A high-precision low-power chip stacking sensor packaging method and system

Abstract

The application discloses a high-precision and low-power chip stacked sensor packaging method and system, and belongs to the field of information technology. The method comprises the following steps: collecting glue performance data in a real-time chip stacking and die bonding process, obtaining an initial signal transmission precision index, and determining a preliminary output loss evaluation value; processing a glue formula and a curing time sequence, and determining a glue performance configuration; calculating a high-temperature-resistant stability coefficient by simulating a signal processing link under a high-temperature environment; when the high-temperature-resistant stability coefficient is greater than a stability threshold value, a candidate packaging scheme is generated; the average value of the drift of all samples of each candidate packaging scheme is calculated, and a complete packaging scheme is generated; the application generates a complete packaging scheme by optimizing dispensing parameters and encapsulation processes and combining low-power configuration, significantly improves the signal stability and environmental adaptability of chip packaging, and provides reliable technical support for high-performance sensors.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a high-precision low-power chip stacked sensor packaging method and system. BACKGROUND

[0002] Pressure sensors are increasingly important in the field of smart life, as they can accurately perceive physical height and are widely used in indoor navigation, altitude measurement, fitness monitoring, and weather forecasting, directly affecting the intelligent process of consumer electronics, industry, and automotive electronics. However, existing solutions face significant limitations in practical applications. Traditional pressure sensor packaging processes and calibration techniques often struggle to balance low power consumption and high precision, especially in high-temperature or complex environments. This leads to signal output deviation under extreme conditions, limiting the reliability of the sensor in high-demand scenarios. The core challenge lies in the optimization of chip packaging and signal processing. The stacking and bonding process of pressure sensor chips has very high requirements for glue performance and curing conditions. The choice of glue material and curing state directly affects the accuracy of signal transmission, and even slight deviations can lead to increased output loss, making it difficult to meet high-precision requirements. In addition, the air-tightness of the packaging process is also crucial, and inaccurate dispensing parameters and encapsulation processes can lead to a decrease in pressure and temperature sensitivity, especially in dynamic environments, where the sensor's response to small pressure changes will be significantly weakened. For example, in smart wearable devices, users need to obtain high-precision altitude data in real-time when climbing mountains. If the sensor has deviations due to poor packaging or insufficient calibration, it may result in incorrect height readings, directly affecting user experience and even safety. Therefore, how to optimize the chip stacking and bonding technology and high-airtightness packaging process to reduce signal output loss and improve pressure and temperature sensitivity has become a key issue in achieving low-power, high-temperature-resistant, and high-precision pressure sensors. SUMMARY

[0003] The present application provides a high-precision low-power chip stacked sensor packaging method and system to solve the problems raised in the background.

[0004] To solve the above technical problems, the present application provides the following technical solutions:

[0005] A high-precision low-power chip stacked sensor packaging method, the method comprising:

[0006] Real-time acquisition of glue performance data during the chip stacking and bonding process, obtaining the initial signal transmission accuracy index, and determining the preliminary output loss evaluation value;

[0007] Processing the glue formula and curing time sequence, and determining the glue performance configuration;

[0008] The high-temperature stability coefficient was calculated by simulating the signal processing process under high-temperature conditions.

[0009] When the high-temperature stability coefficient is greater than the stability threshold, candidate packaging solutions are generated;

[0010] Calculate the average drift of all samples for each candidate packaging scheme and generate a complete packaging scheme.

[0011] As a preferred solution for a high-precision, low-power chip stacking sensor packaging method and system, the real-time acquisition of adhesive performance data during the chip stacking and bonding process, obtaining the initial signal transmission accuracy index, and determining the preliminary output loss assessment value includes:

[0012] The adhesive and curing data during the chip stacking and bonding process are collected in real time by sensors. The adhesive data includes the type of adhesive material and the proportion of adhesive components. The curing data includes the curing temperature and curing time to obtain adhesive performance data.

[0013] The adhesive performance data is input into a pre-trained viscosity classification model, which classifies the viscosity characteristics of the adhesive and outputs the viscosity characteristic level of the adhesive.

[0014] The bonding process control parameters are generated according to a preset parameter template, wherein the parameter template includes parameter generation rules for generating bonding process control parameters according to the viscosity characteristics of the adhesive.

[0015] When the bonding process control parameters are within the preset parameter range, the curing characteristics of the adhesive performance data are extracted, including curing temperature characteristics and curing time characteristics.

[0016] The solidified features are input into the transmission prediction model, and the transmission prediction model outputs the corresponding initial signal transmission accuracy index.

