Real-time monitoring and optimizing method and system for intelligent gold bonding wire driven by Internet of Things

Abstract

The invention relates to the technical field of advanced process control, in particular to an intelligent gold bonding wire real-time monitoring and optimizing method and system driven by the Internet of Things, and the method comprises the following steps: collecting the pressure and temperature data of a gold bonding wire, and obtaining the real-time data of the pressure and temperature of the gold wire; and based on the real-time data of the gold wire pressure and temperature, data verification is carried out, abnormal values are eliminated, and a preliminary data verification result is generated. The pressure and temperature data of the gold bonding wire are collected in real time, abnormal values are recognized and eliminated in time, the reliability of the data is guaranteed, and the accuracy of data analysis is improved; on the basis that reliable data are obtained, real-time data are compared with standard parameters, the deviation degree is rapidly positioned, and precise monitoring of the gold wire bonding process is achieved; furthermore, according to the deviation analysis result, the pressure and temperature parameters of the bonding equipment are adjusted in time, so that the actual production condition better meets the technological standard requirement.

Description

Technical Field

[0001] The present invention relates to the field of advanced process control technology, and in particular to a method and system for real-time monitoring and optimization of intelligent bonding wires driven by the Internet of Things. Background Art

[0002] Advanced process control is a comprehensive technology system for real-time monitoring, analysis, optimization, and control of complex, multivariable, strongly coupled, and nonlinear processes in industrial production. The real-time gold bonding wire monitoring and optimization method is an intelligent control method used to monitor and optimize the gold wire bonding quality during chip bonding.

[0003] In actual production operations, existing technologies often only perform simple monitoring and optimization of the pressure and temperature of the bonding wires. They lack verification and troubleshooting of anomalies in real-time data. This results in some abnormal data not being identified promptly, and erroneous data being included in the analysis process, leading to biased analysis results. During the monitoring and analysis process, there is also no mechanism for accurately comparing real-time data with standard thresholds, making it difficult to accurately and quickly identify fluctuations in key parameters, delaying the timing of parameter adjustments. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose an Internet of Things-driven intelligent bonding wire real-time monitoring and optimization method and system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an IoT-driven intelligent gold bonding wire real-time monitoring and optimization method, comprising the following steps:

[0006] Collecting pressure and temperature data of the bonding gold wire to obtain real-time data of the gold wire pressure and temperature; performing data verification based on the real-time data of the gold wire pressure and temperature, eliminating abnormal values, and generating preliminary data verification results;

[0007] Based on the preliminary data verification results, analyzing the deviation between the gold wire pressure and temperature and the standard parameters, performing data comparison, comparing the real-time pressure and temperature data with the set standard thresholds, determining the degree of deviation, and generating a deviation analysis result; based on the deviation analysis results, adjusting the pressure and temperature settings of the bonding machine and generating a parameter adjustment result;

[0008] Based on the parameter adjustment results, continuously monitoring the adjusted gold wire bonding quality to verify whether the gold wire bonding meets the production standards and generate a continuous quality monitoring result; based on the continuous quality monitoring results, if a quality degradation is detected, fine-tuning the parameters again to generate a readjustment result;

[0009] Based on the readjustment results, all operational data are integrated, all monitoring data and adjustment records are summarized, statistical analysis is performed on the data, critical control points and potential risks are identified, and comprehensive analysis results are generated.

[0010] Preferably, the step of acquiring the real-time data of the gold wire pressure and temperature is: installing pressure and temperature sensors on the production line, continuously monitoring the pressure and temperature of the bonding gold wire, and obtaining the real-time data of the pressure and temperature of the bonding gold wire;

[0011] Data formatting is performed based on the real-time data of pressure and temperature of the bonding gold wire to obtain the real-time data of pressure and temperature of the gold wire.

[0012] Preferably, the step of obtaining the preliminary data verification result is: based on the real-time data of the gold wire pressure and temperature, checking the integrity and accuracy of the data, eliminating erroneous data introduced by equipment failure or environmental factors, and obtaining the pressure and temperature data after cleaning;

[0013] According to the pressure and temperature data after cleaning, the deviation index of each data point is calculated using the following formula:

[0014]

[0015] in, is the pressure value of a single data point, is the mode of the pressure data, is the range of pressure data, is the temperature value of a single data point, is the mode of the temperature data, is the range of temperature data, ZD is the deviation index;

[0016] Based on the deviation index, data points with deviation indexes higher than a threshold are eliminated to obtain preliminary data verification results.

[0017] Preferably, the step of obtaining the deviation analysis result comprises: based on the preliminary data verification result, obtaining the data change trend by calculating the second-order derivatives of adjacent data points, screening the fluctuating data points, calculating the change rate of the fluctuating data points, and obtaining the pressure data and temperature data for the deviation analysis;

[0018] The degree of deviation is calculated based on the pressure data and temperature data used for deviation analysis. The calculation formula is:

[0019]

[0020] in, is the second-order derivative of pressure, is the second derivative of temperature, P max is the maximum pressure, Pmin is the minimum value of pressure, P mid is the median pressure, T max is the maximum temperature, T min is the minimum temperature, T mid is the median temperature, and X is the degree of deviation;

[0021] Based on the degree of deviation, determine whether the deviation continues to exceed the set threshold, screen key abnormal areas, and generate deviation analysis results.

[0022] Preferably, the step of obtaining the parameter adjustment result is: based on the deviation analysis result, calculating the adjusted pressure and temperature setting values, the calculation formula is:

[0023]

[0024] and

[0025]

[0026] Among them, P cur and T cur is the current pressure and temperature setting value of the bonding machine, ΔP and ΔT are the deviation values ​​of pressure and temperature respectively, V P and V T are the real-time pressure and temperature change rates, V Pstd and V Tstd is the standard pressure and temperature change rate, A P and A T is the acceleration of pressure and temperature, A Pstd and A Tstd is the acceleration at standard pressure and temperature, P new and T new are the adjusted pressure and temperature set points;

[0027] Apply the adjusted pressure and temperature settings to the bonding machine, track the equipment's operating status in real time, and continuously measure product quality data to generate parameter adjustment results.

[0028] Preferably, the step of obtaining the continuous quality monitoring result is: calculating the quality index based on the parameter adjustment result, and the calculation formula is:

[0029]

[0030] Among them, H P is the instantaneous harmonic energy of pressure data, H T is the instantaneous harmonic energy of temperature data, Γ is the envelope curve area of ​​ultrasonic signal, Ψ is the optical profile curvature of bonding point, L is the linear deformation of bonding point, S is the solid diffusion area of ​​bonding point, EQ is the comprehensive quality index;

[0031] Based on the quality indicators, the stability of the production process is judged and the product quality is evaluated, and the product is verified to meet the quality requirements, forming a continuous quality monitoring result.

[0032] Preferably, the step of obtaining the readjustment result comprises: extracting pressure, temperature, vibration signals and optical detection data based on the continuous quality monitoring results, analyzing the fluctuation trend of the data over time, calculating the variation range of each data within the target time window, screening out abnormal variation areas, locating the key influencing factors leading to quality degradation by comparing process parameters, and generating quality degradation data analysis results;

[0033] Based on the analysis results of the quality degradation data, determine the persistence and mutation of key influencing factors, analyze the linkage effects of key influencing factors on pressure, temperature and vibration amplitude, and generate parameter fine-tuning plans;

[0034] Based on the parameter fine-tuning scheme, the current process parameters are called, the adjusted pressure and temperature are calculated, and the readjustment results are obtained.

[0035] Preferably, the step of obtaining the comprehensive analysis result comprises: extracting all operation data, including bonding machine pressure, temperature, vibration amplitude and adjustment records, based on the readjustment result, sorting them in time series, and establishing an operation data set;

[0036] Based on the operational data set, the critical control points are calculated using the following formula:

[0037]

[0038] Where P is pressure, Q is vibration amplitude, T is temperature, M is real-time energy consumption, A is maximum fluctuation range, B is minimum fluctuation range, C is the number of continuous adjustments, D is the cumulative amount of past adjustments, and K is the critical control point;

[0039] Based on the critical control points, potential risk points are screened, major risk sources are identified, and comprehensive analysis results are generated.

