Chip transmission and packaging system and method based on electrostatic protection

Abstract

The application discloses a chip transmission and packaging system and method based on electrostatic protection, and relates to the field of semiconductors.The application divides the process flow of chip packaging, collects historical electrostatic data by using a sensor, sets a charge risk threshold according to the historical electrostatic data of damaged chips, then constructs a charge prediction model by using a polynomial algorithm, and then predicts the charge generated by each process flow by using the charge prediction model according to the voltage value collected by the sensor.The sum of the predicted charges of the two process flows associated with each other is compared with the charge risk threshold, and different types of operations are performed according to the comparison result.When the prediction result does not match the actual result, specific parameters are obtained by traceability analysis, and the prediction model is dynamically updated according to the parameters.The application realizes accurate electrostatic monitoring, early warning and protection of the whole process of chip packaging, and improves the yield and efficiency of chip packaging.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of semiconductor, in particular to a chip transmission and packaging system and method based on electrostatic protection. BACKGROUND

[0002] In the field of chip packaging, the traditional chip packaging technology only relies on the wearing of electrostatic rings by technicians or the gradual electrostatic elimination operation in the chip packaging process to protect against static electricity, lacks fine monitoring of static electricity in the entire process, and is mostly passive protection, which is usually performed after static electricity has caused damage, lacks the ability to predict static electricity risks in advance, and cannot adjust the protection strategy according to the dynamic changes in the process, resulting in insufficient specificity and efficiency of static electricity protection, which easily causes low yield of chips.

[0003] The present application provides a chip transmission and packaging technology based on electrostatic protection, which realizes real-time monitoring of static electricity data in the entire process of chip packaging by systematic division and sensor deployment of the process, builds a prediction model according to historical data, can predict whether the next process will cause chip damage due to static electricity after the current process is completed, predicts the static electricity risk of the next process in advance, updates the prediction model in real time through traceability analysis, adapts to the dynamic adjustment of the process, effectively solves the passive protection defects of static electricity in the traditional chip packaging technology, and improves the efficiency and yield of chip packaging. SUMMARY

[0004] The present application aims to provide a chip transmission and packaging system and method based on electrostatic protection to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a chip transmission and packaging method based on electrostatic protection, the chip transmission and packaging method comprising the following steps:

[0006] Step S1, divide the chip packaging process, set sensors in each process, collect static electricity data of historical chip packaging of each process by using the sensors, extract static electricity data that causes chip damage due to static electricity, extract the amount of electric charge, and set a risk threshold for the amount of electric charge;

[0007] Step S1-1, divide the chip packaging process into wafer cutting, chip mounting, wire bonding, and plastic packaging;

[0008] Step S1-2, set sensors in each process, and collect static electricity data of historical chip packaging of each process by using the sensors;

[0009] Step S1-3, for the electrostatic data of the historical chip package, extract the electrostatic data that causes the chip damage due to static electricity, extract the charge amount of each process flow, set the charge amount risk threshold of each process flow for each process flow, specifically:

[0010] Step S1-3-1, for each process flow, respectively construct the charge amount data set of each process flow ;

[0011] The 、 、 in the charge amount data set is the charge amount carried by the chip processed by the first, second, and nth process flow;

[0012] Step S1-3-2, the outliers of the charge amount data set constructed for each process flow are removed using the box plot method, and the outlier determination is specifically:

[0013] After arranging the charge amount data set in ascending order, the value at the 25% position is the first quartile;

[0014] After arranging the charge amount data set in ascending order, the value at the 75% position is the third quartile;

[0015] ;

[0016] In the formula, L is the lower limit threshold of the outlier, Q1 is the first quartile, and IQR is the interquartile range;

[0017] ;

[0018] In the formula, U is the upper limit threshold of the outlier, Q3 is the third quartile, and IQR is the interquartile range;

[0019] When the charge amount is less than the lower limit threshold or the charge amount is greater than the upper limit threshold, the charge amount data is determined as an outlier;

[0020] Step S1-3-3, for the missing values of the charge amount data set constructed for each process flow, use the median filling method to supplement;

[0021] Step S1-3-4, set the smallest charge amount value in the chip damage charge amount data set caused by static electricity as the risk threshold;

[0022] This step collects and processes electrostatic data by dividing the chip packaging process, sets the risk threshold, provides accurate and quantifiable benchmarks for subsequent electrostatic risk monitoring, and clearly defines the electrostatic safety critical value of each process, effectively identifying potential electrostatic risks.

