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

Abstract

The present invention discloses a semiconductor package structure health monitoring method and system based on embedded sensors, belonging to the field of semiconductor technology. The method specifically includes: determining parasitic capacitance sensitive areas through electromagnetic simulation, and deploying capacitance sensors in these areas 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 reversed using a physical model to determine the type and location of the abnormality; the system organizes the abnormality information into alarm information and sends it to the operator so that the operator can use the resources of the collaborative maintenance platform to perform inspection and maintenance, thereby improving the efficiency and accuracy of health monitoring of semiconductor package structures.

Description

Technical Field

[0001] The present invention belongs to the field of semiconductor measurement technology, and in particular to a semiconductor packaging structure health monitoring method and system based on embedded sensors. Background Art

[0002] Semiconductor packaging is the process of converting tested wafers into individual chips according to product model and functional requirements. With the continuous advancement of semiconductor technology, the reliability and stability requirements for semiconductor packaging structures are becoming increasingly stringent. In semiconductor packaging, parasitic capacitance forms between the chip and the packaging material, as well as between different metal layers. The capacitance of these parasitic capacitances is closely related to the physical state of the package structure. However, there is currently limited research and application on using parasitic capacitance characteristics to monitor the health of semiconductor packaging structures. This makes it difficult to promptly and effectively detect potential problems in the package structure, such as delamination and material degradation. This can lead to performance degradation or even failure of semiconductor devices during use due to packaging structure failures.

[0003] For example, Chinese patent publication number CN116609629A discloses a power semiconductor device health monitoring circuit and method, including: a drive circuit for receiving a pulse drive signal output by a control chip and outputting a drive signal to control the operation of the device under test; a first switch tube for receiving a first pulse signal from the control chip and controlling the on / off state of the first switch tube according to the first pulse signal; a second switch tube for receiving a second pulse signal from the control chip and controlling the on / off state of the second switch tube according to the second pulse signal; a current source for providing excitation current to the first and second switch tubes; and a voltage sampling module for sampling the voltage of the device under test. This method solves the problems of high bandwidth requirements for measurement circuits, low measurement accuracy, and difficulty in practical engineering applications, achieving accurate measurement of threshold voltage.

[0004] The above existing technologies all have the following problems: the scope of application is limited; 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, material aging, etc., which may not be fully reflected by simple current and voltage measurements; and the ability to provide fault warning may be weak. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes a semiconductor packaging structure health monitoring method and system based on embedded sensors. Parasitic capacitance sensitive areas are determined through electromagnetic simulation, and capacitance sensors are deployed in these areas 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 changes of the packaging structure are reversed 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 he can use the resources of the collaborative maintenance platform to perform inspection and maintenance. This improves the efficiency and accuracy of health monitoring of semiconductor packaging structures.

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

[0007] A semiconductor package structural health monitoring method based on embedded sensors includes:

[0008] Determine the sensitive area of ​​parasitic capacitance by electromagnetic simulation method, collect parasitic capacitance value data in the sensitive area using capacitance sensor, 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 a normal range;

[0010] When the real-time parasitic capacitance data exceeds the normal range, the pre-established physical model is used to infer the possible physical state changes of the package structure based on the changes in the parasitic capacitance data. The inference results are combined with the actual layout of the package structure to determine the type and location of the abnormality in the package structure.

[0011] 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.

[0012] Specifically, 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:

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

[0014] A2: Determine the sensitive area of ​​parasitic capacitance based on the simulation results;

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

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

[0017] A5: Collect parasitic capacitance data based on the calibrated capacitance sensor and automatically adjust the sampling interval according to the adaptive sampling rate function ,in, represents the adjusted sampling interval, represents the original sampling frequency, Indicates the adjustment factor.

[0018] Specifically, the adaptive sampling rate function in A5 automatically adjusts the sampling interval including:

[0019] A5.1: Set the adaptive sampling rate parameters of the capacitive sensor, including the data change threshold , upper and lower limits of sampling rate 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;

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

[0021] A5.3: Start the capacitance sensor and start collecting parasitic capacitance data. Take samples;

[0022] A5.4: During data acquisition, monitor the changes in parasitic capacitance data in real time and calculate the difference between two adjacent sampling data. , and compare the calculated data change with Make comparisons;

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

[0024] 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.

[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, which include an initial threshold, a threshold adjustment factor, and an iteration number.

[0027] 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 Represent the lower and upper limits of the dynamic threshold respectively;

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

[0029] like , then the parasitic capacitance value data is considered normal;

[0030] like or , then the parasitic capacitance value data is considered abnormal;

[0031] 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.