[0040] The present invention provides an optimization system, comprising:

[0041] The data acquisition module collects data from the pressure sensor and temperature sensor of the gold wire bonding equipment, synchronizes the time, records the real-time data transmission status, and generates data acquisition results;

[0042] The data verification module receives the raw data from the data acquisition results, uses statistical analysis to eliminate data points that exceed the normal working range, synthesizes the verified pressure and temperature data, and generates verification data results;

[0043] The deviation analysis module analyzes the data in the calibration data results, calculates the deviation degree of each data point by comparing the standard parameters, compares the difference value with the standard threshold, and generates the deviation analysis results;

[0044] The parameter adjustment module adjusts the pressure and temperature parameters of the bonding machine in real time based on the deviation analysis results, records each step of the adjustment, and generates parameter adjustment results;

[0045] The quality monitoring module continuously runs the production line using adjusted parameters, collects product quality data in real time, compares it with production standards, confirms that quality meets the standards, and generates continuous quality monitoring results.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] The present invention collects the bonding wire pressure and temperature data in real time, promptly identifies and eliminates abnormal values, ensures the reliability of data, and improves the accuracy of data analysis; on the basis of obtaining reliable data, compares the real-time data with the standard parameters, quickly locates the degree of deviation, and realizes precise monitoring of the gold wire bonding process; further, according to the deviation analysis results, timely adjusts the pressure and temperature parameters of the bonding equipment to make the actual production conditions more in line with the process standard requirements; after the adjustment, it continuously monitors the quality change trend of the gold wire bonding to continuously ensure that the quality meets the production standard requirements, and makes fine adjustments at any time to correct deviations in time to avoid the production of defective products due to parameter fluctuations; in the long-term operation process, it also integrates and statistically analyzes all monitoring data and adjustment records to identify and lock key control nodes and potential risks, form a targeted risk prevention mechanism, improve the stability and reliability of the chip packaging production process, and achieve improved packaging yield and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] See also Figure 1 The present invention provides a technical solution, an IoT-driven intelligent gold bonding wire real-time monitoring and optimization method, comprising the following steps:

[0051] Collect the pressure and temperature data of the bonding wire and obtain real-time data of the wire pressure and temperature; perform data verification based on the real-time data of the wire pressure and temperature, eliminate abnormal values, and generate preliminary data verification results;

[0052] Based on the preliminary data verification results, analyze the deviation between the gold wire pressure and temperature and the standard parameters, perform data comparison, compare the real-time pressure and temperature data with the set standard thresholds, determine the degree of deviation, and generate deviation analysis results; based on the deviation analysis results, adjust the pressure and temperature settings of the bonding machine and generate parameter adjustment results;

[0053] Based on the parameter adjustment results, the quality of the adjusted gold wire bonding is continuously monitored to verify whether the gold wire bonding meets the production standards and generate continuous quality monitoring results. Based on the continuous quality monitoring results, if a quality decline is detected, the parameters are fine-tuned again to generate readjustment results.

[0054] Based on the readjustment results, all operational data are integrated, all monitoring data and adjustment records are summarized, statistical analysis is performed on the data, critical control points and potential risks are identified, and comprehensive analysis results are generated.

[0055] The steps for obtaining real-time data of gold wire pressure and temperature are as follows: installing pressure and temperature sensors on the production line to continuously monitor the pressure and temperature of the bonding gold wire and obtain real-time data of the pressure and temperature of the bonding gold wire;

[0056] According to the real-time data of pressure and temperature of the bonding gold wire, data formatting is performed to obtain the real-time data of pressure and temperature of the gold wire.

[0057] Specifically, after installing pressure and temperature sensors on the production line, based on past process experience and comparison of laboratory test data, the pressure range is initially set to 0MPa to 2MPa, and the temperature range is initially set to 0℃ to 90℃. If the pressure or temperature in a real-time acquisition record exceeds the range, the record is marked as abnormal and the reason is recorded. Then, the pressure and temperature values ​​are acquired at a frequency of ten times per second and each record is timestamped. A continuous data table is then established for subsequent analysis. In order to determine whether there are subtle abnormal fluctuations, a monthly record is selected. The rationality of the two intervals of 0MPa to 2MPa and 0℃ to 90℃ is corrected by comparing the abnormal record ratio obtained by comparison. For example, when the average pressure in actual production is close to 1.8MPa and is as high as 2.1MPa in some periods, the upper threshold needs to be temporarily increased to 2.2MPa or greater. The adjustment basis of this threshold is based on the rated tolerance range of the production line equipment and the historical abnormality ratio. The temperature parameter is then corrected in the same way. While obtaining massive sensor values, in order to judge possible anomalies, a three-layer fully connected neural network can be used. The network recognizes data and outputs abnormal labels. The input of this neural network includes the pressure, temperature, and time interval sequence of each record. The hidden layer consists of 120 neurons and uses ReLU as the activation function. The output layer gives a binary classification result based on whether it is abnormal. During training, sensor data that has been running continuously for two weeks is first collected, and high temperature and high pressure situations that have occurred are marked. The loss function is set to cross entropy and the weights are iterated using stochastic gradient descent with a learning rate of 0.001. In the specific calculation, each input data is first normalized, and then the activation value is calculated layer by layer by matrix multiplication in the forward propagation. The predicted label is obtained at the output layer, and the loss value is obtained by comparing the predicted result with the true label. The weight is then updated layer by layer during back propagation. After about 100 rounds of iteration, if the accuracy of the verification set stabilizes at around 98%, the training is stopped. In the actual inference stage, the new real-time pressure and temperature information is input into the network, first undergoing the same normalization process, and then performing matrix calculation according to the determined weights and biases. Finally, an abnormal or normal judgment label is output. If the label is abnormal, the records of the corresponding time period are separately marked. Based on the above process, the real-time pressure and temperature data of the bonding wire are finally obtained.

[0058] According to the real-time data of pressure and temperature of the bonding wire, first, according to the established continuous data table, add an index number to each record and compare it with the range of 0MPa to 2MPa and 0℃ to 90℃. If it is found that the pressure in a certain record is greater than 2MPa or the temperature is greater than 90℃, add a column marked "overlimit" after the record and record the specific amount of pressure or temperature increase; then, by re-checking all the records marked as "overlimit", if it is confirmed in actual operation that the instantaneous working pressure of the equipment can reach 2.2MPa for a short time without abnormality, the standard value can be raised to 2.2MPa and the range can be re-marked in the record with reference to the previous threshold adjustment strategy; then, the timestamp and pressure and temperature values ​​of the records that do not exceed the limit are added. The data is normalized and converted into serial data in a unified format. The format contains a unified structure: the timestamp is recorded in a six-digit format of year-month-day-hour-minute-second, the pressure is retained to three decimal places, the temperature is retained to one decimal place, and the corresponding analysis label (such as normal, adjusted) is added after each line; in this process, if it is necessary to use certain existing calculation methods to determine whether there is a volatility risk, the absolute value of the difference can be calculated when comparing adjacent records and a fluctuation baseline value can be set. For example, if the pressure changes by more than 0.3MPa within one minute or the temperature changes by more than 10℃ within the same time period, the record will be marked as "excessive fluctuation"; after integrating all the sorted data, the real-time data of the gold wire pressure and temperature are finally obtained.

[0059] The steps for obtaining the preliminary data verification results are as follows: based on the real-time data of gold wire pressure and temperature, check the integrity and accuracy of the data, eliminate erroneous data introduced by equipment failure or environmental factors, and obtain the pressure and temperature data after cleaning;

[0060] According to the pressure and temperature data after cleaning, the deviation index of each data point is calculated using the following formula:

[0061]

[0062] in, is the pressure value of a single data point, is the mode of the pressure data, is the range of pressure data, is the temperature value of a single data point, is the mode of the temperature data, is the range of temperature data, ZD is the deviation index;

[0063] Based on the deviation index, data points with deviation indicators higher than the threshold are eliminated to obtain preliminary data verification results.

[0064] Specifically, based on the real-time data of gold wire pressure and temperature, first refer to the pressure applicable range and temperature applicable range listed in the equipment technical documentation, compare the pressure in the range of 0MPa to 2MPa, and the temperature in the range of 0℃ to 90℃, and mark the timestamp and specific value in each record respectively. Then, by comparing the pressure and temperature changes collected in each period of a week, a threshold setting information for calibration is obtained. The information will record the minimum and maximum pressure values ​​and the minimum and maximum temperature values ​​per hour, and use this as a reference to check whether there are extreme abnormal values ​​in the data due to collection equipment failure or external environmental fluctuations. Then, manual inspections are carried out on the data marked as abnormal and the surrounding environment of the sensor is checked. When the inspection data confirms that the record is indeed from an invalid working condition, it is removed and the fault type is noted. At the same time, if multiple adjacent records are found to be higher than 2MPa or higher than 9 If the temperature is 0℃, refer to the equipment nameplate and historical operation data to check whether the period is in a transient overload state. If it is confirmed that the period was in normal production conditions, the new upper limit threshold is determined based on the existing historical operation data and updated to the current range. For example, when the historical operation data shows that the equipment can maintain stable operation when the pressure reaches 2.3MPa within four seconds, the upper limit threshold is updated to 2.3MPa for subsequent comparison, and the temperature upper limit is updated using the same method. After completing the above steps, all data are reorganized into a standard format table, including columns such as timestamp, pressure value, temperature value, and whether it is abnormal. Then, the invalid records are stripped off and the remaining valid records are summarized. If there is a short gap in certain time periods, the corresponding reason is noted in the record and no further filling operation is performed. Then, all verified and qualified records are stored in the database and the final result data is sorted out to finally obtain the pressure and temperature data after cleaning.

[0065] The benefit of this formula is that it quantifies the degree to which each data point deviates from the center of the overall pressure and temperature distribution through the above-mentioned deviation index. After comprehensively considering the pressure and temperature fluctuations, the range in the denominator is used to adjust the measurement standard, thereby providing a more intuitive numerical measurement method when anomalies are found.

[0066] ZP i Steps to obtain: In actual working conditions, ZP i Represents a single pressure value under a time series index. This value is obtained by collecting the instantaneous pressure from the pressure sensor. For example, if the value is collected once every minute within 24 hours, there will be a total of 1440 records, corresponding to ZP1 to ZP 1440 Assume that the pressure value of a record collected at the time stamp of 10:30 is 1.245 MPa, then the value corresponds to ZP 600 And stored in MPa, through the above process for each ZP iConstruct specific reference values.