[0023] Step S2, according to the collected electrostatic data of the historical chip package, a scatter plot of the charge amount is drawn, and then a polynomial algorithm is used to construct a charge amount prediction model for each process flow;

[0024] Step S2-1, for each independent process flow of the chip package, taking the charge amount as the vertical axis and the voltage as the horizontal axis, all the processed historical sample data are marked as scatter points in the coordinate system, and a scatter plot is drawn for each process flow;

[0025] Step S2-2, setting the process flow as P, for each process flow P i , a polynomial algorithm is used to construct a charge amount prediction model, specifically:

[0026] ;

[0027] In the formula, is the predicted charge amount of the i-th process flow P i , , , , are the polynomial coefficients of the process P i , and V is a given voltage;

[0028] Step S2-3, the least square method is used to solve the polynomial coefficients, specifically:

[0029] ;

[0030] In the formula, min is the minimum value of the square error sum, is the error accumulation of all historical samples of the i-th process flow, is the error term, is the actual measured charge amount of the j-th chip in the i-th process flow, is the predicted charge amount;

[0031] Step S2-4, according to the least square method, the polynomial coefficients of the model are solved, and then the polynomial coefficients are input into the algorithm model to construct a charge amount prediction model;

[0032] According to the scatter plot drawn from the processed data, a polynomial algorithm is used to construct a charge amount prediction model, a quantitative relationship between the charge amount and the voltage is established, the function of predicting the charge amount through the voltage is realized, the prediction ability of the electrostatic data changes of each process flow of the chip package is realized, and the model support for real-time monitoring is provided.

[0033] Step S3, in the chip packaging process, a sensor is used to collect the voltage values generated by the chip in each process flow;

[0034] Step S3-1, number the sensors and deploy the sensors to monitor each process flow of the chip packaging;

[0035] Step S3-2, use the sensors to collect the voltage of each chip in each process flow and the amount of charge generated when the chip packaging operation is performed on the chip by the process flow.

[0036] By numbering and deploying the sensors in each process flow, the voltage and charge amount of each chip are collected in real time, ensuring real-time data acquisition, providing data support for the prediction model, and ensuring the timeliness of the prediction.

[0037] Step S4, use the charge amount prediction model to predict the amount of charge generated by each process flow according to the voltage collected by the sensor for each process flow, compare the sum of the predicted charge amounts of the two process flows that are related to each other with the charge amount risk threshold, and select different operations according to the judgment result;

[0038] Step S4-1, when the chip starts the chip packaging operation, use the sensor to collect the voltage value at the start of the first process flow, and use the charge amount prediction model to predict the amount of charge generated by the first process flow according to the voltage value;

[0039] Step S4-2, after each process flow is completed, collect the voltage value of the chip at the start of the next process flow, use the charge amount prediction model to predict the amount of charge at the start of the process flow according to the voltage value, and compare the sum of the predicted charge amounts of the previous process flow with the charge amount risk threshold;

[0040] Step S4-3, repeat the operation according to the process flow sequence to obtain the accumulated charge amount of each process flow and make a judgment, when the accumulated charge amount of the corresponding process flow is greater than the charge amount risk threshold, perform the static electricity elimination operation, and when it is less than the risk threshold, continue to execute the current process flow;

[0041] By comparing the accumulated charge amount with the risk threshold, the dynamic assessment of the static electricity risk is realized, and the chip packaging efficiency and yield are improved.

[0042] Step S5, when the charge amount prediction value does not conform to the actual charge amount collection value, trace the analysis of the abnormal charge amount, and then update the charge amount prediction model according to the trace analysis result.

[0043] Step S5-1, a professional sets the deviation threshold interval [a, b] of the predicted charge amount and the actual charge amount according to the business scenario;

[0044] Step S5-2, when the deviation between the charge amount prediction value and the actually collected charge amount does not meet the threshold interval, it is determined that the charge amount prediction model is abnormal, and the traceability analysis process is started;

[0045] Step S5-3, collect the charge amount and the corresponding voltage, determine the abnormal process flow through the sensor number, and then store the collected charge amount abnormal value and the corresponding voltage;

[0046] Step S5-4, the professional personnel set the number threshold x according to the business scene, when the number of times that the deviation between the charge amount prediction value and the actually collected charge amount does not meet the threshold interval exceeds x, it is determined that the process flow is adjusted, the charge amount and the voltage value are input into the charge amount prediction model constructed by the polynomial algorithm, the polynomial coefficients of the charge amount prediction model of the process flow are recalculated, and finally the polynomial coefficients are substituted into the charge amount prediction model constructed by the polynomial algorithm to obtain a new charge amount prediction model.

