Semiconductor packaging structure health monitoring method and system based on embedded sensor

Abstract

The invention discloses a semiconductor packaging structure health monitoring method and system based on an embedded sensor, and belongs to the technical field of semiconductors, and the method specifically comprises the steps: determining parasitic capacitance sensitive areas through electromagnetic simulation, and deploying capacitive sensors in the areas to collect parasitic capacitance value data in real time; after receiving the data, the data processing module adopts a dynamic threshold setting algorithm to judge whether the data is abnormal or not; if the data is abnormal, reversely deducing the physical state change of the packaging structure by using a physical model, and determining the abnormal type and position; the system arranges the abnormal information into alarm information and sends the alarm information to an operator, so that the operator can check and maintain by using the collaborative maintenance platform resource; and the health monitoring efficiency and accuracy of the semiconductor packaging structure are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of semiconductor measurement, and specifically relates to a method and system for health monitoring of semiconductor package structures based on embedded sensors. Background Art

[0002] Semiconductor packaging is the process of processing wafers that have passed testing into independent chips according to product models and functional requirements. With the continuous development of semiconductor technology, the requirements for the reliability and stability of semiconductor package structures are getting higher and higher. In semiconductor packaging, parasitic capacitances are formed between the chip and the packaging material, as well as between different metal layers. The capacitance values of these parasitic capacitances are closely related to the physical state of the package structure. However, at present, there are few studies and applications on using the characteristics of parasitic capacitances to monitor the health status of semiconductor package structures, and potential problems such as delamination and deterioration of material properties in the package structure cannot be discovered in a timely and effective manner, resulting in possible performance degradation or even failure of semiconductor devices during use due to package structure failures.

[0003] For example, Chinese Patent No. CN116609629A discloses a power semiconductor device health monitoring circuit and method, including: a driving circuit for receiving a pulse driving signal output by a control chip and outputting a driving signal to control the operation of the device under test; a first switching tube for receiving a first pulse signal of the control chip and controlling the conduction or cutoff of the first switching tube according to the first pulse signal; a second switching tube for receiving a second pulse signal of the control chip and controlling the conduction or cutoff of the second switching tube according to the second pulse; wherein, a current source provides excitation current for the first switching tube and the second switching tube; a voltage sampling module samples the voltage of the device under test. It solves the problems of high bandwidth requirements for the measurement circuit, low measurement accuracy, and difficulty in practical engineering applications, and realizes accurate measurement of the threshold voltage.

[0004] The above existing technologies all have the following problems: there are limitations in the scope of application; they mainly focus on current and voltage parameters and evaluate the health status of the device by measuring these parameters. However, the health problems of semiconductor devices may involve multiple factors such as temperature, mechanical stress, and material aging, and these factors may not be fully reflected by simple current and voltage measurements; their ability in fault warning may be weak. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention proposes a method and system for health monitoring of semiconductor package structures based on embedded sensors. The parasitic capacitance sensitive regions are determined through electromagnetic simulation, and capacitance sensors are deployed in these regions to collect parasitic capacitance value data in real time. After receiving the data, the data processing module uses a dynamic threshold setting algorithm to determine whether the data is abnormal. If the data is abnormal, the physical state change of the package structure is deduced inversely using a physical model to determine the type and location of the abnormality. The system organizes the abnormal information into alarm information and sends it to the operator so that the operator can use the resources of the collaborative maintenance platform for inspection and repair, improving the efficiency and accuracy of the health monitoring of semiconductor package structures.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for health monitoring of semiconductor package structures based on embedded sensors, comprising:

[0008] Determine the sensitive regions of parasitic capacitance through electromagnetic simulation methods, collect parasitic capacitance value data using capacitance sensors within the sensitive regions, and transmit the collected parasitic capacitance value data to the data processing module;

[0009] After receiving the parasitic capacitance value data, the data processing module uses a dynamic threshold setting algorithm to determine whether the real-time parasitic capacitance value data exceeds the normal range;

[0010] When the real-time parasitic capacitance value data exceeds the normal range, use the pre-established physical model to inversely deduce the possible physical state changes of the package structure based on the change of the parasitic capacitance value data, and combine the inverse deduction result with the actual layout of the package structure to determine the type and location of the abnormality of the package structure;

[0011] Organize the determined abnormality type and location information into alarm information and send it to the operator. The operator checks and repairs the semiconductor package structure according to the alarm information in combination with the resources on the pre-established collaborative maintenance platform.

[0012] Specifically, the capacitance sensor has an adaptive sampling rate and automatically adjusts the sampling interval. The process of the capacitance sensor collecting parasitic capacitance value data includes:

[0013] A1: Use electromagnetic simulation software to model the circuit and simulate the generation and distribution of parasitic capacitance;

[0014] A2: Determine the sensitive regions of parasitic capacitance according to the simulation results;

[0015] A3: Configure the parameters of the capacitance sensor according to the characteristics of the sensitive regions and the measurement requirements;

[0016] A4: Install the capacitive sensor within the sensitive area and calibrate the capacitive sensor;

[0017] A5: According to the calibrated capacitive sensor, collect parasitic capacitance value data and automatically adjust the sampling interval according to the adaptive sampling rate function , where represents the adjusted sampling interval, represents the original sampling frequency, represents the adjustment coefficient.