[0067] ZP mode Steps to obtain: ZP mode It is the mode of the pressure data, indicating the pressure value that appears the most times among all valid data points. To obtain it, you need to first count all ZP i The possible numerical segments included, for example, with 0.001MPa as the resolution accuracy, in the previous 48 hours of records, the frequency of occurrence of each valid data value is accumulated and summarized into a pressure frequency distribution table, and then the pressure value with the highest number of occurrences is found in the table, which is the ZP mode For example, among 1440 valid records, the statistics show that 1.200MPa appears 180 times, 1.205MPa appears 179 times, and 1.245MPa appears 179 times. Then 1.200MPa, which appears the most times, can be used as ZP. mode , and incorporate it into subsequent calculations.

[0068] ZP range Steps to obtain: ZP range It is the range of pressure data, which means the difference between the maximum pressure value and the minimum pressure value in the current data set. To obtain this value, you first need to get the value from all the ZP values ​​that have been verified. i Find the minimum and maximum pressures in the equation. For example, if the minimum value monitored during 48 hours of normal production line operation is 0.950 MPa and the maximum value is 1.280 MPa, then ZP can be obtained by calculation. range =1.280-0.950=0.330MPa.

[0069] ZT i Steps to obtain: ZT i It is the temperature value of a single data point. For example, in a 24-hour continuous monitoring, 1440 records are collected and marked in sequence as ZT1 to ZT 1440 For example, if a record shows 78.3 degrees Celsius at 13:45, then ZT is written. 825 It is 78.3 degrees Celsius.

[0070] ZT mode Steps to obtain: ZT mode is the mode of the temperature data, and the acquisition process is the same as ZP mode Similarly, first all ZT i Count the number of occurrences at a resolution of 0.1 degrees Celsius, list the temperature values ​​and the corresponding occurrence frequencies to form a temperature distribution table, and find the temperature value with the highest occurrence frequency as ZT modeFor example, when 78.3 degrees Celsius appears 165 times, 78.6 degrees Celsius appears 161 times, and 79.0 degrees Celsius appears 166 times during 24-hour monitoring, 79.0 degrees Celsius is selected.

[0071] ZT range Steps to obtain: ZT range It is the range of temperature data. It is necessary to find the maximum and minimum temperature values ​​from the temperature collection in the same period. The difference between the two is the range. For example, in the normal operation of a 24-hour production line, the minimum temperature is 72.0 degrees Celsius and the maximum temperature is 80.1 degrees Celsius. Then ZT range =80.1-72.0=8.1.

[0072] Calculation process:

[0073] Assume that the total amount of data is n=3 as an example, and take three groups of sampling points, namely:

[0074] Group 1: ZP1 = 1.210 MPa, ZT1 = 78.2 degrees Celsius;

[0075] Group 2: ZP2 = 1.200 MPa, ZT2 = 79.0 degrees Celsius;

[0076] Group 3: ZP3 = 1.225 MPa, ZT3 = 78.7 degrees Celsius;

[0077] Assume that the previous statistics are: ZP mode =1.200MPa, ZP range =0.330MPa, ZT mode =79.0 degrees Celsius, ZT range =8.1 degrees Celsius, then calculate for the first set of sampling points:

[0078]

[0079] Calculate for the second set of sampling points:

[0080]

[0081] Calculate for the third set of sampling points:

[0082]

[0083] The sum of the three groups:

[0084]

[0085] Then: The results show that for the above three sets of records, based on the accumulation of these pressure and temperature deviation values, the comprehensive deviation index is 0.4918. When this value is compared with other batch sampling points, it is easier to identify the record or record set with the largest deviation. If the index is higher than a certain threshold, the corresponding data point can be excluded.

[0086] Based on the deviation index obtained above, all sampling points that have completed the calculation are compared, and thresholds are set in the specific implementation to determine which records have deviation indicators that are higher than the reference standard range. Then, by checking the distribution of the deviation index during one week of continuous production, an interval distribution diagram is decomposed. In this distribution diagram, the main peaks and edge parts of the concentrated distribution of the deviation index are first observed. For example, when the sampling volume is 5,000, it is found through sorting that most of the deviation indicators are lower than 0.35 and a small number of recorded deviation indicators are between 0.36 and 0.38. There are also very few data points with deviation indicators exceeding 0.40. Therefore, the equipment operation log and environmental records are combined to search for short-term fluctuations. For those exceeding 0.4 For the part with a value of 0, we first try to use 0.40 as the initial reference threshold and mark it one by one, and then adjust it to 0.42 or higher according to the actual inspection results. If it is found that abnormal working conditions do occur in many periods during the sampling process, the threshold is temporarily revised to 0.45. In this way, the deviation index of each record is automatically compared and marked, and each record is recorded to see whether it has exceeded the current threshold. Then, all records marked as exceeding the threshold are read again in the software, and interactively viewed one by one with the pressure distribution and temperature distribution. When it is confirmed to be abnormal, it is removed from the data set for subsequent analysis until all deviation indicators remain within the specified range and no significant deviation occurs. The data after removal is then integrated and updated to the database to finally obtain the preliminary data verification results.

[0087] The steps for obtaining the deviation analysis results are as follows: based on the preliminary data verification results, obtain the data change trend by calculating the second-order derivatives of adjacent data points, filter the fluctuating data points, calculate the change rate of the fluctuating data points, and obtain the pressure data and temperature data used for deviation analysis;

[0088] Based on the pressure data and temperature data used for deviation analysis, the deviation degree is calculated using the following formula:

[0089]

[0090] in, is the second-order derivative of pressure, is the second derivative of temperature, P max is the maximum pressure, P min is the minimum value of pressure, P mid is the median pressure, T max is the maximum temperature, Tmin is the minimum temperature, T mid is the median temperature, and X is the degree of deviation;

[0091] Based on the degree of deviation, determine whether the deviation continues to exceed the set threshold, screen key abnormal areas, and generate deviation analysis results.

[0092] Specifically, based on the preliminary data verification results, it is first necessary to read the pressure and temperature data one by one in the continuous time series, and when reading two adjacent data, calculate the numerical changes between adjacent points by recording the difference between the pressure change and the temperature change over time. Then, after performing a discrete derivative on the difference in each time interval, the obtained first derivative result is compared with the subsequent two adjacent data points, thereby forming an approximate value of the second-order derivative in a limited sampling interval. The second-order derivative represents the acceleration or deceleration of each monitored quantity in a small time interval. Corresponding to the actual production environment, it is necessary to sequentially calculate the second-order derivatives of all adjacent sampling points in a piece of operating data spanning at least 48 hours, and then record these second-order derivatives one by one and compare them with the pre-set fluctuation judgment benchmark. For example, the pressure fluctuation range recorded in the equipment technical manual is from 0MPa to 2MPa, and the temperature fluctuation range is from 0℃ to 90℃. According to the maximum acceleration counted from multiple accumulated equipment operation experiences, A reference value is set for the pressure and temperature fluctuations to distinguish between normal and excessive fluctuations. When the second-order derivative of a record exceeds this reference value in the pressure or temperature direction, the record is marked as a "fluctuation data point." The rate of change of pressure and temperature over several consecutive seconds for these marked fluctuation data points is then analyzed. The instantaneous difference can be calculated by collecting data once per second and compared with the average rate of change over the same time period. If the magnitude of the change exceeds the average rate of change threshold, for example, a pressure change exceeding 0.5 MPa / s or a temperature change exceeding 5.0°C / s, the data point is marked as "high fluctuation" and listed in a table for verification. After completing the above process, a batch of pressure and temperature data for deviation analysis can be generated and distinguished from the previously verified overall sequence. This facilitates subsequent in-depth testing of this area where abnormal fluctuations may occur, resulting in pressure and temperature data for deviation analysis.

[0093] The benefit of this formula is that it can simultaneously measure the fluctuation amplitude of pressure and temperature at the acceleration level, and comprehensively reflect the degree of discreteness in the production process by taking into account the ratio of the overall range to the median. If the value of the second-order derivative is large and the ratio of the range to the median is also large, the result will be significantly improved, thereby characterizing the deviation in multiple dimensions.

[0094] The acquisition steps are as follows: First, a series of pressure values ​​are recorded on the device by collecting pressure once per second, marked as P1, P2, ..., P n , then use discrete difference to approximate the derivative between every two adjacent pressure data, and continue to differentiate the first-order derivative sequence to form a second-order derivative sequence. In order to make the data more accurate, it can be collected during 72 hours of continuous production line operation to obtain a pressure record containing 259,200 data points (72 hours * 3600 seconds). In order to obtain a stable second-order derivative, any continuous several segments are selected as time windows for differentiation. When it is necessary to calculate When , the first-order derivative is recorded as Where Δt is 1 second, and then ΔP k Do differential calculations Finally, the second-order derivative sequence is obtained and the time index is marked. 43,200 pressure data points are extracted within 12 hours of stable operation. After successive differentiation, a first-order derivative sequence of 43,200-1=43,199 values ​​is obtained. Then, the differentiation is continued to generate 43,198 second-order derivative values. If the calculated result at a certain time point is -0.012 MPa / s2, it means that the pressure at that moment is decreasing positively at the acceleration level. If multiple consecutive values ​​show a positive decrease or increase, they need to be marked and compared with the reference range obtained previously (for example, in the statistical data, it is found that the second-order derivative under normal conditions is approximately distributed in the range of -0.020 MPa / s2 to 0.020 MPa / s2) to evaluate whether its value is too large or too small, so as to determine The specific value of .