[0047] By setting the prediction deviation threshold, when the deviation between the prediction value and the actual value is too large, traceability analysis is performed, and when the number of abnormalities exceeds the limit, the model is updated to ensure that the prediction model can adapt to process flow adjustment, continuously guarantee accuracy, and improve the long-term effectiveness and adaptability of the system.

[0048] The chip transmission and packaging system includes a risk threshold setting module, a prediction model construction module, a real-time data acquisition module, a charge risk determination module, and a model updating and optimization module.

[0049] The risk threshold setting module is used to divide the chip packaging process, collect historical electrostatic data, and set the charge risk threshold;

[0050] The prediction model construction module is used to construct a charge amount prediction model based on historical electrostatic data;

[0051] The real-time data acquisition module is used to monitor the sensor number of each process flow during the chip packaging process, and collect the voltage of each chip and the charge amount generated by the packaging operation in each process flow through the sensor;

[0052] The charge risk determination module is responsible for predicting the charge amount generated by each process flow using the charge amount prediction model, comparing the sum of the predicted charge amounts of the upper and lower associated process flows with the risk threshold, and when the predicted charge amount of the upper and lower associated process flows is greater than the risk threshold, performing electrostatic elimination operation, and if it is less than, continuing to execute the current process flow;

[0053] The model update optimization module is used to start tracing analysis to determine the process flow to which the anomaly belongs and store relevant data when the deviation between the charge quantity prediction value and the actual collected value of the sensor exceeds a threshold range. When the number of anomalies exceeds a threshold, the charge quantity prediction model of the process flow is updated with relevant data.

[0054] The risk threshold setting module includes a data processing unit and a threshold setting unit;

[0055] The data processing unit is used to divide the chip packaging process flow, set sensors in each process to collect historical electrostatic data, and process the charge data set;

[0056] The threshold setting unit is used to set the minimum charge value as the risk threshold of each process flow from the charge amount data of the chip damaged by static electricity.

[0057] The prediction model building module includes a scatter plot drawing unit and a polynomial modeling unit;

[0058] The scatter plot drawing unit is used for marking historical sample data as scatter points and drawing a scatter plot for each process flow, with charge as the vertical axis and voltage as the horizontal axis;

[0059] The polynomial modeling unit is used to construct a charge quantity prediction model through a polynomial algorithm and solve the polynomial coefficients through a least squares method;

[0060] The real-time data acquisition module includes a sensor deployment unit and a voltage acquisition unit;

[0061] The sensor deployment unit is used to number and deploy sensors for each process flow to monitor the chip packaging process;

[0062] The voltage acquisition unit is used to use a sensor to acquire the voltage generated by each chip in each process flow.

[0063] The charge risk determination module includes a charge prediction and accumulation unit and a risk response unit;

[0064] The charge prediction and accumulation unit is used to obtain the charge amount prediction value of each process flow according to the voltage collected by the sensor using the prediction model, and add the charge amounts predicted by the two process flows that are connected vertically;

[0065] The risk response unit is used to perform an electrostatic elimination operation when the sum of the charge amounts predicted by the two process flows associated with each other exceeds a charge amount risk threshold, and to continue to perform the current process flow when the sum is less than the charge amount risk threshold;

[0066] The model update optimization module includes an anomaly tracing unit and a model update unit;

[0067] The anomaly tracing unit is used to set a deviation threshold interval, and when it is determined that the charge amount prediction value does not match the actual collected value, the charge amount is stored and subjected to tracing analysis.

[0068] The model updating unit is used to input relevant data into the model to recalculate the polynomial and update the charge amount prediction model when the number of times that the charge amount prediction test does not match the actual collected value exceeds a number threshold.

[0069] Compared with the prior art, the beneficial effects of the present application are:

[0070] 1. The present application standardizes the division of chip packaging process flow, collects and processes electrostatic data, sets a threshold for the collected data, and provides accurate and unified judgment criteria for electrostatic prevention and control of each process, thereby ensuring the scientificity and reliability of risk identification.

[0071] 2. The present application constructs a charge amount prediction model through a polynomial algorithm, combines voltage data collected by real-time sensors, can predict the charge amount of each process, dynamically assesses risks, and promptly initiates electrostatic elimination operations, thereby improving the yield of chip packaging.