[0018] Specifically, the automatic adjustment of the sampling interval by the adaptive sampling rate function in A5 includes:

[0019] A5.1: Set the adaptive sampling rate parameters of the capacitive sensor, and the adaptive sampling rate parameters include the data change threshold , the upper and lower limits of the sampling rate and , the sampling interval adjustment step and the trigger adaptive adjustment condition , where represents the value of the newly collected parasitic capacitance value data, represents the value of the old parasitic capacitance value data, represents the set data change rate threshold;

[0020] A5.2: Set the sampling rate of the capacitive sensor to a preset initial value , and ;

[0021] A5.3: Start the capacitive sensor, start collecting parasitic capacitance value data, and sample according to ;

[0022] A5.4: During the data collection process, monitor the change of the parasitic capacitance value data in real time, and obtain the change amount between adjacent two sampling data through the difference calculation method, and compare the calculated data change amount with ;

[0023] If , continue to collect data at the current sampling rate;

[0024] If , automatically adjust the sampling interval according to the preset sampling interval adjustment step , and update the sampling rate of the capacitive sensor according to the adjusted sampling interval.

[0025] Specifically, the judgment process of the dynamic threshold setting algorithm includes:

[0026] B1: The data processing module receives real-time parasitic capacitance value data from the capacitance sensor and initializes dynamic threshold parameters, where the dynamic threshold parameters include an initial threshold, a threshold adjustment factor, and the number of iterations;

[0027] B2: Using a dynamic threshold setting algorithm, calculate the current dynamic threshold according to the real-time received parasitic capacitance value data , where and respectively represent the lower and upper limits of the dynamic threshold;

[0028] B3: Compare the real-time parasitic capacitance value data X with the dynamic threshold;

[0029] If , it is considered that the parasitic capacitance value data is normal;

[0030] If or , it is considered that the parasitic capacitance value data is abnormal;

[0031] B4: If it is determined that the parasitic capacitance value data is abnormal, the data processing module triggers an alarm and records the abnormal data. At the same time, according to the new real-time parasitic capacitance value data, the dynamic threshold is updated dynamically.

[0032] Specifically, the specific steps for determining the abnormal type and location of the package structure include:

[0033] C1: Load a physical model established in advance according to the package structure and parasitic capacitance characteristics, and obtain the abnormal parasitic capacitance value data obtained after dynamic threshold judgment ;

[0034] C2: Input into the pre-established physical model. The physical model matches according to the input real-time and searches for similar parasitic capacitance characteristics in the physical model database;

[0035] C3: Use a deep learning algorithm to analyze the matched to identify changes in the physical state of the package structure;

[0036] C4: Combine the identified physical state changes with the actual layout of the package structure, and through comparison and analysis, determine the abnormal type and specific location of the package structure;

[0037] C5: Generate an abnormal report according to the abnormal location and analysis results in C4. The abnormal report includes the abnormal type, location, possible reasons, and recommended repair measures.

[0038] Specifically, the specific steps for searching for similar parasitic capacitance characteristics in the physical model database include:

[0039] D1: Load the pre-established physical model and initialize the parameters and variables in the physical model;

[0040] D2: Input the preprocessed abnormal parasitic capacitance value data into the physical model in the format required by the physical model;

[0041] D3: In the database of the physical model, use the distance-based search method to search according to the input real-time parasitic capacitance value data;

[0042] D4: Use the similarity measurement method to find the parasitic capacitance characteristics similar to the input data in the database of the physical model and output the matching result;

[0043] D5: Evaluate the matching result and further analyze the health status of the package structure according to the matching result.

[0044] A semiconductor package structure health monitoring system based on an embedded sensor, including: a data acquisition module, an anomaly detection module, a reverse inference and positioning module, and an alarm and repair module;

[0045] The data acquisition module determines the sensitive area of the parasitic capacitance through electromagnetic simulation, deploys a capacitance sensor inside the sensitive area to collect the parasitic capacitance value data, and transmits the parasitic capacitance value data to the data processing module in real time;

[0046] The anomaly detection module is used to receive the collected parasitic capacitance value data, use the dynamic threshold setting algorithm to judge whether the real-time data exceeds the normal range, and trigger the anomaly detection process;

[0047] The reverse inference and positioning module is used to, when the real-time parasitic capacitance value data exceeds the normal range, use the pre-established physical model to reverse infer the possible physical state changes of the package structure, and determine the anomaly type and location in combination with the actual layout of the package structure;

[0048] The alarm and repair module is used to organize the determined anomaly type and location information into alarm information and send it to the operator. The operator checks and repairs the semiconductor package structure according to the alarm information in combination with the resources on the pre-established collaborative repair platform.

[0049] Specifically, the anomaly detection module includes: a data receiving unit, a dynamic threshold setting unit, and an anomaly detection unit;

[0050] The data receiving unit is used to receive the parasitic capacitance value data transmitted from the data acquisition and transmission module;

[0051] The dynamic threshold setting unit is used to dynamically adjust the normal range threshold of the parasitic capacitance value according to historical data and the current environment;

[0052] The abnormal detection unit is used to judge whether the real-time data exceeds the normal range by using a dynamic threshold setting algorithm, and trigger the corresponding abnormal detection process.