[0017] When the initial signal transmission accuracy index is less than the preset transmission accuracy threshold, the curing time is adjusted by changing the glue component ratio data, and the curing data is re-acquired to obtain updated glue performance data and signal transmission accuracy index.

[0018] Based on the updated signal transmission accuracy index, the preliminary output loss assessment value is calculated using the following formula:

[0019]

[0020] Among them, L initial This indicates the initial output loss value, where M represents the total number of adhesive performance data collected.

[0021] P adhesive,i T represents the viscosity characteristic level of the i-th adhesive performance data.cure,i S represents the curing time of the i-th adhesive performance data. transmission,i α represents the initial signal transmission accuracy index corresponding to the i-th glue performance data. loss This represents the loss assessment coefficient.

[0022] As a preferred embodiment of a high-precision, low-power chip stacked sensor packaging method and system, the process of processing the adhesive formulation and curing time sequence, and determining the adhesive performance configuration, includes:

[0023] When the initial output loss assessment value is greater than the assessment threshold, the adhesive formulation is optimized;

[0024] Key formulation features, including adhesive component ratios and curing time, are extracted from the adhesive performance data, and a set of formulation parameters is constructed.

[0025] When the viscosity characteristic level of the adhesive corresponding to the formulation in the formulation parameter set is greater than the preset maximum viscosity threshold, cluster analysis is performed on the formulation based on the adhesive viscosity characteristic level and historical curing uniformity score.

[0026] Formulas with the highest historical curing uniformity scores and adhesive viscosity characteristics within the preset range were selected from each cluster as candidate optimized formula parameters.

[0027] The temperature curve and ambient humidity of the curing oven are dynamically adjusted by a real-time monitoring system, and the adjusted curing process parameters are recorded. The curing process parameters include temperature-time series and humidity values.

[0028] Curing experiments were conducted under the adjusted curing process parameters, and the adhesive performance data of the cured adhesive samples were collected to form a curing condition optimization dataset.

[0029] Using data analysis tools, we comprehensively analyze the adhesive performance data in the curing condition optimization dataset to evaluate compatibility with substrates or components and signal transmission quality.

[0030] Based on the evaluation results, a combination of adhesive formulation and curing process parameters that meets all preset key performance indicators is selected to form an adhesive performance configuration scheme.

[0031] As a preferred solution for a high-precision, low-power chip stacked sensor packaging method and system, the step of calculating the high-temperature stability coefficient by simulating the signal processing stage under high-temperature conditions includes:

[0032] Based on the adhesive performance configuration scheme, the high-temperature stability coefficient is calculated by simulating the signal processing stage under high-temperature conditions. The specific formula is as follows:

[0033] K stability =(β×σsignal ) / (ΔT×δ deform )

[0034] Among them, K stability σ represents the high-temperature stability coefficient, ΔT represents the temperature change under high-temperature conditions, and σ represents the temperature resistance coefficient. signal β represents the standard deviation of the signal, β represents the thermal sensitivity coefficient of the material, and δ represents the standard deviation of the signal. deform This represents the deformation parameters of the adhesive layer.

[0035] As a preferred embodiment of a high-precision, low-power chip stacked sensor packaging method and system, the step of generating candidate packaging schemes when the high-temperature stability coefficient is greater than the stability threshold includes:

[0036] When the high-temperature stability coefficient corresponding to the adhesive performance configuration scheme is greater than the preset stability threshold, the dispensing parameters are obtained according to the fluid dynamics model. The dispensing parameters include dispensing pressure, dispensing speed and dispensing position coordinates. Based on the packaging structure size, viscosity characteristic level and adhesive surface tension, the control point sequence constructed by the dispensing parameters is output.

[0037] A sealing experiment was conducted based on a control point sequence. Sealing pressure data and adhesive curing morphology scanning data were collected. The sealing pressure data and adhesive curing morphology scanning data were then input into a preset airtightness prediction model, and the helium leak detection rate was output.

[0038] When the helium leak detection rate of the dispensing parameters is greater than or equal to the leak detection threshold, the spacing between control points is reduced according to the spacing control template.

[0039] When the helium leak detection rate of the dispensing parameters is less than the leak detection threshold and there are defects in the glue line, increase the spacing of the control points according to the spacing control template.

[0040] The process continues until the helium leak detection rate of the dispensing parameters is less than the leak detection threshold and the glue line is continuous and defect-free. Then, the updated control point sequence is output as the dispensing process sequence, and candidate packaging solutions are generated by combining the glue performance configuration scheme.

[0041] As a preferred embodiment of a high-precision, low-power chip stacked sensor packaging method and system, the step of calculating the average drift of all samples for each candidate packaging scheme and generating a complete packaging scheme includes:

[0042] The packaged sensor is packaged according to the candidate packaging scheme and qualified, and the packaged sensor is placed in a thermo-pressure vessel for full characteristic testing.