[0072] 3. The present application sets a deviation threshold and a number threshold for the predicted charge amount value and the actual collected value, performs tracing analysis operations and updates the model when the prediction model deviates, so that the prediction model can adapt to changes in the process flow, continuously maintain high prediction accuracy, and ensure that the system can dynamically adapt to adjustments in the process flow. BRIEF DESCRIPTION OF DRAWINGS

[0073] Fig. 1 FIG. 1 is a flowchart of a chip transmission and packaging method based on electrostatic protection according to the present application;

[0074] Fig. 2 FIG. 2 is a structural diagram of a chip transmission and packaging system based on electrostatic protection according to the present application. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0076] Embodiment: As shown in FIG. 1, the present application provides a technical solution, a chip transmission and packaging method based on electrostatic protection, which comprises the following steps: Figs. 1-2

[0077] ​Step S1, divide the chip packaging process flow, set sensors at each process flow, collect electrostatic data of historical chip packaging of each process flow by using the sensors, extract electrostatic data causing chip damage due to static electricity, extract the charge quantity, and set the charge quantity risk threshold;

[0078] Step S1-1, divide the chip packaging flow into wafer cutting, chip mounting, wire bonding, and plastic packaging;

[0079] Step S1-2, set sensors at each process flow, and collect electrostatic data of historical chip packaging of each process flow by using the sensors;

[0080] Step S1-3, for the electrostatic data of historical chip packaging, extract electrostatic data causing chip damage due to static electricity, extract the charge quantity of each process flow, and set the charge quantity risk threshold of each process flow for each process flow, specifically:

[0081] Step S1-3-1, for each process flow, construct a charge quantity data set of each process flow ;

[0082] The charge quantity in the charge quantity data set 、 、 is the charge quantity carried by the chip processed by the first, second, and nth process flow;

[0083] Step S1-3-2, use the box plot method to remove outliers of the charge quantity data set constructed for each process flow, and the outlier determination is specifically:

[0084] After the charge quantity data set is arranged in ascending order, the value at the 25% position is the first quartile;

[0085] After the charge quantity data set is arranged in ascending order, the value at the 75% position is the third quartile;

[0086] ;

[0087] In the formula, L is the lower limit threshold of the outlier, Q1 is the first quartile, and IQR is the interquartile range;

[0088] ;

[0089] In the formula, U is the upper limit threshold of the outlier, Q3 is the third quartile, and IQR is the interquartile range;

[0090] When the charge quantity is less than the lower limit threshold or the charge quantity is greater than the upper limit threshold, the charge quantity data is determined as an outlier;

[0091] Step S1-3-3, the missing values of the charge quantity dataset constructed for each process flow are supplemented using the median filling method;

[0092] Step S1-3-4, the minimum charge quantity value in the chip damage charge quantity dataset caused by static electricity is set as a risk threshold value;

[0093] This step collects and processes static electricity data by dividing the chip packaging process, sets a risk threshold value, and provides an accurate and quantifiable benchmark for subsequent static electricity risk monitoring. It also clearly defines the static electricity safety threshold of each process and effectively identifies potential static electricity risks.

[0094] Step S2, after drawing a scatter plot of the charge quantity according to the collected static electricity data of the historical chip packaging, a polynomial algorithm is used to construct a charge quantity prediction model for each process flow;

[0095] Step S2-1, for each independent process flow of chip packaging, take the charge quantity as the vertical axis and the voltage as the horizontal axis. Mark all the processed historical sample data as scatter points in the coordinate system, and draw scatter plots for each process flow respectively;

[0096] Step S2-2, set the process flow as P, and for each process flow P i , use a polynomial algorithm to construct a charge quantity prediction model, specifically:

[0097] ;

[0098] In the formula, is the predicted charge quantity of the i-th process flow P i , , , ,..., are the polynomial coefficients of process P i , and V is a given voltage;

[0099] Step S2-3, use the least squares method to solve the polynomial coefficients, specifically:

[0100] ;

[0101] In the formula, min is the minimum value of the sum of squares, is the error accumulation of all historical samples of the i-th process flow, is the error term, is the actual measured charge quantity of the j-th chip in the i-th process flow, is the predicted charge quantity;

[0102] Step S2-4, the polynomial coefficients of the model are obtained according to the least square method, and then the polynomial coefficients are input into the algorithm model to construct the charge quantity prediction model;

[0103] According to the processed data, a scatter plot is drawn, a charge quantity prediction model is constructed by using a polynomial algorithm, a quantitative relationship between the charge quantity and the voltage is established, the function of predicting the charge quantity through the voltage is realized, and the prediction ability of the static electricity data change of each process flow of the chip packaging is realized, thereby providing model support for real-time monitoring.

[0104] Step S3, in the chip packaging process, a sensor is used to collect the voltage value generated by the chip in each process flow;

[0105] Step S3-1, for each process flow of the chip packaging, the sensor is numbered, and the sensor is deployed in each process flow for monitoring;

[0106] Step S3-2, the voltage of each chip in each process flow and the charge quantity generated when the chip packaging operation is performed on the chip by the process flow are collected by using the sensor.