[0053] Specifically, the backtracking and positioning module includes: a physical model backtracking unit and an abnormal positioning unit;

[0054] The physical model backtracking unit is used to backtrack the physical state change of the packaging structure by using a pre-established physical model according to the change of parasitic capacitance value data;

[0055] The abnormal positioning unit determines the type and specific location of the abnormality by combining the backtracking result and the actual layout of the packaging structure.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] 1. The present invention proposes a method for health monitoring of semiconductor packaging structures based on embedded sensors. By determining the sensitive area through electromagnetic simulation and using a capacitance sensor to collect parasitic capacitance data in real time, accurate monitoring of the health state of semiconductor packaging structures is achieved; by using a dynamic threshold setting algorithm to judge whether the data is abnormal, potential problems can be detected early, thus effectively improving the reliability and stability of semiconductor packaging structures.

[0058] 2. The present invention proposes a method for health monitoring of semiconductor packaging structures based on embedded sensors. By combining a physical model and the actual layout of the packaging structure, the type and location of the abnormality can be accurately judged, and the alarm information can be sent to the operator in time. This not only helps the operator quickly locate and solve the problem, but also combines the resources on the collaborative maintenance platform, improves the maintenance efficiency and accuracy, and reduces the maintenance cost. Description of the Drawings

[0059] Figure 1 It is a schematic diagram of the method for health monitoring of semiconductor packaging structures based on embedded sensors of the present invention;

[0060] Figure 2 It is a principle flowchart of the method for health monitoring of semiconductor packaging structures based on embedded sensors of the present invention;

[0061] Figure 3 It is an architecture diagram of the system for health monitoring of semiconductor packaging structures based on embedded sensors of the present invention. Detailed Embodiments

[0062] Embodiment 1

[0063] Please refer to Figure 1 and Figure 2, an embodiment provided by the present invention: a method for health monitoring of a semiconductor package structure based on an embedded sensor, the implementation process of which includes S101 - S104, and the specific steps are as follows:

[0064] S101: Determine the sensitive area of the parasitic capacitance through electromagnetic simulation methods, collect the parasitic capacitance value data using a capacitance sensor within the sensitive area, and transmit the collected parasitic capacitance value data to the data processing module;

[0065] Furthermore, the process of collecting and transmitting the parasitic capacitance value data includes:

[0066] (1) Use the electromagnetic simulation software ANSOFT Q3D EXTRACTOR to model the circuit or PCB layout, and set the simulation parameters, such as the frequency range and material properties;

[0067] (2) Run the simulation, analyze the distribution of the parasitic capacitance, and determine the sensitive area of the parasitic capacitance according to the simulation results;

[0068] (3) Arrange the capacitance sensor within the sensitive area determined by the electromagnetic simulation to ensure a stable and reliable connection between the sensor and the circuit or PCB layout to be measured;

[0069] (4) Use a data acquisition system or a microcontroller device to connect to the capacitance sensor and set the data acquisition parameters, such as the sampling rate and resolution;

[0070] (5) Start data acquisition, record the parasitic capacitance value data, and transmit the collected capacitance value data to the data processing module through a serial bus, such as I2C or SPI;

[0071] (6) The data processing module receives and stores the collected parasitic capacitance value data, and performs pre - processing operations on the data, such as cleaning, conversion, and formatting.

[0072] It should be noted that the process of the capacitance sensor collecting the parasitic capacitance value data is essentially to sense and measure the capacitance change caused by the sensitive area of the parasitic capacitance through the sensor. Specifically, the sensitive area of the parasitic capacitance is simply referred to as the target object. When the target object approaches the sensing surface of the sensor, it will change the capacitance of the oscillator inside the sensor, thereby triggering the output change of the sensor. This process relies on the capacitance coupling principle, that is, the capacitance formed between two metal plates will change with the change of the distance and relative position between them. Here, the two metal plates refer to the sensing surface of the sensor and the target object.

[0073] S102: After receiving the parasitic capacitance value data, the data processing module uses a dynamic threshold setting algorithm to determine whether the real - time parasitic capacitance value data exceeds the normal range;

[0074] S103: When the real-time parasitic capacitance value data exceeds the normal range, use the pre-established physical model to inversely deduce the possible physical state changes of the package structure according to the change of the parasitic capacitance value data. Combine the inverse deduction result with the actual layout of the package structure to determine the abnormal type and location of the package structure;

[0075] S104: Organize the determined abnormal type and location information into alarm information and send it to the operator. The operator checks and repairs the semiconductor package structure according to the alarm information and combines the resources on the pre-established collaborative maintenance platform.