[0095] The acquisition steps are as follows: similarly to temperature detection, obtain the temperature sequence T1, T2, ..., T with a sampling interval of seconds. n , and first calculate the first-order derivative Then differentiate the first-order derivative Get the second-order derivative In a 24-hour full-load operation, 86,400 temperature values ​​can be collected, forming a numerical sequence. Since it has been confirmed that the normal second-order derivative distribution is between -0.70℃ / sec2 and 0.70℃ / sec2, it is necessary to determine that it belongs to the larger positive acceleration range.

[0096] P max The steps to obtain P are: maxRefers to the maximum pressure. It is necessary to search all valid pressure data within the same period and select the one with the highest value. The data of the previous 72 hours or longer can be used to confirm. If the statistical maximum value is 1.40MPa, it can be defined as P max .

[0097] P min The steps to obtain P are: min With P max To obtain the minimum value, we need to retrieve a long period of data within the range covered by the normal production log, exclude the extremely low pressure at the beginning of startup or during shutdown, and compare it with the lowest value during continuous operation. If 0.95MPa is found as the lowest stable operating point, it will be selected as P min And mark it out.

[0098] P mid The steps to obtain P are: mid The median pressure is determined by sorting all valid pressure data and selecting the one in the middle after sorting, or by taking the average of the two middle values ​​when the amount of data is even. This operation needs to be performed on the screened stable working condition data. Assuming that there are 10,000 data points in the obtained valid pressure set, after sorting them from small to large, the average of the 5000th and 5001st pressure values ​​is P. mid , if the 5000th value is 1.12MPa and the 5001st value is 1.13MPa, then

[0099] T max The steps to obtain T are: max Refers to the maximum temperature. If the temperature peak is found to be stable at 85.6°C during the 720-hour long period statistics, this value is taken as T max And mark it out.

[0100] T min The steps to obtain T are: min Indicates the minimum temperature. If the lowest temperature is 72.5℃ under normal working conditions after inspection, select it as T min .

[0101] T mid The steps to obtain T are: mid Is the median temperature. If there are 15,000 valid temperature values ​​in the statistical data, then take the average of the 7500th and 7501th data. Assuming that these two temperatures are 78.2℃ and 78.3℃ respectively, we can get

[0102] Calculation process:

[0103] Substitute into the formula:

[0104]

[0105] For example, let P max =1.40MPa, P min =0.95MPa, P mid =1.125MPa, T max =85.6℃, T min =72.5℃, T mid =78.25℃.

[0106] First calculate the absolute difference:

[0107] |P max -P min |=1.40-0.95=0.45MPa;

[0108] |T max -T min |=85.6-72.5=13.1℃;

[0109] Then divide by the median:

[0110]

[0111] Add to brackets:

[0112] 1+0.40+0.1675=1.5675;

[0113] Then calculate the sum of squares of the second derivatives:

[0114] (-0.008) 2 +(0.50) 2 =0.000064+0.25=0.250064;

[0115] Find the square root:

[0116]

[0117] The product of the value in the previous step is: X = 0.500064 × 1.5675 ≈ 0.7835. This result shows that when the absolute value of the second-order derivative is relatively small and the ratio of the range to the median is moderately high, a medium-level deviation value of 0.7835 will be obtained. For this period of time series data, if it is significantly larger than that of other time periods, it means that the fluctuation of the current period is relatively obvious. If this value exceeds the established threshold, such as 1.0 or 1.2 (this threshold needs to be combined with the actual production environment and the historical distribution of equipment, and confirmed through multiple statistics), it means that the pressure and temperature deviations in this period are relatively high, and further investigation of the production process or equipment status is required.

[0118] Based on the degree of deviation, each record containing second-order derivative information is read and sorted, and the numerical distribution is retrieved. During the retrieval, the obtained second-order derivative is matched one-to-one with the degree of deviation obtained from the previous formula. The deviation value is compared with the compiled deviation threshold, and records with a deviation greater than the threshold are marked in the database. The corresponding timestamps and numerical distributions are then extracted from these records. A volatility list is created and compared with each time period in the production log. By comparing the continuity of the acceleration distribution within a short period of time, it is counted whether there are multiple adjacent data points exceeding the threshold. If the comparison reveals significant deviation for three or more consecutive records, this section of persistent high deviation is marked as a key anomaly area. After confirmation with the operator, the field test logs or sensor links for this period are verified. If there is a loose sensor or poor contact, a corresponding explanation is added to the cause list. Otherwise, the possible cause of the significant increase in deviation in this section of data is further identified during the integration of the equipment load change data. After all the markings are completed, the key anomaly areas can be summarized and categorized in the final aggregation step to generate the deviation analysis results.

[0119] The steps to obtain the parameter adjustment results are: based on the deviation analysis results, calculate the adjusted pressure and temperature set values. The calculation formula is:

[0120]

[0121] and

[0122]

[0123] Among them, P cur and T cur is the current pressure and temperature setting value of the bonding machine, ΔP and ΔT are the deviation values ​​of pressure and temperature respectively, V P and V T are the real-time pressure and temperature change rates, V Pstd and V Tstdis the standard pressure and temperature change rate, A P and A T is the acceleration of pressure and temperature, A Pstd and A Tstd is the acceleration at standard pressure and temperature, P new and T new are the adjusted pressure and temperature set points;

[0124] Apply the adjusted pressure and temperature settings to the bonding machine, track the equipment's operating status in real time, and continuously measure product quality data to generate parameter adjustment results.

[0125] Specifically, the benefit of the formula lies in that by calculating the exponential decrease factor of the pressure and temperature deviation values, and comprehensively considering the acceleration of the current pressure and temperature and the difference between the rate and the standard rate, it can take into account multiple influencing factors in a one-time adjustment, making the adjustment result more flexible and avoiding excessive or insufficient set value corrections as much as possible, thereby achieving pressure and temperature adjustments that are more in line with actual needs in a shorter time.

[0126] P cur The steps to obtain P are: cur Represents the current pressure setting value of the bonding machine, which needs to be obtained based on the actual control panel reading of the equipment under normal production conditions. When collecting data, it is necessary to read the current pressure value applied to the gold wire bond from its process control interface or control parameter list during the period when the equipment has been running stably, and ensure that the parameter is recorded after the equipment has been running continuously for at least 2 hours. If the default range of the equipment is between 0MPa and 2MPa, the real-time pressure displayed on the control panel can be compared with the sensor feedback one by one. After eliminating sensor failure or calibration error, the average control panel reading within this period is selected as P cur For example, in a 2-hour recording, 120 data points are collected per minute, accumulating 14,400 data points. After removing the anomalies, the average value is 1.200 MPa and is set as P cur .

[0127] The steps to obtain ΔP are as follows: ΔP represents the pressure deviation value, that is, the difference between the current actual pressure and the expected or target pressure, which needs to be combined with the previously obtained P cur and the preset target pressure P target Perform calculations, if ΔP=P cur -P target , then we must first clarify P target How to generate: Through the comprehensive collation of past process data, a target pressure value suitable for production can be selected. For example, in previous tests, it was identified that 1.180MPa can maintain the gold wire bonding effect at a good level, so 1.180MPa is used as P targetThen, use the currently read P cur Subtracting the target value will give ΔP, for example, when P cur =1.200MPa and P target =1.180MPa, then ΔP=0.020MPa.

[0128] V P The steps to obtain V are: P Indicates the rate of change of the current real-time pressure. It is necessary to record the pressure change amplitude point by point within a certain time window and divide it by the time increment to obtain an average or instantaneous rate value. Specifically, within a one-minute window, the pressure can be collected once per second, for a total of 60 values, recorded as P1, P2, ..., P 60 , then let Δt = 1 second and calculate by discrete difference: Averaging all adjacent values ​​of this formula gives the average rate of change over one minute. If the pressure rises from 1.190 MPa to 1.200 MPa during this period, the rate is (1.2001.190) / 60 MPa / second = 0.00017 MPa / second.

[0129] V Pstd The steps to obtain V are: Pstd V is the standard pressure change rate, which needs to be obtained from reliable historical data or production specifications. For example, in long-term monitoring, the production stages of high-quality products are marked. A stable bonding process is selected and the ratio of pressure change to time within the same time window is calculated to form a complete rate sequence. The most representative segment value in the sequence is then selected as the standard rate. If the average pressure change rate of most high-quality products is found to be about 0.00010 MPa / s in statistics, it is defined as V. Pstd =0.00010MPa / second.

[0130] A P The steps to obtain are: A P It is the acceleration of pressure. It is necessary to calculate the change of rate over time based on the pressure rate data obtained. Specifically, it can be differentiated by seconds or shorter periods. For example, when Δt = 1 second, let the pressure rate per second in a certain period of time be V P (1),V P (2),…,V P (n), you can use If the pressure increases from 1.195 MPa to 1.210 MPa within a 60-second window and the rate of increase occurs per second, the average acceleration is calculated to be 0.00002 MPa / s2, which is recorded as A. P =0.00002MPa / second2.