[0107] By numbering and deploying the sensor in each process flow, the voltage and the charge quantity of each chip are collected in real time, so that the real-time acquisition of data is ensured, data support is provided for the prediction model, and the timeliness of the prediction is ensured.

[0108] Step S4, the charge quantity generated by each process flow is predicted by using the charge quantity prediction model according to the voltage of each process flow collected by the sensor, the sum of the predicted charge quantities of the two process flows in the upper and lower correlations is compared with the charge quantity risk threshold value for judgment, and different operations are selected according to the judgment result;

[0109] Step S4-1, when the chip starts the chip packaging operation, the voltage value of the first process flow is collected by using the sensor, and the charge quantity generated by the first process flow is predicted by using the charge quantity prediction model according to the voltage value;

[0110] Step S4-2, after each process flow is completed, the voltage value of the chip at the beginning of the next process flow is collected, the charge quantity at the beginning of the process flow is predicted by using the charge quantity prediction model according to the voltage value, and the sum of the predicted charge quantity at the beginning of the process flow and the predicted charge quantity of the previous process flow is compared with the charge quantity risk threshold value for judgment;

[0111] Step S4-3, the operation is repeated according to the process flow sequence, the accumulated charge quantity of each process flow is obtained and judged, when the accumulated charge quantity of the corresponding process flow is greater than the charge quantity risk threshold value, the static electricity elimination operation is performed, and when the accumulated charge quantity is less than the risk threshold value, the current process flow is continued to be executed;

[0112] The static electricity risk is dynamically evaluated by comparing the charge amount with the risk threshold, and the chip packaging efficiency and yield are improved.

[0113] Step S5, when the charge amount prediction value does not conform to the actual charge amount collection value, traceability analysis is performed on the charge amount anomaly, and then the charge amount prediction model is updated according to the traceability analysis result.

[0114] Step S5-1, a professional sets a deviation threshold interval [a, b] of the predicted charge amount and the actual charge amount according to the business scenario;

[0115] Step S5-2, when the deviation between the charge amount prediction value and the actual collected charge amount does not conform to the threshold interval, it is determined that the charge amount prediction model is abnormal, and a traceability analysis process is started;

[0116] Step S5-3, the charge amount and the corresponding voltage are collected, the abnormality belongs to the process flow is determined through the sensor number, and then the collected charge amount abnormal value and the corresponding voltage are stored;

[0117] Step S5-4, the professional sets a number threshold x according to the business scenario, when the number of deviations between the charge amount prediction value and the actual collected charge amount that do not conform to the threshold interval exceeds x, it is determined that the process flow is adjusted, the charge amount and the voltage value are input into the charge amount prediction model constructed by the polynomial algorithm, the polynomial coefficients of the charge amount prediction model of the process flow are recalculated, and finally the new charge amount prediction model is obtained by substituting the polynomial coefficients into the charge amount prediction model constructed by the polynomial algorithm.

[0118] By setting the prediction deviation threshold, when the deviation between the prediction value and the actual value is too large, traceability analysis is performed, and when the number of abnormalities exceeds the limit, the model is updated, which ensures that the prediction model can adapt to process flow adjustment, continuously guarantees accuracy, and improves the long-term effectiveness and adaptability of the system.

[0119] The chip transmission and packaging system comprises a risk threshold setting module, a prediction model construction module, a real-time data collection module, a charge risk determination module, and a model updating and optimization module.

[0120] The risk threshold setting module is used to divide the chip packaging process, collect historical static electricity data, and set the charge amount risk threshold.

[0121] The prediction model construction module is used to construct a charge amount prediction model based on historical static electricity data.

[0122] The real-time data collection module is used to collect the voltage of each chip and the charge amount generated by the packaging operation in each process flow through sensors during the chip packaging process.

[0123] The charge risk determination module is responsible for predicting the charge amount generated by each process flow using a charge amount prediction model, comparing the sum of the predicted charge amounts of the upper and lower associated process flows with the risk threshold, and performing electrostatic elimination operation when the predicted charge amount of the upper and lower associated process flows is greater than the risk threshold, and continuing to execute the current process flow when it is less than the risk threshold;

[0124] The model update optimization module is used to start traceability analysis to determine the process flow to which the anomaly belongs and store related data when the deviation of the charge amount prediction value from the actual acquisition value of the sensor exceeds the threshold interval, and update the charge amount prediction model of the process flow with the related data when the number of anomalies exceeds the number threshold.

[0125] The risk threshold setting module includes a data processing unit and a threshold setting unit;

[0126] The data processing unit is used to divide the chip packaging process flow, set the sensor to collect historical electrostatic data in each process, and process the charge amount data set;

[0127] The threshold setting unit is used to set the minimum charge amount value as the risk threshold of each process flow from the charge amount data that causes chip damage due to static electricity.