[0076] Further, the specific steps of S104 include:

[0077] (1) When the data processing module determines that the parasitic capacitance value data exceeds the normal range and inversely deduces the possible physical state changes of the package structure, the system will organize these abnormal information, including abnormal type, location, possible physical state changes, etc., into structured alarm information;

[0078] (2) The system sends the alarm information to the operator's terminal device, such as a mobile phone or a computer, through a preset communication channel, such as text message, email, or internal communication software;

[0079] (3) After receiving the alarm information, the operator carefully reads and understands the content, especially the abnormal type and location information. Then, according to the alarm information, the operator logs in to the pre-established collaborative maintenance platform, which may include maintenance manuals, spare part inventories, maintenance personnel schedules, etc.;

[0080] (4) The operator formulates a detailed maintenance plan by combining the alarm information and the resources on the collaborative maintenance platform, including determining maintenance personnel, spare part requirements, maintenance time, etc.;

[0081] (5) The maintenance personnel check and repair the semiconductor package structure according to the maintenance plan. During the maintenance process, specific tools and equipment may be required, and specific operation steps and safety specifications must be followed;

[0082] (6) After the maintenance is completed, the operator needs to record the maintenance results on the collaborative maintenance platform, including maintenance time, maintenance personnel, spare parts used, and findings during the maintenance process.

[0083] Exemplarily, in a semiconductor package structure for a mobile communication device, through electromagnetic simulation, it is determined that the four corners of the chip and the packaging material and three key connection parts between different metal layers are the key sensitive areas of parasitic capacitance. Capacitive sensors based on MEMS technology are installed in these areas respectively; the capacitive sensors are connected to the data acquisition module through micro-wires. The data acquisition module collects capacitance value data every 100 milliseconds and transmits the data to the data processing module; the data processing module has pre-established a parasitic capacitance value database for this semiconductor package structure within the normal operating temperature range and normal operating voltage range, where the normal operating temperature range is from -20°C to 85°C, and the normal operating voltage range is from 1.8V to 3.3V; when the data processing module receives new capacitance value data, it first performs median filtering on the data to remove noise, then normalizes the data, and compares the processed data with the normal range in the database; during a monitoring process, the data processing module finds that the capacitance value at a certain position exceeds the upper limit of the normal range, starts the abnormal analysis process, and analyzes it using the pre-established physical model; after analysis, it is judged that it may be due to a slight delamination of the package structure at this position, resulting in a change in the distance between the chip and the packaging material, thereby causing an increase in the parasitic capacitance value; the data processing module sends the abnormal information to the alarm module, and the alarm module reminds the operator through audible and visual alarms; at the same time, the data processing module stores the abnormal data and analysis results of this time in the data storage module; the operator checks and repairs the semiconductor package structure according to the alarm information, avoiding the degradation of the communication device performance caused by the package structure failure; through the above implementation methods, the effective monitoring of the health status of the semiconductor package structure is realized, and the reliability and stability of semiconductor devices are improved.

[0084] The capacitive sensor has an adaptive sampling rate and automatically adjusts the sampling interval. The process of the capacitive sensor collecting parasitic capacitance value data includes:

[0085] A1: Use electromagnetic simulation software to model the circuit and simulate the generation and distribution of parasitic capacitance;

[0086] Furthermore, the specific steps of A1 include:

[0087] (1) Define the circuit structure, operating frequency, and the parasitic capacitance area of concern to be simulated. At the same time, according to the simulation requirements and software characteristics, select the electromagnetic simulation software COMSOL;

[0088] (2) According to the actual circuit layout, establish a geometric model of the circuit in the simulation software, including signal lines, power lines, ground lines, and the packages of components, etc., and set the correct material properties for different parts of the circuit, such as the conductivity of conductors and the dielectric constant of insulating media;

[0089] (3)According to the simulation objectives, define the regions where parasitic capacitances need to be extracted in the established circuit geometric model, and set the simulation parameters, including the operating frequency, simulation accuracy, and mesh division. It should be noted that mesh division is crucial for the accurate simulation of parasitic capacitances because the magnitude of parasitic capacitances is closely related to the distance and shape between conductors;

[0090] (4)Run the simulation in the software, and the software will calculate the distribution of parasitic capacitances based on the set parameters and geometric model;

[0091] (5)During the simulation process, monitor the progress and results of the simulation to ensure the smooth progress of the simulation;

[0092] (6)After the simulation is completed, view the distribution of parasitic capacitances in the software, including the magnitude and location of parasitic capacitances.

[0093] A2: Determine the sensitive regions of parasitic capacitances based on the simulation results;

[0094] Further, the specific steps of A2 include:

[0095] (1)Export the simulation results of parasitic capacitances from the electromagnetic simulation software, which usually include the distribution diagrams and numerical tables of parasitic capacitances;

[0096] (2)Carefully analyze the exported results of parasitic capacitances. Pay attention to the magnitude, distribution location of parasitic capacitances, and their mutual relationships with other parts of the circuit;

[0097] (3)Based on the magnitude and distribution of parasitic capacitances, identify the regions with larger parasitic capacitances, which are usually the sensitive regions of parasitic capacitances and have a greater impact on circuit performance;

[0098] (4)Verify the accuracy of the simulation results by comparing with the actual circuit test results.