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

[0033] 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. ;

[0034] C2: Input into the pre-established physical model, the physical model will be based on the real-time input Perform matching to find parasitic capacitance characteristics similar to those in the physical model database;

[0035] C3: Using deep learning algorithms to match Perform analysis 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 determine the type and specific location of the abnormality through comparison and analysis;

[0037] C5: Generate an exception report based on the exception location and analysis results in C4, which includes the exception type, location, possible cause and recommended repair measures.

[0038] Specifically, the specific steps of searching for parasitic capacitance characteristics similar to those 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 pre-processed abnormal parasitic capacitance value data into the physical model in the format required by the physical model;

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

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

[0043] D5: Evaluate the matching results and further analyze the health of the package structure based on the matching results.

[0044] Semiconductor package structure health monitoring system based on embedded sensors, including: data acquisition module, anomaly detection module, reverse estimation and positioning module, alarm and maintenance module;

[0045] The data acquisition module determines the sensitive area of ​​parasitic capacitance through electromagnetic simulation, and deploys capacitance sensors 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;

[0046] 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 the normal range, and trigger the anomaly detection process;

[0047] The reverse inference and positioning module is used to reversely infer possible physical state changes of the package structure using a pre-established physical model when the real-time parasitic capacitance value data exceeds the normal range, and determine the type and location of the abnormality based on the actual layout of the package structure;

[0048] 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.

[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 current environment;

[0052] 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.

[0053] Specifically, the reverse inference and positioning module includes: a physical model reverse inference unit and an anomaly positioning unit;

[0054] The physical model 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;

[0055] 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.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The present invention proposes a semiconductor package structure health monitoring method based on embedded sensors. By using electromagnetic simulation to determine sensitive areas and using capacitive sensors to collect parasitic capacitance data in real time, the method achieves accurate monitoring of the health status of the semiconductor package structure. By using a dynamic threshold setting algorithm to determine whether the data is abnormal, potential problems can be discovered at an early stage, thereby effectively improving the reliability and stability of the semiconductor package structure.

[0058] 2. The present invention proposes a semiconductor package structure health monitoring method based on embedded sensors. Combining the physical model and the actual layout of the package structure, it can accurately determine the type and location of the abnormality and send alarm information to the operator in a timely manner. This not only helps the operator to quickly locate and solve the problem, but also combines the resources on the collaborative maintenance platform to improve maintenance efficiency and accuracy and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of a semiconductor package structure health monitoring method based on embedded sensors according to the present invention;

[0060] Figure 2 This is a flow chart showing the principle of the semiconductor package structure health monitoring method based on embedded sensors of the present invention;

[0061] Figure 3 This is an architecture diagram of the semiconductor packaging structure health monitoring system based on embedded sensors of the present invention. DETAILED DESCRIPTION

[0062] Example 1

[0063] See also Figure 1 and Figure 2The present invention provides an embodiment of a semiconductor package structure health monitoring method based on an embedded sensor, wherein the implementation process includes S101-S104, and the specific steps include:

[0064] S101: determining a sensitive area of ​​parasitic capacitance using an electromagnetic simulation method, collecting parasitic capacitance value data in the sensitive area using a capacitance sensor, and transmitting the collected parasitic capacitance value data to a 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 simulation parameters such as frequency range and material properties;

[0067] (2) Run the simulation to analyze the distribution of parasitic capacitance and determine the sensitive areas of parasitic capacitance based on the simulation results;

[0068] (3) Place capacitive sensors in the sensitive areas determined by electromagnetic simulation to ensure stable and reliable connections between the sensors and the circuit under test or PCB layout;

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

[0070] (5) Start data acquisition, record parasitic capacitance data, and transmit the collected capacitance 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 such as cleaning, conversion, and formatting on the data.

[0072] It should be noted that the process of a capacitive sensor collecting parasitic capacitance value data is essentially the process of sensing and measuring the capacitance changes caused by the sensitive area of ​​the parasitic capacitance through the sensor. Specifically, the sensitive area of ​​the parasitic capacitance is 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 principle of capacitive coupling, that is, the capacitance formed between two metal plates will change with the change of the distance and relative position between them, where 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 a normal range;

[0074] S103: When the real-time parasitic capacitance value data exceeds the normal range, a pre-established physical model is used to infer possible physical state changes of the package structure based on the changes in the parasitic capacitance value data. The inference result is combined with the actual layout of the package structure to determine the type and location of the abnormality of the package structure.

[0075] S104: The determined abnormality type and location information are organized into alarm information and sent to an 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.