[0128] The prediction model construction module includes a scatter plot drawing unit and a polynomial modeling unit;

[0129] The scatter plot drawing unit is used to mark historical sample data as scatter points and draw scatter plots with charge amount as the vertical axis and voltage as the horizontal axis for each process flow;

[0130] The polynomial modeling unit is used to construct a charge amount prediction model through a polynomial algorithm and solve the polynomial coefficients through the least squares method;

[0131] The real-time data acquisition module includes a sensing deployment unit and a voltage acquisition unit;

[0132] The sensing deployment unit is used to number and deploy sensors for each process flow to monitor the chip packaging process;

[0133] The voltage acquisition unit is used to acquire the voltage generated by each chip in each process flow using the sensor.

[0134] The charge risk determination module includes a charge prediction accumulation unit and a risk response unit;

[0135] The charge prediction accumulation unit is used to obtain the charge amount prediction value of each process flow using the prediction model according to the voltage acquired by the sensor, and add the predicted charge amounts of the two process flows associated above and below;

[0136] The risk response unit is used to perform the static electricity elimination operation when the added sum of the predicted charge amounts of the two process flows in the upper and lower correlations exceeds the charge amount risk threshold value, and to continue to perform the current process flow when the added sum is less than the charge amount risk threshold value;

[0137] The model update optimization module comprises an anomaly tracing unit and a model update unit.

[0138] The anomaly tracing unit is used to set a deviation threshold interval, and when it is determined that the predicted charge amount value does not match the actual collected value, the charge amount is stored and subjected to tracing analysis.

[0139] The model update unit is used to input the relevant data into the model to recalculate the polynomial and update the charge amount prediction model when the number of times that the predicted charge amount test value does not match the actual collected value exceeds a number threshold value.

[0140] Embodiment one: the chip packaging is divided into four process flows of wafer cutting, chip mounting, wire bonding and plastic packaging;

[0141] The historical static electricity data of each flow is collected through a sensor, and the charge amount data set of a certain flow is:

[0142] P1[5, 8, 12, 10, 15, 50, 18, 25, 30, (missing value), 16, 13, 40, (abnormal value)]

[0143] P2[3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39]

[0144] The charge amount data set of the chip damage caused by static electricity is: [28, 32, 35, 40]

[0145] The sorted data set P1 is [5, 8, 10, 12, 13, 15, 16, 18, 20, 25, 30, 40]

[0146] According to the box plot method, Q1 is 10, Q3 is 20, and IQR is 10.

[0147] The lower limit L is 5, the upper limit U is 35, and 40>35, so the charge amount is determined as an abnormal value, and the P1 data set after elimination is [5, 8, 10, 12, 13, 15, 16, 18, 20, 25, 30]

[0148] The P1 data set is filled by the missing value filling method, and the median is calculated to be 15. The final P1 data set is: [5, 8, 10, 12, 13, 15, 15, 16, 18, 20, 25, 30]

[0149] According to the minimum value, the charge amount risk threshold value is 28.

[0150] Draw a scatter plot, and the sample data of the scatter plot is: (2, 5), (3, 8), (4, 10), (5, 12), (6, 13), (7, 15), (8, 16), (9, 18), (10, 20), (11, 25), (12, 30).

[0151] For P1, the P1 model is obtained by solving the coefficients of the quadratic polynomial model by the least square method:

[0152] ;

[0153] The P2 model is:

[0154] ;

[0155] The sensor collects the chip voltage data, P1 is 5V, P2 is 7V, and the charge amount generated by P1 is 7.7 and the charge amount generated by P2 is 9.02 by substituting the model, the sum does not exceed the risk threshold, and the process flow is continued to be executed, and then the prediction judgment is performed in sequence according to the order, when the cumulative sum exceeds the risk threshold, the static electricity elimination operation is performed.

[0156] The threshold deviation interval [a, b] is set to [-1.2, 1.2], the abnormal number threshold x is 3, the actual charge amount of P1 is 8.9, the predicted value is 7.7, the deviation is 1.2, and it is determined to be abnormal.

[0157] When the abnormal number accumulates 4 times, the process flow is adjusted according to the threshold value, the new data is substituted into the polynomial algorithm to recalculate the coefficients, and the model is updated.

[0158] It is apparent to those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the present application being defined by the appended claims rather than the foregoing description, and it is intended to embrace all changes and modifications that fall within the meaning and scope of equivalents of the claims. Any reference signs in the claims should not be construed as limiting the claims to which they belong.