[0099] A3: Configure the parameters of the capacitance sensor according to the characteristics of the sensitive regions and measurement requirements;

[0100] A4: Install the capacitance sensor in the sensitive region and calibrate the capacitance sensor. The calibration process is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0101] A5: According to the calibrated capacitance sensor, collect the data of parasitic capacitance values and automatically adjust the sampling interval according to the adaptive sampling rate function , where, represents the adjusted sampling interval, represents the original sampling frequency, represents the adjustment coefficient.

[0102] In the present invention, the adjustment coefficient is dynamically calculated according to the fluctuation range. First, the maximum value and the minimum value of the data within one week are statistically counted, and their difference is calculated as the fluctuation range of the data. Then, corresponding adjustment coefficients are set according to the size of the fluctuation range. For example, when the fluctuation range is less than 10% of the data average value, the adjustment coefficient is set to 0.7; when the fluctuation range is between 10% and 30% of the average value, the adjustment coefficient is set to 1; when the fluctuation range is greater than 30% of the average value, the adjustment coefficient is set to 1.3.

[0103] The automatic adjustment of the sampling interval in A5 for the adaptive sampling rate function includes:

[0104] A5.1: Set the adaptive sampling rate parameters of the capacitance sensor, and the adaptive sampling rate parameters include the data change threshold , the upper and lower limits of the sampling rate and , the sampling interval adjustment step and the trigger condition for adaptive adjustment , where represents the data value of the newly collected parasitic capacitance value, represents the data value of the old parasitic capacitance value, represents the set data change rate threshold;

[0105] A5.2: Set the sampling rate of the capacitance sensor to a preset initial value , and ;

[0106] A5.3: Start the capacitance sensor, start collecting the data of the parasitic capacitance value, and sample according to ;

[0107] A5.4: During the data collection process, monitor the change of the parasitic capacitance value data in real time, and obtain the change amount between two adjacent sampling data through the difference calculation method , and compare the calculated data change amount with , where and respectively represent the sampling data of two adjacent times, and n represents the number of collections;

[0108] If , continue to collect data at the current sampling rate;

[0109] If , then according to the preset sampling interval adjustment step , automatically adjust the sampling interval, and update the sampling rate of the capacitance sensor according to the adjusted sampling interval.

[0110] The judgment process of the dynamic threshold setting algorithm includes:

[0111] B1: The data processing module receives real-time parasitic capacitance value data from the capacitance sensor and initializes dynamic threshold parameters, where the dynamic threshold parameters include an initial threshold, a threshold adjustment factor, and the number of iterations;

[0112] B2: Use the dynamic threshold setting algorithm to calculate the current dynamic threshold according to the real-time received parasitic capacitance value data , where, and respectively represent the lower and upper limits of the dynamic threshold;

[0113] Furthermore, the specific steps of B2 include:

[0114] (1) Receive real-time parasitic capacitance value data from the data acquisition device and preprocess the received parasitic capacitance value data, including noise removal, filtering, and smoothing;

[0115] (2) Set an initial dynamic threshold according to the historical parasitic capacitance value data. In this application, the average value of the parasitic capacitance value is used as the initial dynamic threshold;

[0116] (3) According to the real-time received parasitic capacitance value data, use the dynamic threshold setting algorithm to calculate the current dynamic threshold. The formula of the dynamic threshold setting algorithm is:

[0117] ;

[0118] where, represents the threshold at time s, represents the threshold at time s - 1, represents the attenuation rate, represents the weight adjustment factor, which is used to reflect the importance or credibility of the current data point relative to other data points, represents the i-th real-time parasitic capacitance value data.

[0119] B3: Compare the real-time parasitic capacitance value data X with the dynamic threshold;

[0120] If , it is considered that the parasitic capacitance value data is normal;

[0121] If or , it is considered that the parasitic capacitance value data is abnormal;

[0122] B4: If it is detected that the parasitic capacitance value data is abnormal, the data processing module triggers an alarm and records the abnormal data. At the same time, according to the new real-time parasitic capacitance value data, the dynamic threshold is updated dynamically.

[0123] It should be noted that the threshold judgment mentioned in A5 is mainly about the implementation of the adaptive sampling rate function. The data change threshold here is used to determine whether the sampling interval needs to be adjusted. When the change amount between two adjacent sampling data exceeds this threshold, it means that the parasitic capacitance value may be undergoing significant changes. Therefore, it may be necessary to adjust the sampling rate to better capture this change. This threshold judgment is part of the sampling strategy, aiming to optimize the efficiency and accuracy of data acquisition. The dynamic threshold judgment mentioned in B2 and B3, on the other hand, is about judging whether the real-time parasitic capacitance value data exceeds the normal range. The dynamic threshold here is calculated dynamically based on the real-time received parasitic capacitance value data, aiming to reflect the actual state of the current circuit or system. If the real-time data exceeds the range of this dynamic threshold, it may mean that there are abnormalities or faults in the circuit. This dynamic threshold judgment is part of data analysis and fault detection, aiming to provide real-time fault warnings and abnormal records. It should also be noted that the threshold in A5 is preset and obtained based on statistical analysis. Its threshold judgment triggers the adjustment of the sampling interval, while the dynamic threshold in B2 is calculated dynamically according to real-time data and can reflect the state of the current system. Its dynamic threshold judgment triggers the recording of alarms and abnormal data, so it serves different functions and objectives.