[0043] The raw data of the packaged sensor is collected, and the temperature-nonlinear compensation parameter lookup table is generated by the fitting algorithm to calibrate the signal accuracy.

[0044] Power consumption analysis is performed on the system that outputs calibrated signals. Power consumption is optimized by dynamically adjusting the sampling rate and intelligent sleep-wake strategy, and low-power configuration parameters are generated.

[0045] The packaged sensor was placed in a high-temperature environment, and the drift trend lines of the zero-point signal and sensitivity of the packaged sensor were calculated throughout the entire test cycle.

[0046] Calculate the average drift of all samples for each candidate packaging scheme, select the candidate packaging scheme with the smallest average drift, and generate a complete packaging scheme by combining the corresponding low-power configuration parameters.

[0047] A high-precision, low-power chip-mounted sensor packaging system, the system comprising:

[0048] The adhesive data acquisition and preliminary evaluation module is used to acquire adhesive performance data in real time during the chip stacking and bonding process, obtain the initial signal transmission accuracy index, and determine the preliminary output loss assessment value.

[0049] The adhesive performance configuration module is used to process adhesive formulations and curing time series to determine adhesive performance configuration.

[0050] The high-temperature stability testing module is used to calculate the high-temperature stability coefficient by simulating the signal processing process under high-temperature conditions.

[0051] The candidate module is used to generate candidate packaging solutions when the high-temperature stability coefficient is greater than the stability threshold.

[0052] The packaging module is used to calculate the average drift of all samples for each candidate packaging scheme and generate a complete packaging scheme.

[0053] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0054] This invention discloses a high-precision, low-power chip stacking sensor packaging method and system, comprising: real-time acquisition of adhesive performance data during the chip stacking and bonding process, obtaining an initial signal transmission accuracy index, and determining a preliminary output loss assessment value; processing the adhesive formulation and curing time series, and determining the adhesive performance configuration; calculating a high-temperature stability coefficient by simulating signal processing under high-temperature conditions; generating candidate packaging schemes when the high-temperature stability coefficient is greater than a stability threshold; calculating the average drift of all samples for each candidate packaging scheme and generating a complete packaging scheme; this invention significantly improves the signal stability and environmental adaptability of chip packaging by optimizing dispensing parameters and encapsulation processes and combining them with low-power configurations to generate a complete packaging scheme, providing reliable technical support for high-performance sensors. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0056] Figure 1 This is a flowchart of a high-precision, low-power chip stacking sensor packaging method according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of a high-precision, low-power chip stacked sensor packaging system according to an embodiment of the present invention. Detailed Implementation

[0058] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The following detailed description, in conjunction with the accompanying drawings and embodiments, further illustrates this application. It should be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0059] like Figure 1 As shown, this embodiment provides a high-precision, low-power chip stacking sensor packaging method, the method comprising:

[0060] Real-time acquisition of adhesive performance data during chip stacking and bonding process, obtaining initial signal transmission accuracy index, and determining preliminary output loss assessment value;

[0061] Process the adhesive formulation and curing time series, and determine the adhesive performance configuration;

[0062] The high-temperature stability coefficient was calculated by simulating the signal processing process under high-temperature conditions.

[0063] When the high-temperature stability coefficient is greater than the stability threshold, candidate packaging solutions are generated;

[0064] Calculate the average drift of all samples for each candidate packaging scheme and generate a complete packaging scheme.

[0065] Specifically, the real-time acquisition of adhesive performance data during the chip stacking and bonding process, obtaining the initial signal transmission accuracy index, and determining the preliminary output loss assessment value include:

[0066] The adhesive and curing data during the chip stacking and bonding process are collected in real time by sensors. The adhesive data includes the type of adhesive material and the proportion of adhesive components. The curing data includes the curing temperature and curing time to obtain adhesive performance data.

[0067] Using a high-precision temperature sensor and a mixing ratio monitoring system, data such as the material type of the adhesive, the mixing ratio of A / B components, the real-time curing temperature and duration are collected in real time, generating adhesive data and curing data and integrating them into structured adhesive performance data.

[0068] The adhesive performance data is input into a pre-trained viscosity classification model, which classifies the viscosity characteristics of the adhesive and outputs the viscosity characteristic level of the adhesive.

[0069] The collected adhesive performance data is input into a pre-trained viscosity classification model, which is a classifier built based on a convolutional neural network or gradient boosting tree algorithm. The model outputs the viscosity characteristic level of the adhesive under the current conditions, such as "low viscosity", "medium viscosity" and "high viscosity".