[0076] Furthermore, 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 infers the possible physical state changes of the package structure, the system will organize these abnormal information, including the abnormality type, location, possible physical state changes, etc., into structured alarm information;

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

[0079] (3) After receiving the alarm information, the operator carefully reads and understands the content, especially the abnormality type and location information. Then, based on the alarm information, the operator logs into the pre-established collaborative maintenance platform, which may contain information such as maintenance manuals, spare parts inventory, and maintenance personnel schedules.

[0080] (4) The operator combines the alarm information and the resources on the collaborative maintenance platform to develop a detailed maintenance plan, including determining the maintenance personnel, spare parts requirements, maintenance time, etc.

[0081] (5) Maintenance personnel shall inspect and repair the semiconductor packaging structure in accordance with the maintenance plan. During the maintenance process, they may need to use specific tools and equipment and follow specific operating procedures and safety regulations;

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

[0083] For example, in a semiconductor packaging structure for mobile communication equipment, electromagnetic simulation is used to determine that the four corners of the chip and the packaging material and the three key connection parts between different metal layers are key sensitive areas of parasitic capacitance, and capacitance sensors based on MEMS technology are installed in these areas respectively; the capacitance sensor is connected to the data acquisition module through micro-wires, and the data acquisition module collects capacitance value data every 100 milliseconds and transmits the data to the data processing module; the data processing module pre-establishes a parasitic capacitance value database of the semiconductor packaging structure under the normal operating temperature range and normal operating voltage range, wherein the normal operating temperature range is -20°C to 85°C, and the normal operating voltage range is 1.8V. to 3.3V; when the data processing module receives the 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 uses the pre-established physical model for analysis; after analysis, it is determined that it may be due to slight delamination of the packaging structure at this position, which causes the distance between the chip and the packaging material to change, thereby causing the parasitic capacitance value to increase; the data processing module sends the abnormal information to the alarm module, and the alarm module reminds the operator through sound and light alarms; at the same time, the data processing module stores the abnormal data and analysis results in the data storage module; the operator inspects and repairs the semiconductor packaging structure according to the alarm information, thereby avoiding the performance degradation of the communication equipment due to the failure of the packaging structure; through the above implementation method, the health status of the semiconductor packaging structure is effectively monitored, and the reliability and stability of the semiconductor device are improved.

[0084] The capacitive sensor has an adaptive sampling rate and automatically adjusts the sampling interval. The process of collecting parasitic capacitance value data by the capacitive sensor 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) Identify the circuit structure, operating frequency, and parasitic capacitance area to be simulated. At the same time, select the electromagnetic simulation software COMSOL based on the simulation requirements and software characteristics;

[0088] (2) Based on the actual circuit layout, establish the geometric model of the circuit in the simulation software, including signal lines, power lines, ground lines, and component packaging, and set the correct material properties for different parts of the circuit, such as the conductivity of the conductor and the dielectric constant of the insulating medium;

[0089] (3) Based on the simulation objectives, define the area where parasitic capacitance needs to be extracted in the established circuit geometry model and set the simulation parameters, including operating frequency, simulation accuracy, and meshing. It should be noted that meshing is crucial for accurate simulation of parasitic capacitance because the size of parasitic capacitance is closely related to the distance and shape between conductors.

[0090] (4) Run the simulation in the software, which will calculate the distribution of parasitic capacitance 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 process;

[0092] (6) After the simulation is completed, check the distribution of parasitic capacitance in the software, including the size and location of the parasitic capacitance.

[0093] A2: Determine the sensitive area of ​​parasitic capacitance based on the simulation results;

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

[0095] (1) Export the simulation results of parasitic capacitance from electromagnetic simulation software. These results usually include the distribution diagram and value table of parasitic capacitance;

[0096] (2) Carefully analyze the derived parasitic capacitance results. Pay attention to the size, distribution, and relationship of the parasitic capacitance with other parts of the circuit.

[0097] (3) Based on the size and distribution of parasitic capacitance, identify areas with large parasitic capacitance. These areas are usually sensitive areas of parasitic capacitance and have a greater impact on circuit performance;

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

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

[0100] A4: Install the capacitive sensor in the sensitive area and calibrate the capacitive sensor. The calibration process is known in the art and does not constitute the inventive solution of this application, so it will not be described in detail here.

[0101] A5: Collect parasitic capacitance data based on the calibrated capacitance sensor and automatically adjust the sampling interval according to the adaptive sampling rate function ,in, represents the adjusted sampling interval, represents the original sampling frequency, Indicates the adjustment factor.