[0124] The specific steps to determine the abnormal type and location of the package structure include:

[0125] C1: Load the physical model established in advance according to the package structure and parasitic capacitance characteristics, and obtain the abnormal parasitic capacitance value data obtained after dynamic threshold judgment ;

[0126] Among them, the establishment process of the physical model includes:

[0127] (1) Collect relevant information on the package structure and parasitic capacitance characteristics, including package size, material, layout, and traces;

[0128] (2) Based on the collected information, use the equivalent circuit model to establish the physical model. Among them, the equivalent circuit model is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here;

[0129] (3) Simulate the physical model to obtain the predicted value of the parasitic capacitance.

[0130] C2: Input into the pre-established physical model, and the physical model matches according to the input real-time to find the parasitic capacitance characteristics similar to those in the physical model database;

[0131] Further, the specific steps of C2 include:

[0132] (1) Load the pre-established physical model. Meanwhile, establish a database containing various packaging structures and parasitic capacitance characteristics. Each record in the database should include the parasitic capacitance value, the corresponding packaging structure parameters, and possible electromagnetic responses or characteristics.

[0133] (2) Input the detected abnormal parasitic capacitance value data into the physical model.

[0134] (3) Use the nearest neighbor search algorithm to search for parasitic capacitance characteristics similar to the input data in the database. The nearest neighbor search algorithm is prior art in this field and not a creative solution of this application, so it will not be elaborated here.

[0135] Among them, the nearest neighbor search algorithm calculates the distance or similarity between the input data and each record in the database, and finds the record with the smallest distance or the highest similarity to obtain similar parasitic capacitance characteristics.

[0136] (4) Output the matching result, including the parasitic capacitance characteristics most similar to the input data and their corresponding packaging structure parameters.

[0137] C3: Use a deep learning algorithm to analyze the matched to identify physical state changes in the packaging structure. The deep learning algorithm is prior art in this field and not a creative solution of this application, so it will not be elaborated here.

[0138] C4: Combine the identified physical state changes with the actual layout of the packaging structure, and through comparison and analysis, determine the abnormal type and specific location of the packaging structure.

[0139] Further, the specific steps of C4 include:

[0140] (1) Use sensors to capture physical state changes in the packaging structure in real time, such as temperature, vibration, stress, and preprocess the captured data, such as filtering, denoising, and feature extraction, to extract useful information.

[0141] (2) Consult design documents, use CAD software to view layout diagrams or conduct physical measurements to obtain the actual layout information of the packaging structure, including component positions, connection relationships, and material properties.

[0142] (3) Map the data of physical state changes to the coordinate system of the packaging structure to match and align the identified physical state changes with the layout information of the packaging structure.

[0143] (4) Use the differential analysis method to detect anomalies in the packaging structure, and determine the type of anomaly, such as cracks, deformations, overheating, and the specific location, based on the characteristics of the physical state changes and the layout information of the packaging structure;

[0144] (5) Use X-ray detection to verify and confirm the detected anomalies. Here, X-ray detection is the prior art content in the field and not the creative solution of this application, so it will not be elaborated here;

[0145] (6) According to the verification results, adjust the judgment of the anomaly type and location, and prepare the corresponding report or document.

[0146] C5: Generate an anomaly report according to the anomaly location and analysis results in C4. The anomaly report includes the anomaly type, location, possible causes, and recommended repair measures.

[0147] The specific steps for finding parasitic capacitance characteristics similar to those in the physical model database include:

[0148] D1: Load the pre-established physical model and initialize the parameters and variables in the physical model;

[0149] D2: Input the preprocessed abnormal parasitic capacitance value data into the physical model in the format required by the physical model;

[0150] D3: In the database of the physical model, use the distance-based search method to search according to the input real-time parasitic capacitance value data. Here, the distance-based search method is the prior art content in the field and not the creative solution of this application, so it will not be elaborated here;

[0151] D4: Use the similarity measurement method to find parasitic capacitance characteristics similar to the input data in the database of the physical model and output the matching result. Here, the similarity measurement method is the prior art content in the field and not the creative solution of this application, so it will not be elaborated here;

[0152] D5: Evaluate the matching result and further analyze the health status of the packaging structure according to the matching result.

[0153] Embodiment 2

[0154] Please refer to Figure 3 , Another embodiment provided by the present invention: A semiconductor packaging structure health monitoring system based on an embedded sensor, including:

[0155] A data acquisition module, an anomaly detection module, a backtracking and positioning module, and an alarm and repair module;

[0156] The data acquisition module determines the sensitive area of the parasitic capacitance through electromagnetic simulation, and deploys capacitance sensors within the sensitive area to collect parasitic capacitance value data and transmits the parasitic capacitance value data to the data processing module in real time;

[0157] The anomaly detection module is used to receive the collected parasitic capacitance value data, use the dynamic threshold setting algorithm to judge whether the real-time data exceeds the normal range, and trigger the anomaly detection process;

[0158] The reverse inference and positioning module is used to, when the real-time parasitic capacitance value data exceeds the normal range, use the pre-established physical model to reverse infer the possible physical state changes of the packaging structure, and determine the type and location of the anomaly in combination with the actual layout of the packaging structure;

[0159] The alarm and maintenance module is used to organize the determined anomaly type and location information into alarm information and send it to the operator. The operator checks and repairs the semiconductor packaging structure according to the alarm information in combination with the resources on the pre-established collaborative maintenance platform.