Claims

1. A chip transmission and packaging method based on electrostatic protection, characterized by: The chip transmission and packaging method comprises the following steps: Step S1: Divide the chip packaging process flow, set sensors in each process flow, use the sensors to collect electrostatic data of chip packaging in each process flow, extract electrostatic data of chip damage caused by static electricity, extract the charge amount, and set the charge amount risk threshold; Step S2: Based on the collected historical chip packaging electrostatic data, a scatter plot of the charge is drawn and then a polynomial algorithm is used to construct a charge prediction model for each process flow; Step S3: During the chip packaging process, use a sensor to collect the voltage values ​​generated on the chip during each process flow; Step S4: using a charge prediction model to predict the charge generated by each process flow based on the voltage of each process flow collected by the sensor, and performing a judgment based on the sum of the predicted charge amounts of the two upper and lower related process flows and comparing it with the charge risk threshold, and selecting different operations based on the judgment result; Step S5: When the predicted charge value does not conform to the actual collected charge value, a source analysis is performed on the charge anomaly, and then the charge prediction model is updated according to the source analysis result.

2. The chip transmission and packaging method based on electrostatic protection according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S1-1, dividing the chip packaging process into wafer dicing, chip mounting, wire bonding, and plastic encapsulation; Step S1-2: Setting a sensor in each process flow and using the sensor to collect historical chip packaging electrostatic data of each process flow; Step S1-3: Based on the historical electrostatic data of chip packaging, extract the electrostatic data of chip damage caused by static electricity, extract the charge amount of each process flow, and set the charge amount risk threshold for each process flow, specifically: Step S1-3-1: Construct a charge data set for each process flow ; Charge data set 、 、 The amount of charge carried by the chips processed by the 1st, 2nd, and nth process flows; Step S1-3-2: Use the box plot method to eliminate the outliers in the charge data set constructed for each process flow. The outlier determination is specifically as follows: After the charge data set is sorted in ascending order, the value at the 25% position is the first quartile; After the charge data set is sorted in ascending order, the value at the 75th percentile position is the third quartile; ； Where L is the lower threshold of the outlier, Q1 is the first quartile, and IQR is the interquartile range; ； Where U is the upper threshold of the outlier, Q3 is the third quartile, and IQR is the interquartile range; When the charge amount is less than the lower threshold or the charge amount is greater than the upper threshold, the charge amount data is determined to be an abnormal value; Step S1-3-3: using the median filling method to fill in the missing values ​​of the charge data set constructed for each process flow; Step S1-3-4: setting the minimum charge value in the charge data set of chip damage caused by static electricity as the risk threshold.

3. The chip transmission and packaging method based on electrostatic protection according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1: For each independent process flow of chip packaging, mark all processed historical sample data as scattered points in the coordinate system with charge as the vertical axis and voltage as the horizontal axis, and draw a scatter plot for each process flow; Step S2-2, set the process flow to P, for each process flow P i , the charge prediction model is constructed using the polynomial algorithm, specifically: ； Where, is the i-th process P i The predicted charge, 、 、 ,..., Process P i The polynomial coefficients are: V is the given voltage; Step S2-3: Solve the polynomial coefficients using the least squares method, specifically: ； In the formula, min is the minimum value of the sum of squared errors, is the cumulative error of all historical samples of the i-th process, is the error term, is the actual measured charge of the jth chip in the i-th process flow, To predict the charge; Step S2-4: Calculate the polynomial coefficients of the model using the least squares method, and then input the polynomial coefficients into the algorithm model to construct a charge prediction model.

4. The chip transmission and packaging method based on electrostatic protection according to claim 3, characterized in that: The specific steps of step S3 are as follows: Step S3-1: number the sensors for each process of chip packaging and deploy sensor monitoring in each process; Step S3-2: using sensors to collect the voltage of each chip in each process flow and the amount of charge generated when the process flow performs chip packaging operations on the chip.

5. The chip transmission and packaging method based on electrostatic protection according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1: When the chip starts the chip packaging operation, the voltage value of the first process flow is collected by the sensor in the first process flow, and the charge amount generated by the first process flow is predicted according to the voltage value using the charge amount prediction model; Step S4-2: After each process is completed, at the beginning of the next process, the voltage value of the chip at the beginning of the next process is collected, and the charge value at the beginning of the process is predicted using the charge value prediction model based on the voltage value. The charge value predicted at the beginning of the process is added to the charge value predicted by the previous process, and the sum is compared with the charge value risk threshold for determination; Step S4-3, repeat the operation in the order of the process flow to obtain the amount of charge accumulated in each process flow and make a judgment. When the amount of charge accumulated in the corresponding process flow is greater than the charge risk threshold, perform the static elimination operation. When it is less than the risk threshold, continue to execute the current process flow.