[0131] A Pstd The steps to obtain are: A Pstd It is the standard pressure acceleration. It is necessary to make acceleration statistics for the period of stable production. P The idea of ​​calculating the standard value is to continuously collect the discrete values ​​of the pressure change rate in the previous excellent production, make a difference and average them, and finally select the representative acceleration value as A when the best quality performance area appears. Pstd For example, if 120 hours of good production period are collected in 2 weeks, it is found that the acceleration of more than 70% of the time periods is distributed between 0.00001MPa / s2 and 0.00002MPa / s2. Therefore, a typical value can be selected as A based on the median or mode of the distribution. Pstd , such as selecting 0.00001MPa / s2.

[0132] T cur The steps to obtain T are: cur Indicates the current temperature setting value of the bonding machine, which can be read directly from the device temperature control panel or the temperature control parameter list. In order to eliminate the error between the temperature sensor and the display instrument, it is necessary to read the value after the sensor has been calibrated and the device has been kept stably heated for at least 1 hour. If the temperature of the bonding machine is set between 70℃ and 90℃, check the control panel display at this time and compare it with the internal sensor feedback curve to determine the average value as T cur For example, during one hour of continuous monitoring, the temperature control panel displays an average of 78.5°C, while the sensor's average value during the same period is 78.3°C. After correction, 78.4°C is selected and recorded as T cur .

[0133] The steps to obtain ΔT are as follows: ΔT represents the temperature deviation value, that is, the difference between the current actual temperature value and the target temperature value. If the equipment requires 80.0℃ for better bonding under the process regulations, then this 80.0℃ is regarded as T target By comparing the temperature value collected by the on-site thermocouple with the display on the control panel, if the record shows that the current average temperature is 78.4°C, then ΔT = 78.4-80.0 = -1.6°C.

[0134] V T The steps to obtain V are: T is the rate of change of temperature. Similarly, the temperature data can be discretely differentiated and averaged under second-level sampling. If the temperature rises slowly from 78.0°C to 78.4°C within a 30-second time window, it can be sampled once per second, and T1 = 78.0, T 30 =78.4, then the average rate

[0135] V TstdThe steps to obtain V are: Tstd V is the standard temperature change rate, which can be obtained from a large number of excellent production periods. Similarly, it is necessary to first differentiate the temperature records under normal and stable processes, calculate the typical rate values ​​of temperature rise and fall under these conditions, and then select a value in the distribution of values ​​that can represent the general situation as the standard rate. For example, after multiple batches of experiments, the temperature rate is concentrated in the range of 0.01℃ / s to 0.015℃ / s for most of the time, so 0.012℃ / s can be selected as V Tstd .

[0136] A T The steps to obtain are: A T Indicates the acceleration of temperature, which needs to be obtained based on the temperature rate data V T (k) is differentiated again. For example, if the rate is recorded every second for 30 seconds, there are 29 discrete rate values ​​in total. An acceleration sequence is generated, and the average or typical value of the sequence is then used in the adjustment process. If the temperature rise rate increases from 0.012°C / second to 0.015°C / second during a 30-second observation period, the acceleration can be calculated as (0.0150.012) / 30 = 0.0001°C / second².

[0137] A Tstd The steps to obtain are: A Tstd Indicates the standard temperature acceleration. It is necessary to select a relatively stable or good period in many high-quality production processes for acceleration analysis to form a reference acceleration distribution curve, and then select the typical value that best reflects the overall situation to define it as A. Tstd For example, after analyzing records for 10 consecutive days, it was found that the average acceleration during the stable heating stage was 0.00008°C / s2.

[0138] Calculation process:

[0139] After confirming all parameters, enter the following example:

[0140] P cur =1.200MPa, ΔP=0.020MPa, V P =0.00017MPa / s, V Pstd =0.00010MPa / s, A P =0.00002MPa / second2,A Pstd =0.00001MPa / sec2, T cur =78.4℃, ΔT=-1.6℃, V T =0.0133℃ / sec, V Tstd =0.012℃ / second, A T =0.00010℃ / sec2, ATstd =0.00008°C / sec2;

[0141] First calculate the denominator "1+|V P -V Pstd |”:

[0142] |V P -V Pstd |=|0.00017-0.00010|=0.00007;

[0143] 1+0.00007=1.00007;

[0144] Then calculate the square root part

[0145] |ΔT|=|-1.6|=1.6;

[0146] |A T -A Tstd |=|0.00010-0.00008|=0.00002;

[0147] 1.6+0.00002=1.60002;

[0148]

[0149] Combining them gives the exponential terms:

[0150]

[0151] The exponent part is negative as the exponent power:

[0152] e -0.0253 ≈0.97499;

[0153] Calculate P from this new :

[0154] P new =1.200×0.97499≈1.16999MPa;

[0155] Similarly, when calculating temperature:

[0156] “1+|V T -V Tstd |”:

[0157] |V T -V Tstd |=|0.0133-0.012|=0.0013;

[0158] 1+0.0013=1.0013;

[0159]

[0160] |ΔP|=0.020,|A P -A Pstd |=|0.00002-0.00001|=0.00001;

[0161] 0.020+0.00001=0.02001;

[0162]

[0163] Multiply by the numerator:

[0164]

[0165] Exponential term: e -0.226 ≈0.798, thus obtaining T new :T new =78.4×0.798≈62.5℃. The result shows that by substituting the pressure and temperature deviation, acceleration and rate into the formula, the final P new and T new They are approximately 1.17MPa and 62.5℃ respectively. new If the value is significantly lower than the original setting, it means that a certain degree of temperature deviation occurred in the early stage, accompanied by a high acceleration, which caused the formula to impose a large negative adjustment during the adjustment. When such a result exceeds the acceptance range set by the on-site technician in advance (for example, the minimum temperature setting cannot be lower than 65°C), a second correction is required based on the production log and equipment status. If the calculated P new or T new If it is within the acceptable range, it can be used as the new set value to enter the next stage of production.

[0166] After applying the adjusted pressure and temperature setting values ​​to the bonding machine, first enter the pressure and temperature values ​​calculated according to the formula on the equipment control panel, and after the production line starts running, continuously record the pressure, temperature and local state of the product bonding parts at the production line monitoring point to compare the actual changes. Then, compare the pressure data records every ten minutes and record and compare the temperature in the same way. If a pressure deviation of more than 0.3MPa or a temperature deviation of more than 5℃ is found, retrieve the historical operation information of the corresponding period from the database, compare the rate and acceleration threshold data generated previously to see if there is any abnormal fluctuation, and then record it in each period. The acceleration value is compared with the standard value calculated in the previous stage to confirm whether the pressure or temperature needs to be fine-tuned. During this period, the appearance inspection data of the bonding point should also be summarized. For example, if the gold wire contact surface has obvious color changes or surface texture differences during a certain period, it is necessary to compare the equipment operation log of the corresponding time. If it is confirmed that pressure fluctuations occur during this period, the specific fluctuation value can be directly recorded and associated with the generated product quality data. Finally, the comprehensive information of the two sections is compared uniformly, and the pressure and temperature that can most stabilize the bonding effect are selected and fixed as the current production plan. The real-time comparison curves or value lists generated by the whole process are centralized and integrated to obtain the final parameter adjustment results.

[0167] The steps for obtaining continuous quality monitoring results are: based on the parameter adjustment results, calculate the quality index, and the calculation formula is:

[0168]

[0169] Among them, H P is the instantaneous harmonic energy of pressure data, H T is the instantaneous harmonic energy of temperature data, Γ is the envelope curve area of ​​ultrasonic signal, Ψ is the optical profile curvature of bonding point, L is the linear deformation of bonding point, S is the solid diffusion area of ​​bonding point, EQ is the comprehensive quality index;

[0170] Based on quality indicators, judge the stability of the production process and evaluate product quality, verify that the product meets quality requirements, and form continuous quality monitoring results.

[0171] Specifically, the formula's benefit lies in its ability to integrate the instantaneous effects of multiple physical quantities into a single metric by taking the square root of the product of the instantaneous harmonic energy of pressure and temperature data, combining this with the envelope curve area of ​​the ultrasonic signal and the curvature of the bond's optical profile, and combining it with a comprehensive measurement of the bond's inherent force and diffusion levels. The formula's structure is designed to simultaneously account for the combined effects of mechanics and thermals, incorporating ultrasonic and optical inspection elements into the numerator and quantified values ​​of physical deformation and solid-state diffusion into the denominator. This allows for a more comprehensive focus on the characteristics of the bond, ultimately quantifying the overall quality with a single value.