[0160] The data acquisition module includes: an electromagnetic simulation unit, a sensor deployment unit, a data acquisition unit, and a data transmission unit;

[0161] The electromagnetic simulation unit determines the sensitive area of the parasitic capacitance by establishing a three-dimensional model of the semiconductor packaging structure, performing mesh division, setting simulation parameters, and running the simulation;

[0162] The sensor deployment unit is used to select a suitable location within the sensitive area to deploy capacitance sensors according to the results of electromagnetic simulation, and ensure that the performance and accuracy of the sensors meet the requirements;

[0163] The data acquisition unit is used to read the parasitic capacitance value data from the capacitance sensor and perform preliminary preprocessing, such as filtering and amplification;

[0164] The data transmission unit is used to transmit the preprocessed parasitic capacitance value data to the data processing module wirelessly.

[0165] The anomaly detection module includes: a data reception unit, a dynamic threshold setting unit, and an anomaly detection unit;

[0166] The data reception unit is used to receive the parasitic capacitance value data transmitted from the data acquisition and transmission module;

[0167] The dynamic threshold setting unit is used to dynamically adjust the normal range threshold of the parasitic capacitance value according to historical data and the current environment;

[0168] The anomaly detection unit is used to use the dynamic threshold setting algorithm to judge whether the real-time data exceeds the normal range and trigger the corresponding anomaly detection process.

[0169] The reverse inference and positioning module includes: a physical model reverse inference unit and an anomaly positioning unit;

[0170] The physical model reverse inference unit is used to reverse infer the physical state change of the packaging structure by using a pre-established physical model according to the change situation of the parasitic capacitance value data;

[0171] The anomaly positioning unit determines the type and specific location of the anomaly by combining the reverse inference result and the actual layout of the packaging structure.

[0172] The alarm and maintenance module includes: an alarm information generation unit, an alarm information sending unit, and a collaborative maintenance unit;

[0173] The alarm information generation unit is used to organize the anomaly type and location information into alarm information and ensure the accuracy and integrity of the information;

[0174] The alarm information sending unit is used to send the generated alarm information to the operator through appropriate channels, such as mobile devices and emails;

[0175] The collaborative maintenance unit provides necessary maintenance resources and technical support for the operator by docking with a pre-established collaborative maintenance platform.

[0176] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0178] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make changes, modifications, substitutions, and variations to the above embodiments without departing from the spirit and scope of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A semiconductor package structure health monitoring method based on embedded sensors, characterized in that: include: Determine the sensitive area of ​​parasitic capacitance by electromagnetic simulation method, collect parasitic capacitance value data in the sensitive area by using capacitance sensor, and transmit the collected parasitic capacitance value data to the data processing module; After receiving the parasitic capacitance value data, the data processing module uses a dynamic threshold setting algorithm to determine whether the real-time parasitic capacitance value data exceeds a normal range; When the real-time parasitic capacitance value data exceeds the normal range, the pre-established physical model is used to reversely infer the physical state change of the package structure according to the change of the parasitic capacitance value data, and the abnormal type and location of the package structure are determined by combining the reverse inference result and the actual layout of the package structure; The determined abnormality type and location information are organized into alarm information and sent to the operator. The operator inspects and repairs the semiconductor packaging structure based on the alarm information and the resources on the pre-established collaborative maintenance platform.

2. The semiconductor package structure health monitoring method based on embedded sensors according to claim 1, characterized in that: The capacitive sensor has an adaptive sampling rate and automatically adjusts the sampling interval. The process of the capacitive sensor collecting parasitic capacitance value data includes: A1: Use electromagnetic simulation software to model the circuit and simulate the generation and distribution of parasitic capacitance; A2: Determine the sensitive area of ​​parasitic capacitance according to the simulation results; A3: Configure the parameters of the capacitive sensor according to the characteristics of the sensitive area and the measurement requirements; A4: Install the capacitive sensor in the sensitive area and calibrate the capacitive sensor; A5: According to the calibrated capacitance sensor, collect parasitic capacitance value data, and automatically adjust the sampling interval according to the adaptive sampling rate function ,in, represents the adjusted sampling interval, represents the original sampling frequency, Represents the adjustment factor.

3. The semiconductor package structure health monitoring method based on embedded sensors according to claim 2, characterized in that: The adaptive sampling rate function in A5 automatically adjusts the sampling interval including: A5.1: Setting the adaptive sampling rate parameters of the capacitive sensor, including the data change threshold , sampling rate upper and lower limits and , Sampling interval adjustment step and triggering adaptive adjustment conditions ,in, Indicates the newly collected parasitic capacitance data value, Indicates the old parasitic capacitance data value, Indicates the set data change rate threshold; A5.2: Set the sampling rate of the capacitive sensor to the preset initial value ,and ; A5.3: Start the capacitance sensor and start collecting parasitic capacitance data. Take samples; A5.4: During the data collection process, the parasitic capacitance value data is monitored in real time, and the change between two adjacent sampling data is obtained by the difference calculation method. , and compare the calculated data change with Make comparisons; like , then continue to collect data at the current sampling rate; like , then adjust the step size according to the preset sampling interval , automatically adjust the sampling interval, and update the sampling rate of the capacitive sensor according to the adjusted sampling interval.

