameters and the pre-acquired target data of the chip manufacturing process parameters on each chip to obtain sample process deviation data of the chip manufacturing process parameters on each chip; Performing a first analysis operation according to the sample process deviation data to determine a process deviation feature, wherein the process deviation feature includes a maximum positive process deviation, a maximum negative process deviation, and an absolute process deviation; Performing calculation operations according to the chip function test parameters to obtain a chip function test deviation; Performing a second analysis operation according to the chip function test deviation to obtain a function deviation feature, wherein the function deviation feature includes a maximum positive function deviation, a maximum negative function deviation and an absolute function deviation; The preset initial weights are updated and calculated according to the deviation feature correlation coefficient to obtain the process feature weights and the functional feature weights; Perform a comprehensive analysis operation according to the process deviation feature, the functional deviation feature, the process feature weight, and the functional feature weight to obtain a comprehensive evaluation value; The comprehensive evaluation value is judged based on the preset first score and the preset second score to obtain a chip operation status detection result.

2. The chip defect detection method according to claim 1, characterized in that: Calculating according to the chip manufacturing process parameters and the pre-acquired target data of the chip manufacturing process parameters on each chip to obtain sample process deviation data of the chip manufacturing process parameters on each chip includes: Subtract the chip manufacturing process parameter from the pre-acquired target data of the chip manufacturing process parameter on each chip to obtain sample process deviation data of the chip manufacturing process parameter on each chip; The calculation formula of the sample process deviation data is: in, is the sample process deviation data, indicating the i-th value of the deviation of a chip in a certain process parameter. is the i-th value of the chip manufacturing process parameter, is the i-th value of the chip manufacturing process parameter in the target data of each chip.

3. The chip defect detection method according to claim 1, characterized in that: Performing a first analysis operation according to the sample process deviation data to determine a process deviation feature includes: The maximum value of the sample process deviation data is selected as the maximum positive process deviation, and the minimum value of the sample process deviation data is selected as the maximum negative process deviation; Performing calculation operations according to the maximum positive process deviation, the minimum negative process deviation and the absolute process deviation to obtain a process deviation feature; Among them, the absolute process deviation is obtained by the following calculation: in, is the absolute process deviation, is the sample process deviation data, which indicates the deviation of a chip in a certain process parameter. values, Indicates the total number of sample process deviation data; Among them, the calculation formula of process deviation characteristics is: in, is the process deviation characteristic, is the absolute process deviation, is the maximum positive process deviation, is the minimum negative process deviation, , and The preset initial weight for the process deviation feature.

4. The chip defect detection method according to claim 1, characterized in that: Calculate the chip function test parameters to get the chip function test deviation. include: Among them, the chip function test deviation is obtained by the following formula: in, is the chip function test deviation, which indicates the ith value of the deviation of a chip in terms of functional parameters. is the i-th value of the chip function test parameter; Represents a logic function, which is used to convert the i-th value of the chip function test parameter Converted into calculation results of chip functional test deviation; is the logistic function weight, is the logistic function bias.

5. The chip defect detection method according to claim 1, characterized in that: Performing a second analysis operation according to the chip function test deviation to obtain a function deviation feature includes: The maximum value of the functional test deviation is selected as the maximum positive functional deviation, and the minimum value of the functional test deviation is selected as the minimum negative functional deviation; Perform calculation operations according to the maximum positive functional deviation, the minimum negative functional deviation and the absolute functional deviation to obtain a functional deviation feature; Among them, the absolute functional deviation is obtained by the following calculation: in, is the absolute functional deviation, is the chip functional test result, indicating the i-th value of the functional test deviation of a chip. Indicates the total number of functional test deviations; Among them, the calculation formula of functional deviation characteristics is: in, is the functional deviation characteristic, is the absolute functional deviation, is the maximum positive functional deviation, is the minimum negative functional deviation, , and The preset initial weight for the functional deviation feature.