[0102] In the present invention, the adjustment coefficient is dynamically calculated based on the fluctuation range. First, the maximum and minimum values ​​of the data within a week are counted, and their difference is calculated as the fluctuation range of the data; then, the corresponding adjustment coefficient is set according to the size of the fluctuation range. For example, when the fluctuation range is less than 10% of the data average, the adjustment coefficient is set to 0.7; when the fluctuation range is between 10%-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 adaptive sampling rate function in A5 automatically adjusts the sampling interval including:

[0104] A5.1: Set the adaptive sampling rate parameters of the capacitive sensor, including the data change threshold , upper and lower limits of sampling rate 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;

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

[0106] A5.3: Start the capacitance sensor and start collecting parasitic capacitance data. Take samples;

[0107] A5.4: During the data acquisition process, the parasitic capacitance value data is monitored in real time, and the difference calculation method is used to calculate the parasitic capacitance value. Get the change between two adjacent sampling data , and compare the calculated data change with For comparison, and They represent two adjacent sampling data, and n represents the number of acquisitions;

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

[0109] 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.

[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, which include an initial threshold, a threshold adjustment factor, and an iteration number.

[0112] 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 Represent the lower and upper limits of the dynamic threshold respectively;

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

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

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

[0116] (3) Based on the parasitic capacitance value data received in real time, the current dynamic threshold is calculated using the dynamic threshold setting algorithm. The formula of the dynamic threshold setting algorithm is:

[0117] ;

[0118] in, represents the threshold at time s, represents the threshold at time s-1, represents the decay rate, Represents a 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] like , then the parasitic capacitance value data is considered normal;

[0121] like or , then the parasitic capacitance value data is considered abnormal;

[0122] B4: If an abnormal parasitic capacitance value data is detected, 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.

[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 between two adjacent sampling data exceeds this threshold, it means that the parasitic capacitance value may be undergoing significant changes. Therefore, the sampling rate may need to be adjusted to better capture this change. This threshold judgment is part of the sampling strategy, which aims to optimize the efficiency and accuracy of data acquisition. The dynamic threshold judgment mentioned in B2 and B3 is about whether the real-time parasitic capacitance value data exceeds the normal range. The dynamic threshold here is dynamically calculated based on the parasitic capacitance value data received in real time, and is intended 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 is an abnormality or fault in the circuit. This dynamic threshold judgment is part of data analysis and fault detection, and is intended to provide real-time fault warning and abnormality 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 dynamically calculated based on real-time data, which can reflect the current system state. Its dynamic threshold judgment triggers alarms and abnormal data recording, so it serves different functions and goals.

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

[0125] 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. ;

[0126] The process of establishing the physical model includes:

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

[0128] (2) Based on the collected information, a physical model is established using an equivalent circuit model. The equivalent circuit model is a prior art in this field and is not an inventive solution of the present application, and is not described in detail here.

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

[0130] C2: Input into the pre-established physical model, the physical model will be based on the real-time input Perform matching to find parasitic capacitance characteristics similar to those in the physical model database;

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

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

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

[0134] (3) Using a nearest neighbor search algorithm to search for parasitic capacitance characteristics similar to the input data in the database, wherein the nearest neighbor search algorithm is a prior art in this field and is not an inventive solution of the present application, and is not described in detail 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, so as to obtain similar parasitic capacitance characteristics.

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

[0137] C3: Using deep learning algorithms to match Perform analysis to identify changes in the physical state of the package structure. The deep learning algorithm is a prior art in this field and is not an inventive solution of the present application, so it will not be described in detail here.

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

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

[0140] (1) Use sensors to capture the physical state changes in the packaging structure in real time, such as temperature, vibration, and 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 drawings, or perform physical measurements to obtain the actual layout information of the package structure, including component locations, connection relationships, and material properties;

[0142] (3) Mapping the data of the physical state change to the coordinate system of the package structure to match and align the identified physical state change with the layout information of the package structure;

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

[0144] (5) Using X-ray detection to verify and confirm the detected anomaly. X-ray detection is a prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0145] (6) Based on the verification results, adjust the judgment of the abnormality type and location and prepare corresponding reports or documents.

[0146] C5: Generate an exception report based on the exception location and analysis results in C4, which includes the exception type, location, possible cause and recommended repair measures.

[0147] The specific steps to find 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 pre-processed abnormal parasitic capacitance value data into the physical model in the format required by the physical model;

[0150] D3: In the physical model database, a distance-based search method is used to search based on the input real-time parasitic capacitance value data. The distance-based search method is a prior art in this field and does not constitute an inventive solution of the present application, and is not described in detail here.