[0172] H P The steps to obtain H are: P The instantaneous harmonic energy of pressure data indicates the energy value extracted from the harmonic components of the pressure signal at a specific moment or within a very short time window. During acquisition, the continuous pressure waveform obtained by the pressure sensor can be decomposed into different frequency components using a fast Fourier transform. Then, the specific harmonic peak is locked and the harmonic energy is obtained through integral analysis. To more accurately quantify this energy, it is necessary to accumulate pressure dynamic data for several hours during several periods of stable equipment operation. During this period, the pressure is recorded at a sampling frequency higher than 1kHz and converted into a corresponding waveform sequence. Then, within a small time interval such as every second or every two seconds, a subsequence is selected and a discrete Fourier transform is performed. The sum of the squares of the amplitudes of the main harmonic and any subharmonic components is calculated, and then multiplied by the corresponding normalization factor as needed to obtain the harmonic energy. If the frequency band energy value corresponding to the main harmonic peak is found to be approximately 12.8mJ and the energy value corresponding to the subharmonic peak is found to be approximately 3.2mJ, the two can be added to obtain 16.0mJ, which is used as the harmonic energy H for this window. P .

[0173] H T The steps to obtain H are: T To obtain the instantaneous harmonic energy of temperature data, it is necessary to extract the high-frequency fluctuation components of the temperature within a very short sampling interval, and then calculate the energy value of a specific harmonic or harmonic group through a similar frequency domain analysis method. In order to make the obtained results more credible, first record the slight temperature fluctuations on the thermal coupling sensor at a sampling rate of not less than 100Hz, and continue sampling for about 2 minutes or longer. Then, perform a discrete Fourier transform on each small segment of data to expand the temperature fluctuations in the frequency domain, and summarize the energy of the main harmonic components by summing or integrating. In a production environment with a temperature control range of approximately 70°C to 90°C, if it is calculated that the main peak energy of the temperature harmonics in a certain period of time is 5.0mJ and the accompanying subharmonic energy is 1.0mJ, the sum of the two, 6.0mJ, can be used as the H for this segment. T .

[0174] The steps for obtaining Γ are as follows: Γ is the area of ​​the ultrasonic signal's envelope curve. Ultrasonic vibrations are typically applied during the bonding process to achieve a higher degree of bonding. To acquire this signal, the sensor's receiving end records the amplitude of the received ultrasonic signal over time. Envelope detection is then performed on the entire waveform. This involves performing half-wave rectification or Hilbert transform on the amplitude curve to obtain the envelope curve. The envelope curve is then definite-integrated over a time window or the entire critical process interval. The resulting value is considered the envelope curve area Γ. In actual measurement, the production line can be operated continuously for 30 minutes while the ultrasonic waveform is acquired, for example, at 95.0 mV·s.

[0175] The steps to obtain Ψ are as follows: Ψ refers to the optical profile curvature of the bonding point. It is necessary to perform curvature analysis on the profile curve of the gold wire bonding point captured under a microscope or imaging system. First, an optical instrument can be used to scan and obtain a two-dimensional profile image of the bonding point, and the profile is discretized into several pixel coordinate points. Then, the curvature calculation formula (such as ) The optical profile curvature is obtained by integrating or weighted averaging over a local or global area. To obtain more stable data, image acquisition can be performed at 20x or higher magnification, followed by comparison and segmented curvature calculation of multiple frames. For example, if the average curvature value calculated for several frames is 0.025 / mm, it can be considered Ψ = 0.025.

[0176] The steps for obtaining L are as follows: L represents the linear deformation of the bonding point under force. In order to obtain this value, it is necessary to read the real-time deformation while applying force to the gold wire and observe the change in the thickness of the bonding point. Assuming that after applying pressure to an area with an original thickness of 1.0 mm, it is measured that the area becomes 0.98 mm, then the linear deformation is 0.02 mm, recorded as L = 0.02 mm.

[0177] The steps for obtaining S are as follows: S is the solid diffusion area of ​​the bond point. The metallurgical bond formed between the gold wire and the substrate under the combined effects of high temperature and pressure is measured, and then the actual physical size is calculated based on the known pixel area. If a quantitative analysis of several slices yields an average value of 2.5 mm², then 2.5 mm² can be considered the solid diffusion area S under the current conditions.

[0178] Calculation process:

[0179] After obtaining the above parameters, put them all into the formula:

[0180] Here is an example:

[0181] H P =16.0mJ. H T=6.0mJ. Γ=95.0. Ψ=0.025 / mm. L=0.02mm. S=2.5mm2.

[0182] Calculate first

[0183] (16.0×6.0) 0.5 =(96.0) 0.5 ≈9.7979;

[0184] Then calculate Γ·Ψ:

[0185] 95.0×0.025=2.375;

[0186] Adding the two together:

[0187] 9.7979+2.375=12.1729;

[0188] Denominator

[0189] L·S=0.02×2.5=0.05;

[0190]

[0191] final: The results show that when the instantaneous harmonic energy is high and the ultrasonic envelope curve area and optical profile curvature are both within a relatively large range, while the linear deformation of the bond point and the solid-state diffusion area are relatively small, the value of the comprehensive quality indicator EQ will be relatively large. If this value exceeds the reference threshold set by the equipment manager (for example, statistical analysis determines that an EQ above 30 in similar processes indicates strong performance in all aspects), it can be said that the overall bonding quality under this condition is good. If later monitoring finds that the EQ is below 25, it means that there is a disharmony between factors such as harmonic energy or deformation, and the actual cause needs to be found and investigated based on specific parameters.

[0192] Based on the quality indicators, the relevant monitoring records in the bonding process are read, including the instantaneous harmonic energy value, ultrasonic signal envelope curve area and optical imaging results obtained previously, and all records are classified and sorted after the end of each bonding period. The corresponding acquisition time periods of pressure harmonic energy and temperature harmonic energy are compared. If it is found that the harmonic energy value in certain time periods deviates too much from the usual level, it is marked with reference to the established empirical threshold. For example, when the harmonic energy is lower than 5mJ or higher than 20mJ, the amplitude of the difference from the average value is noted in the record. Then, when checking the ultrasonic envelope curve area, the envelope area is compared with the set range for each completed bonding. For example, the current value is compared with the range of 80 to 100. If it is greater than 100 or less than 80, the original waveform of this segment is extracted from the data of this batch for review, and then the contour curvature result obtained by the optical system is compared with the previous one. If the curvature value deviates from the preset range of 0.025±0.005, the record will be extracted separately and matched one by one with the linear deformation under force and the solid diffusion area. After the completion of multiple batches of bonding tasks, all comparison results will be summarized, and the records in question will be further verified with the process operators. If it is confirmed that this batch has an abnormality, it will be marked in the database and used as a basis for cross-departmental inspection. The parts that are confirmed to have no abnormalities will be uniformly marked as qualified and the corresponding EQ values ​​will be counted. By counting the EQ value distribution obtained in each cycle over a period of time, it is possible to check whether the high and low value areas have repeated occurrences in the same period. If multiple high or low values ​​occur in the same period, the field data records will be queried according to the timestamp and compared with the ultrasonic signal, temperature curve and pressure curve for investigation. Finally, all data will be summarized and processed to form the results of this continuous quality monitoring.

[0193] The steps for obtaining readjustment results are as follows: Based on the continuous quality monitoring results, extract pressure, temperature, vibration signals and optical detection data, analyze the fluctuation trend of the data over time, calculate the change range of each data within the target time window, and screen out areas of abnormal change. By comparing process parameters, locate the key factors that lead to quality degradation and generate quality degradation data analysis results;

[0194] Based on the quality degradation data analysis results, determine the persistence and mutation of key influencing factors, analyze the linkage effect of key influencing factors on pressure, temperature and vibration amplitude, and generate parameter fine-tuning plans;

[0195] Based on the parameter fine-tuning plan, the current process parameters are called, the adjusted pressure and temperature are calculated, and the readjustment results are obtained.

[0196] Specifically, based on the results of continuous quality monitoring, first read the pressure, temperature, vibration signals and optical detection data from a stable production cycle, and sort and record these data by time index of minutes or seconds. Then, read the pressure value for each record in turn and compare it with the pre-established valid range. For example, the pressure recorded in the equipment technical data can fluctuate between 0MPa and 2MPa. If it exceeds 2MPa, it is marked as abnormal. Similarly, compare the temperature data one by one, for example, compare the temperature with the range of 0℃ to 90℃. If the temperature value exceeds 90℃, it is also marked as abnormal. For vibration signals, a reference range (such as an amplitude of 0 to 30 mm / s) can be obtained after statistics on the equipment operation characteristics. If the vibration amplitude of a certain section exceeds 30 mm / s, it is marked as excessive fluctuation. For optical detection data, the contour parameters are compared with the previously established range by acquiring images on a microscope and performing basic geometric analysis, such as comparing optical roundness or deformation. A quantitative judgment between 0 and 2 is made and 1.5 is set as the dividing value. When the value is higher than 1.5, it is marked as a large deformation. These marked abnormal data are summarized in chronological order and compared with the process parameter list of the machine. The process parameter list will list the pressurization period, heating period and the time period for applying ultrasonic vibration. By searching the timeline to see whether there are corresponding out-of-limit records in these periods, it is possible to locate whether there are improper parameter settings or sudden failure events causing fluctuations. Then all data suspected of causing quality degradation are verified with the production log. If it is confirmed that there is a significant abnormality in pressure or temperature in a certain period, it will be listed separately and the vibration signal and optical detection data amplitude observed in the period will be identified. Finally, these abnormal change areas are matched with the corresponding process operation steps, and the key factors related to equipment overload, material deformation or temperature control offset are found and located in a special classification table to obtain a list of key influencing factors that lead to quality degradation and generate quality degradation data analysis results.