6. The chip defect detection method according to claim 1, characterized in that: The preset initial weights are updated and calculated according to the deviation feature correlation coefficient to obtain the process feature weights and functional feature weights, including: The deviation characteristic correlation coefficient between the process deviation characteristic and the functional deviation characteristic is calculated; Calculating the initial weight of the process deviation feature according to the deviation feature correlation coefficient to obtain the process feature weight; Calculating the initial weight of the functional deviation feature according to the deviation feature correlation coefficient to obtain the functional feature weight; Among them, the process feature weight is calculated by the following formula: in, is the process feature weight corresponding to the process deviation feature, is the preset initial weight of the process deviation feature, Represents the total number of sample process deviation data, represents the deviation characteristic correlation coefficient; Among them, the functional feature weight is calculated by the following formula: in, is the functional feature weight corresponding to the functional deviation feature, is the preset initial weight of the functional deviation feature, Indicates the total number of functional test deviations, represents the deviation characteristic correlation coefficient.

7. The chip defect detection method according to claim 1, characterized in that: A comprehensive analysis operation is performed according to the process deviation feature, the functional deviation feature, the process feature weight and the functional feature weight to obtain a comprehensive evaluation value, including: Calculating according to the process deviation feature and the process feature weight to obtain a process evaluation value; Calculate according to the functional deviation feature and the functional feature weight to obtain a functional evaluation value; Calculating according to the process evaluation value and the function evaluation value to obtain a comprehensive evaluation value; The process evaluation value is calculated by the following formula: in, is the process evaluation value, is the absolute process deviation, is the maximum positive process deviation, is the minimum negative process deviation, , and is the process feature weight; The functional evaluation value is calculated by the following formula: in, is the function evaluation value, is the absolute functional deviation, is the maximum positive functional deviation, is the minimum negative functional deviation, , and The preset initial weights for the functional deviation features; The calculation formula of the comprehensive evaluation value is as follows: in, is the comprehensive evaluation value, is the process feature weight, is the functional feature weight, is the process evaluation value, is the function evaluation value.

8. The chip defect detection method according to claim 1, characterized in that: The step of judging the comprehensive evaluation value based on the preset first score and the preset second score to obtain the chip operation status detection result includes: When the comprehensive evaluation value y≠0 and y<a1, the operation status detection result indicating that the chip is in good operation status is output; When the comprehensive evaluation value y≠0 and a1≤y<a2, the general operation status detection result of the chip operation status is output; When the comprehensive evaluation value y≠0 and a1≤y≤1, the operating state detection result indicating that the chip operating state is poor is output; When the comprehensive evaluation value y=0, the output chip does not meet the minimum requirement of the operating state detection result; wherein a1 and a2 are the first score and the second score respectively, and satisfy 0<a1<a2<1.

9. A chip defect detection device, characterized in that: include: A data acquisition module, which acquires chip manufacturing process parameters and chip function test parameters; A process deviation module is configured to calculate sample process deviation data of the chip manufacturing process parameters on each chip based on the chip manufacturing process parameters and the pre-acquired target data of the chip manufacturing process parameters on each chip; A process feature module, performing a first analysis operation according to the sample process deviation data to determine a process deviation feature, wherein the process deviation feature includes a maximum positive process deviation, a maximum negative process deviation, and an absolute process deviation; A function deviation module performs calculation operations according to the chip function test parameters to obtain a chip function test deviation; A function characteristic module performs a second analysis operation according to the chip function test deviation to obtain a function deviation characteristic, wherein the function deviation characteristic includes a maximum positive function deviation, a maximum negative function deviation and an absolute function deviation; The weight updating module updates and calculates the preset initial weights according to the deviation feature correlation coefficient to obtain the process feature weights and functional feature weights; A comprehensive evaluation module performs a comprehensive analysis operation according to the process deviation feature, the functional deviation feature, the process feature weight and the functional feature weight to obtain a comprehensive evaluation value; The result output module judges the comprehensive evaluation value based on the preset first score and the preset second score to obtain the chip operation status detection result.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the chip defect detection method according to any one of claims 1 to 8.

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