[0151] D4: Using a similarity measurement method to find parasitic capacitance characteristics similar to the input data in the physical model database, and outputting a matching result. The similarity measurement method is prior art in this field and does not constitute an inventive solution of the present application, and is not described in detail here.

[0152] D5: Evaluate the matching results and further analyze the health of the package structure based on the matching results.

[0153] Example 2

[0154] See also Figure 3 Another embodiment of the present invention provides a semiconductor package structure health monitoring system based on embedded sensors, comprising:

[0155] Data acquisition module, anomaly detection module, reverse estimation and positioning module, alarm and maintenance module;

[0156] The data acquisition module determines the sensitive areas of parasitic capacitance through electromagnetic simulation, deploys capacitance sensors in the sensitive areas 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 determine 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 use the pre-established physical model to reversely infer the possible physical state changes of the package structure when the real-time parasitic capacitance value data exceeds the normal range, and determine the type and location of the anomaly based on the actual layout of the package structure;

[0159] 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.

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

[0161] The electromagnetic simulation unit builds a three-dimensional model of the semiconductor package structure, performs meshing, sets simulation parameters, and runs simulations to determine sensitive areas of parasitic capacitance.

[0162] The sensor deployment unit is used to select appropriate locations to deploy capacitive sensors in sensitive areas based on 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 pre-processed parasitic capacitance value data to the data processing module in a wireless manner.

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

[0166] A data receiving unit, used to receive parasitic capacitance value data transmitted from the data acquisition and transmission module;

[0167] A dynamic threshold setting unit, used to dynamically adjust the normal range threshold of the parasitic capacitance value based on historical data and current environment;

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

[0169] The inverse inference and positioning module includes: physical model inverse inference unit and anomaly positioning unit;

[0170] A physical model 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;

[0171] The abnormality locating unit determines the type and specific location of the abnormality by combining the inverse result and the actual layout of the package structure.

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

[0173] Alarm information generation unit, used to organize abnormality type and location information into alarm information and ensure the accuracy and completeness of the information;

[0174] An 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 operators with necessary maintenance resources and technical support by connecting to the pre-established collaborative maintenance platform.

[0176] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

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

[0178] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention. These are all protected by 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 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 changes of the package structure based on the changes in the parasitic capacitance value data. The reverse inference results and the actual layout of the package structure are combined to determine the type and location of the package structure anomaly. The identified abnormality type and location information is compiled 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. The capacitive sensor has an adaptive sampling rate and automatically adjusts the sampling interval. The process of collecting parasitic capacitance value data by the capacitive sensor 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 based on simulation results; A3: Configure the parameters of the capacitive sensor according to the characteristics of the sensitive area and measurement requirements; A4: Install the capacitive sensor in the sensitive area and calibrate the capacitive sensor; A5: Collect parasitic capacitance data based on the calibrated capacitance sensor 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; The adaptive sampling rate function in A5 automatically adjusts the sampling interval including: A5.1: Set the adaptive sampling rate parameters of the capacitive sensor, including the data change threshold , upper and lower limits of sampling rate 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 data acquisition, monitor the changes in parasitic capacitance data in real time and calculate the difference between two adjacent sampling data. , 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.

2. The semiconductor package structure health monitoring method based on embedded sensors according to claim 1, characterized in that: The judgment 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 Represent the lower and upper limits of the dynamic threshold respectively; 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.

3. The semiconductor package structure health monitoring method based on embedded sensors according to claim 2, 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 will be 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 type and specific location of the abnormality through comparison and analysis; C5: Generate an exception report based on the exception location and analysis results in C4, which includes the exception type, location, possible cause and recommended repair measures.

4. The semiconductor package structure health monitoring method based on embedded sensors according to claim 3, 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 physical model database, 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 physical model database and output the matching results; D5: Evaluate the matching results and further analyze the health of the package structure based on the matching results.

5. 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 according to any one of claims 1 to 4, characterized in that: include: Data acquisition module, anomaly detection module, reverse estimation and positioning module, alarm and maintenance module; The data acquisition module determines the sensitive area of ​​parasitic capacitance through electromagnetic simulation, and deploys capacitance sensors 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 the normal range, and trigger the anomaly detection process; The reverse inference and positioning module is used to reversely infer possible physical state changes of the package structure using a pre-established physical model when the real-time parasitic capacitance value data exceeds the normal range, and determine the type and location of the abnormality based on the actual layout of the package 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.

6. The semiconductor package structure health monitoring system based on embedded sensors according to claim 5, 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.

7. The semiconductor package structure health monitoring system based on embedded sensors according to claim 6, characterized in that: The reverse inference and positioning module includes: a physical model reverse inference unit and an anomaly positioning unit; The physical model 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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