6. The chip transmission and packaging method based on electrostatic protection according to claim 5, characterized in that: The specific steps of step S5 are as follows: Step S5-1: Professionals set a deviation threshold range [a, b] between the predicted charge amount and the actual charge amount according to the business scenario; Step S5-2: When the deviation between the predicted charge value and the actual collected charge value does not meet the threshold range, it is determined that the charge prediction model is abnormal, and the traceability analysis process is started; Step S5-3: Collect the charge and the voltage corresponding to the charge, determine the process flow to which the abnormality belongs by the sensor number, and then store the collected abnormal charge value and the corresponding voltage; Step S5-4. Professionals set a number threshold x based on the business scenario. When the number of times that the charge prediction value and the actual collected charge value do not meet the threshold interval exceeds x, it is determined to be a process adjustment. The charge and voltage values ​​are input into the charge prediction model constructed by the polynomial algorithm, and the polynomial coefficients of the charge prediction model of the process are recalculated. Finally, the polynomial coefficients are substituted into the charge prediction model constructed by the polynomial algorithm to obtain a new charge prediction model.

7. A chip transmission and packaging system based on electrostatic protection, characterized by: The chip transmission and packaging system includes a risk threshold setting module, a prediction model building module, a real-time data acquisition module, a charge risk determination module, and a model update optimization module; The risk threshold setting module is used to divide the chip packaging process flow, collect historical electrostatic data and set the charge risk threshold; The prediction model building module is used to build a charge prediction model based on historical electrostatic data; The real-time data acquisition module is used to number and deploy sensors for each process flow during the chip packaging process, and collect the voltage of each chip in each process flow and the charge generated by the packaging operation through the sensors; The charge risk determination module is responsible for predicting the charge generated by each process flow using a charge prediction model, comparing the sum of the predicted charge amounts of the upper and lower related process flows with the risk threshold, and performing static elimination when the predicted charge amounts of the upper and lower related process flows are greater than the risk threshold; otherwise, the current process flow continues to execute; The model update optimization module is used to start tracing analysis to determine the process flow to which the anomaly belongs and store relevant data when the deviation between the charge quantity prediction value and the actual collected value of the sensor exceeds a threshold range. When the number of anomalies exceeds a threshold, the charge quantity prediction model of the process flow is updated with relevant data.

8. The chip transmission and packaging system based on electrostatic protection according to claim 7, characterized in that: The risk threshold setting module includes a data processing unit and a threshold setting unit; The data processing unit is used to divide the chip packaging process flow, set sensors in each process to collect historical electrostatic data, and process the charge data set; The threshold setting unit is used to set the minimum charge value as the risk threshold of each process flow from the charge amount data of the chip damaged by static electricity.

9. The chip transmission and packaging system based on electrostatic protection according to claim 7, characterized in that: The prediction model building module includes a scatter plot drawing unit and a polynomial modeling unit; The scatter plot drawing unit is used for marking historical sample data as scatter points and drawing a scatter plot for each process flow, with charge as the vertical axis and voltage as the horizontal axis; The polynomial modeling unit is used to construct a charge quantity prediction model through a polynomial algorithm and solve the polynomial coefficients through a least squares method; The real-time data acquisition module includes a sensor deployment unit and a voltage acquisition unit; The sensor deployment unit is used to number and deploy sensors for each process flow to monitor the chip packaging process; The voltage acquisition unit is used to use a sensor to acquire the voltage generated by each chip in each process flow.

10. The chip transmission and packaging system based on electrostatic protection according to claim 7, characterized in that: The charge risk determination module includes a charge prediction and accumulation unit and a risk response unit; The charge prediction and accumulation unit is used to obtain the charge amount prediction value of each process flow according to the voltage collected by the sensor using the prediction model, and add the charge amounts predicted by the two process flows that are connected vertically; The risk response unit is used to perform an electrostatic elimination operation when the sum of the charge amounts predicted by the two process flows associated with each other exceeds a charge amount risk threshold, and to continue to perform the current process flow when the sum is less than the charge amount risk threshold; The model update optimization module includes an anomaly tracing unit and a model update unit; The abnormality tracing unit is used to set a deviation threshold interval, and when it is determined that the charge amount prediction value is inconsistent with the actual collected value, the charge amount is stored and traced and analyzed; The model updating unit is used to input relevant data into the model to recalculate the polynomial and update the charge quantity prediction model when the number of times that the charge quantity pre-test and the actual collected value are inconsistent exceeds a threshold value.

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