[0197] Based on the results of the quality degradation data analysis, first determine the key influencing factors that have been classified in the list and check whether they appear multiple times in different time periods. Record the duration and mutation amplitude of each influencing factor, and perform linkage analysis on the pressure influencing factor, temperature influencing factor, and vibration amplitude one by one. For example, if the pressure is high during a certain period of time, accompanied by a slight increase in temperature but a significant increase in vibration amplitude, then the pressure, temperature, and vibration are continuously observed during this period. If it is continuously higher than the threshold range set above (such as pressure 2MPa, temperature 90℃, vibration 30mm / s), it means that the influencing factor is persistent. Otherwise, if it only appears for a very short time and the fluctuation amplitude is extremely large, it is mutational. This information is mapped one by one to the production line. After comparing the log with the rated load or process nominal value of the equipment, mark the degree of coupling between each influencing factor and the other factors. For example, when the vibration amplitude is relatively large, the temperature curve is also abnormal at the same time, which is recorded as the linkage effect between vibration and temperature. Then, based on this effect and the existing process limit range (for example, if the temperature is greater than 95°C, the pressure needs to be reduced, and if the pressure is greater than 2.2MPa, the pressing time needs to be shortened), a parameter fine-tuning plan is summarized. All adjustable parameters listed in the plan are listed together and the corresponding upper and lower limits of the values ​​are given. Then, the variation range between each influencing factor is adjusted in a targeted manner to form a plan list containing specific pressure and temperature adjustment strategies to generate a parameter fine-tuning plan.

[0198] Based on the parameter fine-tuning plan, when calling the current process parameters, the current pressure and temperature set points are first read from the process record. Then, the specific amount to be adjusted is found in the corresponding row of the fine-tuning plan. For example, the plan clearly states that the current pressure needs to be reduced by 0.1 MPa or the temperature needs to be increased by 1.5°C. These amounts are added or subtracted from the original set points to obtain the new operating parameters. If the plan also stipulates that vibration signals or optical inspection data will continue to be monitored at a certain time to determine whether further fine-tuning is required, after the pressure and temperature changes are implemented, the equipment will continue to operate and synchronously collect vibration amplitude and optical profile information during the corresponding time period. The current vibration value is compared to see if it still exceeds 30 mm / s or the roundness of the optical inspection exceeds the limit of 1.5. If it is still above the threshold, the new adjustment amount is read from the plan table. The fine-tuning step size is combined with the process constraints and the pressure or temperature is updated again. Finally, after multiple fine-tuning, a relatively stable set of pressure and temperature values ​​is determined and applied to the bonding process, thereby obtaining the readjustment result.

[0199] The steps for obtaining comprehensive analysis results are as follows: based on the readjustment results, all operation data, including the bonder pressure, temperature, vibration amplitude, and adjustment records, are extracted, sorted in time series, and an operation data set is established;

[0200] Based on the operational data set, the critical control points are calculated using the following formula:

[0201]

[0202] Where P is pressure, Q is vibration amplitude, T is temperature, M is real-time energy consumption, A is maximum fluctuation range, B is minimum fluctuation range, C is the number of continuous adjustments, D is the cumulative amount of past adjustments, and K is the critical control point;

[0203] Based on key control points, potential risk points are screened, major risk sources are identified, and comprehensive analysis results are generated.

[0204] Specifically, based on the readjustment results, first read all the operation data at the production site and sort and record them in time series. These operation data include the bonding machine pressure, temperature, vibration amplitude and each corresponding adjustment record. Then divide the integrated sequence data into observation intervals according to minutes or seconds, and read the pressure value and temperature value in each interval respectively. If the pressure exceeds the preset range of 0MPa to 2MPa, the period is marked as pressure abnormality. If the temperature exceeds the range of 0℃ to 90℃, it is marked as temperature abnormality. Then continue to compare the vibration amplitude in the corresponding period. For example, set 0 to 40 mm / s as the acceptable range. If the average vibration value or the maximum instantaneous value within a minute is higher than 40 mm / s, the period is registered as a record of excessive vibration. Then check the adjustment records one by one to see which periods have undergone parameter fine-tuning actions, and compare the pressure at that time. Whether the force, temperature and vibration amplitude are consistent with the problems encountered during the quality inspection of downstream products, these matching records are arranged in chronological order and the equipment load size and the current and voltage values ​​measured by other peripheral sensors are listed in the same table. For example, the current is compared with the range of 0A to 5A, and the voltage is compared with the range of 0V to 24V. When a rapid jump in current or voltage is found, the temperature and pressure changes during the jump period are referred to to determine whether there are other interference factors. In this way, all abnormal time periods corresponding to the fine-tuning actions of the equipment are unified and various data are compared in multiple dimensions to confirm whether there is a correlation between the repeated pressure or temperature out of range in certain time periods and the effect after fine-tuning, and record the detailed parameters of these related moments. Finally, all data are merged in chronological order to establish a comprehensive operation data set.

[0205] The benefit of the formula lies in that by integrating multiple factors such as pressure and vibration amplitude, as well as temperature and energy consumption in the same framework, the values ​​of different dimensions are integrated together and attributed to the fluctuation range of the denominator, thereby providing a more refined measurement of critical moments in the production process. By combining the logarithmic terms of the number of consecutive adjustments and the cumulative amount of past adjustments, the impact of multiple fine-tuning can also be quantified in the same formula.

[0206] The steps for obtaining P are: P represents pressure.

[0207] The steps for obtaining Q are as follows: Q represents the vibration amplitude. The amplitude of the equipment vibration can be collected using an accelerometer or vibration measuring device in the same time series. If the equipment technical specification recommends a tolerable range of 0 to 40 mm / s, the vibration amplitude is recorded second by second during data collection and checked to see if it exceeds the range of 40 mm / s. These original records are then aligned with the pressure records of the corresponding seconds to facilitate the subsequent synchronous multiplication of P and Q during integration. If, after inspection, the vibration value in a certain 10-second interval is between 25 mm / s and 35 mm / s, the discrete second-by-second data can be taken and point-to-point multiplied with the pressure value.

[0208] The steps for obtaining T are: T represents temperature.

[0209] The steps for obtaining M are as follows: M refers to real-time energy consumption, which can be obtained from the current, voltage and other measuring devices of the equipment. The instantaneous power is calculated and accumulated to form an energy consumption time series. If the power measured on site during the operation of the equipment is mostly between 120W and 160W, these time series points are recorded as discrete values ​​of M once per second or per minute and retained in the same time series.

[0210] The steps for obtaining A are as follows: A is the maximum fluctuation range. It is necessary to perform a fluctuation analysis on the previously recorded pressure, temperature, vibration, and energy consumption, and extract the most significant fluctuation amplitude value. If you want to unify the measurement, you can normalize the four quantities or select the most influential quantity as the main fluctuation indicator. Then scan the entire production process to find the maximum peak-to-valley difference of this indicator, and record the difference as A. For example, if the vibration amplitude changes from 2 mm / s to 38 mm / s, and the total fluctuation reaches 36 mm / s, 36 will be used as the value of A at this time.

[0211] The steps to obtain B are as follows: B is the minimum fluctuation range. Similarly, the same data source needs to be analyzed. After excluding the abnormal values ​​generated when the equipment is just started or in a faulty state, the peak-to-valley difference in the relatively stable period is found and recorded as B. For example, in a stable stage without special fluctuations, the vibration amplitude only varies between 25 mm / s and 28 mm / s, and the minimum fluctuation is only 3 mm / s. At this time, 3 can be set as B.

[0212] The steps for obtaining C are as follows: C is the number of consecutive adjustments. It is necessary to retrieve which operations are consecutive adjustments from all the adjustment operation information recorded previously and count them. For example, if the pressure and temperature are fine-tuned 4 times or more within 1 hour, the number of consecutive adjustments during this period can be determined to be 4. It can also be counted on a longer time scale. If 6 fine-tunings are performed cumulatively on a certain day, it can be recorded as 6.

[0213] The steps for obtaining D are as follows: D is the cumulative amount of past adjustments. The intensity of all adjustment operations can be tracked during production over multiple days or weeks, and the total amount is added up to form a total adjustment amplitude indicator. If the amount of pressure or temperature deviation from the initial value is digitized and accumulated each time the adjustment is made, for example, within a certain period of time, the total pressure is adjusted from 1.200MPa to 1.250MPa, and the temperature is adjusted from 78.0℃ to 79.0℃, which corresponds to 0.050MPa plus 0.010×n cumulative amounts respectively. After integration, a total value is obtained, which is recorded as D.

[0214] Calculation process:

[0215] Let the integration interval t1 = 0 seconds, t2 = 10 seconds, and assume that within these 10 seconds, ∫(PQ+TM) is accumulated over time.