4. The method for semiconductor package structure health monitoring based on embedded sensors as claimed in claim 3, characterized in that: The determination process of the dynamic threshold setting algorithm includes: B1: The data processing module receives real-time parasitic capacitance value data from the capacitance sensor and initializes dynamic threshold parameters, which include an initial threshold, a threshold adjustment factor, and an iteration number; B2: Use the dynamic threshold setting algorithm to calculate the current dynamic threshold based on the parasitic capacitance value data received in real time ,in, and Respectively represent the lower and upper limits of the dynamic threshold; B3: Compare the real-time parasitic capacitance value data X with the dynamic threshold; like , then the parasitic capacitance value data is considered normal; like or , then the parasitic capacitance value data is considered abnormal; B4: If the parasitic capacitance value data is judged to be abnormal, the data processing module triggers an alarm and records the abnormal data. At the same time, the dynamic threshold is dynamically updated according to the new real-time parasitic capacitance value data.

5. The semiconductor package structure health monitoring method based on embedded sensors according to claim 4, characterized in that: The specific steps of determining the abnormal type and location of the package structure include: C1: Load the physical model pre-established based on the package structure and parasitic capacitance characteristics, and obtain the abnormal parasitic capacitance value data obtained after dynamic threshold judgment. ; C2: Input into the pre-established physical model, the physical model is based on the real-time input Perform matching to find parasitic capacitance characteristics similar to those in the physical model database; C3: Using deep learning algorithms to match Perform analysis to identify changes in the physical state of the package structure; C4: Combine the identified physical state changes with the actual layout of the package structure, and determine the abnormal type and specific location of the package structure through comparison and analysis; C5: Generate an exception report based on the exception location and analysis results in C4, where the exception report includes the exception type, location, possible cause, and recommended repair measures.

6. The method for semiconductor package structure health monitoring based on embedded sensors according to claim 5, characterized in that: The specific steps of searching for parasitic capacitance characteristics similar to those in the physical model database include: D1: Load the pre-established physical model and initialize the parameters and variables in the physical model; D2: Input the pre-processed abnormal parasitic capacitance value data into the physical model in the format required by the physical model; D3: In the database of the physical model, a distance-based search method is used to search according to the input real-time parasitic capacitance value data; D4: Use the similarity measurement method to find parasitic capacitance characteristics similar to the input data in the database of the physical model and output the matching results; D5: Evaluate the matching results and further analyze the health status of the packaging structure based on the matching results.

7. A semiconductor package structure health monitoring system based on embedded sensors, which is used to implement the semiconductor package structure health monitoring method based on embedded sensors as described in any one of claims 1 to 6, characterized in that: include: Data acquisition module, anomaly detection module, reverse thrust and positioning module, alarm and maintenance module; The data acquisition module determines the sensitive area of ​​the parasitic capacitance through electromagnetic simulation, and deploys a capacitance sensor in the sensitive area to collect parasitic capacitance value data, and transmits the parasitic capacitance value data to the data processing module in real time; The anomaly detection module is used to receive the collected parasitic capacitance value data, use a dynamic threshold setting algorithm to determine whether the real-time data exceeds a normal range, and trigger an anomaly detection process; The reverse inference and positioning module is used to reverse the possible physical state changes of the packaging structure using a pre-established physical model when the real-time parasitic capacitance value data exceeds the normal range, and determine the abnormality type and location in combination with the actual layout of the packaging structure; The alarm and maintenance module is used to organize the determined abnormality type and location information into alarm information and send it to the operator. The operator inspects and repairs the semiconductor packaging structure based on the alarm information and the resources on the pre-established collaborative maintenance platform.

8. The semiconductor package structure health monitoring system based on embedded sensors as claimed in claim 7, characterized in that: The anomaly detection module includes: a data receiving unit, a dynamic threshold setting unit, and an anomaly detection unit; The data receiving unit is used to receive the parasitic capacitance value data transmitted from the data acquisition and transmission module; The dynamic threshold setting unit is used to dynamically adjust the normal range threshold of the parasitic capacitance value according to historical data and current environment; The anomaly detection unit is used to use a dynamic threshold setting algorithm to determine whether the real-time data exceeds a normal range and trigger a corresponding anomaly detection process.

9. The semiconductor package structure health monitoring system based on embedded sensors as claimed in claim 8, characterized in that: The reverse inference and positioning module includes: a physical model reverse inference unit and an abnormality positioning unit; The physical model inverse inference unit is used to infer the physical state change of the packaging structure using a pre-established physical model according to the change of the parasitic capacitance value data; The abnormality locating unit determines the type and specific location of the abnormality by combining the inverse deduction result and the actual layout of the packaging structure.

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