[0216] dt = 280.0, where the product of pressure P and vibration amplitude Q plus the product of temperature T and energy consumption M is obtained by discrete integration or numerical method to obtain the total of 280.0;

[0217] Let A = 36, B = 3, C = 3, D = 5;

[0218] Calculate the denominator first:

[0219]

[0220] Then divide the integral by the denominator:

[0221]

[0222] Finally, multiply by ln(1+CD):

[0223] CD = 3 × 5 = 15;

[0224] 1+15=16;

[0225] ln(16)≈2.7726;

[0226] K = 26.95 × 2.7726 ≈ 74.7;

[0227] The result shows that the integrated amount of pressure, vibration, temperature and energy consumption in the current interval is combined, and a higher ratio is obtained when comparing large fluctuations with small fluctuations. It is then amplified by the logarithmic terms of the number of continuous adjustments and the cumulative amount of past adjustments to reach 74.7. If the process manager sets the K threshold at 50 or 60 after statistics on a large amount of historical data and finds that it reaches 74.7 in this period, it means that there are parts of the comprehensive control level of this period that require special attention. In subsequent operations, this period should be marked as a potential risk point, and priority should be given to investigation or corresponding improvements during data retrieval.

[0228] Based on the key control points, all time series data generated within a week are first retrieved, and the K value obtained in each time period is compared with the stored machine failure records. When the K of a certain time period is greater than the reference value previously set by the technical team (for example, 60), the time period is listed as a suspected risk period and the specific pressure, vibration, temperature and energy consumption levels are recorded. Then, the maintenance log of the machine is compared with the manual inspection notes, and the changes in the continuous adjustment times C and the cumulative amount D from the previous fine-tuning are combined to see whether there are multiple unplanned shutdowns or significant over-limit situations in this period. If it is confirmed that obvious faults or machine abnormalities are also observed in this period, it will be marked as a major risk point and assigned to the corresponding maintenance personnel for follow-up inspection. Finally, all time periods exceeding the reference value are integrated and combined with other data information to screen out areas with high repetition or wide impact range as a list of potential risk sources to obtain a comprehensive analysis result.

[0229] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An IoT-driven intelligent gold bonding wire real-time monitoring and optimization method, characterized in that: The following steps are involved: Collecting pressure and temperature data of the bonding gold wire to obtain real-time data of the gold wire pressure and temperature; performing data verification based on the real-time data of the gold wire pressure and temperature, eliminating abnormal values, and generating preliminary data verification results; Based on the preliminary data verification results, analyzing the deviation between the gold wire pressure and temperature and the standard parameters, performing data comparison, comparing the real-time pressure and temperature data with the set standard thresholds, determining the degree of deviation, and generating a deviation analysis result; based on the deviation analysis results, adjusting the pressure and temperature settings of the bonding machine and generating a parameter adjustment result; Based on the parameter adjustment results, continuously monitor the adjusted gold wire bonding quality, verify whether the gold wire bonding meets the production standards, and generate continuous quality monitoring results; Based on the continuous quality monitoring results, if quality degradation is detected, fine-tuning the parameters again to generate a readjustment result; Based on the readjustment results, all operational data are integrated, all monitoring data and adjustment records are summarized, statistical analysis is performed on the data, critical control points and potential risks are identified, and comprehensive analysis results are generated.

2. The method for real-time monitoring and optimization of intelligent gold bonding wires driven by the Internet of Things according to claim 1, characterized in that: The steps of acquiring the real-time data of the gold wire pressure and temperature are as follows: installing pressure and temperature sensors on the production line, continuously monitoring the pressure and temperature of the bonding gold wire, and obtaining the real-time data of the pressure and temperature of the bonding gold wire; Data formatting is performed based on the real-time data of pressure and temperature of the bonding gold wire to obtain the real-time data of pressure and temperature of the gold wire.

3. The method for real-time monitoring and optimization of intelligent gold bonding wire driven by the Internet of Things according to claim 1, characterized in that: The step of obtaining the preliminary data verification result is: based on the real-time data of the gold wire pressure and temperature, checking the integrity and accuracy of the data, eliminating erroneous data introduced by equipment failure or environmental factors, and obtaining the pressure and temperature data after cleaning; According to the pressure and temperature data after cleaning, the deviation index of each data point is calculated using the following formula: Among them, ZP i is the pressure value of a single data point, ZP mode is the mode of pressure data, ZP range is the range of pressure data, ZT i is the temperature value of a single data point, ZT mode is the mode of temperature data, ZT range is the range of temperature data, ZD is the deviation index; Based on the deviation index, data points with deviation indexes higher than a threshold are eliminated to obtain preliminary data verification results.

4. The method for real-time monitoring and optimization of intelligent gold bonding wires driven by the Internet of Things according to claim 1, characterized in that: The step of obtaining the deviation analysis result comprises: based on the preliminary data verification result, obtaining the data change trend by calculating the second-order derivatives of adjacent data points, screening the fluctuating data points, calculating the change rate of the fluctuating data points, and obtaining the pressure data and temperature data for the deviation analysis; The degree of deviation is calculated based on the pressure data and temperature data used for deviation analysis. The calculation formula is: in, is the second-order derivative of pressure, is the second derivative of temperature, P max is the maximum pressure, P min is the minimum value of pressure, P mid is the median pressure, T max is the maximum temperature, T min is the minimum temperature, T mid is the median temperature, and X is the degree of deviation; Based on the degree of deviation, determine whether the deviation continues to exceed the set threshold, screen key abnormal areas, and generate deviation analysis results.

5. The method for real-time monitoring and optimization of intelligent gold bonding wire driven by the Internet of Things according to claim 1, characterized in that: The step of obtaining the parameter adjustment result is: based on the deviation analysis result, calculating the adjusted pressure and temperature setting values, the calculation formula is: and Among them, P cur and T cur is the current pressure and temperature setting value of the bonding machine, ΔP and ΔT are the deviation values ​​of pressure and temperature respectively, V P and V T are the real-time pressure and temperature change rates, V Pstd and V Tstd is the standard pressure and temperature change rate, A P and A T is the acceleration of pressure and temperature, A Pstd and A Tstd is the acceleration at standard pressure and temperature, P new and T new are the adjusted pressure and temperature set points; Apply the adjusted pressure and temperature settings to the bonding machine, track the equipment's operating status in real time, and continuously measure product quality data to generate parameter adjustment results.

6. The method for real-time monitoring and optimization of intelligent gold bonding wires driven by the Internet of Things according to claim 1, characterized in that: The step of obtaining the continuous quality monitoring result is: based on the parameter adjustment result, calculating the quality index, the calculation formula is: Among them, H P is the instantaneous harmonic energy of pressure data, H T is the instantaneous harmonic energy of temperature data, Γ is the envelope curve area of ​​ultrasonic signal, Ψ is the optical profile curvature of bonding point, L is the linear deformation of bonding point, S is the solid diffusion area of ​​bonding point, EQ is the comprehensive quality index; Based on the quality indicators, the stability of the production process is judged and the product quality is evaluated, and the product is verified to meet the quality requirements, forming a continuous quality monitoring result.

7. The method for real-time monitoring and optimization of intelligent gold bonding wires driven by the Internet of Things according to claim 1, characterized in that: The step of obtaining the readjustment result comprises: extracting pressure, temperature, vibration signals and optical detection data based on the continuous quality monitoring results, analyzing the fluctuation trend of the data over time, calculating the change amplitude of each data within the target time window, screening out abnormal change areas, locating the key influencing factors causing quality degradation by comparing process parameters, and generating quality degradation data analysis results; Based on the analysis results of the quality degradation data, determine the persistence and mutation of key influencing factors, analyze the linkage effects of key influencing factors on pressure, temperature and vibration amplitude, and generate parameter fine-tuning plans; Based on the parameter fine-tuning scheme, the current process parameters are called, the adjusted pressure and temperature are calculated, and the readjustment results are obtained.

8. The method for real-time monitoring and optimization of intelligent gold bonding wires driven by the Internet of Things according to claim 1, characterized in that: The step of obtaining the comprehensive analysis result is: based on the readjustment result, extracting all operation data, including bonding machine pressure, temperature, vibration amplitude and adjustment records, sorting them according to time series, and establishing an operation data set; Based on the operational data set, the critical control points are calculated using the following formula: Where P is pressure, Q is vibration amplitude, T is temperature, M is real-time energy consumption, A is maximum fluctuation range, B is minimum fluctuation range, C is the number of continuous adjustments, D is the cumulative amount of past adjustments, and K is the critical control point; Based on the critical control points, potential risk points are screened, major risk sources are identified, and comprehensive analysis results are generated.

9. The optimization system of the IoT-driven intelligent gold bonding wire real-time monitoring and optimization method according to any one of claims 1 to 8, characterized in that: include: The data acquisition module collects data from the pressure sensor and temperature sensor of the gold wire bonding equipment, synchronizes the time, records the real-time data transmission status, and generates data acquisition results; The data verification module receives the raw data from the data acquisition results, uses statistical analysis to eliminate data points that exceed the normal working range, synthesizes the verified pressure and temperature data, and generates verification data results; The deviation analysis module analyzes the data in the calibration data results, calculates the deviation degree of each data point by comparing the standard parameters, compares the difference value with the standard threshold, and generates the deviation analysis results; The parameter adjustment module adjusts the pressure and temperature parameters of the bonding machine in real time based on the deviation analysis results, records each step of the adjustment, and generates parameter adjustment results; The quality monitoring module continuously runs the production line using adjusted parameters, collects product quality data in real time, compares it with production standards, confirms that quality meets the standards, and generates continuous quality monitoring results.

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