[0070] The bonding process control parameters are generated according to a preset parameter template. The parameter template includes parameter generation rules for generating bonding process control parameters based on the viscosity characteristics of the adhesive. For example, for low-viscosity adhesives, the dispensing pressure should be reduced and the curing time should be extended.

[0071] When the bonding process control parameters are within the preset parameter range, such as the dispensing pressure being between 120-400 kPa, the bonding process control parameters consist of dispensing speed, pressure, and temperature rise curve during the curing stage. The curing characteristics of the adhesive performance data are extracted, including curing temperature characteristics and curing time characteristics. The curing temperature characteristics include heating rate and peak holding time.

[0072] The fixed features are input into the transmission prediction model, which predicts the initial signal transmission accuracy index based on the time series regression algorithm. The transmission prediction model outputs the corresponding initial signal transmission accuracy index, which has a value range of 0-100.

[0073] When the initial signal transmission accuracy index is less than the preset transmission accuracy threshold, the transmission accuracy threshold can be 90 points, which can be set by the administrator. By changing the glue component ratio data, the curing time is adjusted, and the curing data is re-collected to obtain the updated glue performance data and signal transmission accuracy index. The update stops when the initial signal transmission accuracy index is greater than or equal to the transmission accuracy threshold.

[0074] Based on the updated signal transmission accuracy index, the preliminary output loss assessment value is calculated using the following formula:

[0075]

[0076] Among them, L initial This indicates the initial output loss value, M represents the total number of adhesive performance data collected, and P... adhesive,iT represents the viscosity characteristic level of the i-th adhesive performance data. cure,i S represents the curing time of the i-th adhesive performance data. transmission,i α represents the initial signal transmission accuracy index corresponding to the i-th glue performance data. loss This represents the loss assessment coefficient.

[0077] Specifically, the process of processing the adhesive formulation and curing time sequence, and determining the adhesive performance configuration, includes:

[0078] When the initial output loss assessment value is greater than the assessment threshold, the adhesive formulation is optimized;

[0079] Key formulation features, including adhesive component ratios and curing time, are extracted from the adhesive performance data. A set of formulation parameters is constructed by associating the key formulation features with corresponding performance results (such as signal transmission accuracy index and loss value) to form a multi-dimensional, tagged set of formulations, which serves as the basis for subsequent data analysis.

[0080] When the viscosity characteristic level of the adhesive corresponding to the formulation in the formulation parameter set is greater than the preset maximum viscosity threshold, cluster analysis is performed on the formulation based on the adhesive viscosity characteristic level and historical curing uniformity score, such as using K-Means or DBSCAN algorithm.

[0081] From each cluster, the formulation with the highest historical curing uniformity score and the adhesive viscosity characteristic level within the preset range was selected as the candidate optimized formulation.

[0082] The temperature curve and ambient humidity of the curing oven are dynamically adjusted by a real-time monitoring system, and the adjusted curing process parameters are recorded. The curing process parameters include temperature-time series and humidity values.

[0083] For each candidate formulation, the real-time monitoring system does not simply use its historical curing parameters, but rather performs dynamic optimization. The system controls the curing oven, fine-tuning its temperature profile, such as changing linear heating to stepped heating, or adjusting the heating rate and ambient humidity to match the current experimental environment. All adjusted parameters, including complete temperature-time series and humidity values, are precisely recorded as curing process parameters.

[0084] Curing experiments were conducted under the adjusted curing process parameters, and the adhesive performance data of the cured adhesive samples were collected to form a curing condition optimization dataset.

[0085] Using data analysis tools, we comprehensively analyze the adhesive performance data in the curing condition optimization dataset to evaluate compatibility with substrates or components and signal transmission quality.

[0086] Based on the evaluation results, a combination of adhesive formulation and curing process parameters that meets all preset key performance indicators is selected to form an adhesive performance configuration scheme.

[0087] Specifically, the calculation of the high-temperature stability coefficient by simulating the signal processing steps under high-temperature conditions includes:

[0088] Based on the adhesive performance configuration scheme, the high-temperature stability coefficient is calculated by simulating the signal processing stage under high-temperature conditions. The specific formula is as follows:

[0089] K stability =(β×σ signal ) / (ΔT×δ deform )

[0090] Among them, K stability σ represents the high-temperature stability coefficient, ΔT represents the temperature change under high-temperature conditions, and σ represents the temperature resistance coefficient. signal β represents the standard deviation of the signal, β represents the thermal sensitivity coefficient of the material, and δ represents the standard deviation of the signal. deform This represents the deformation parameters of the adhesive layer.

[0091] Specifically, when the high-temperature stability coefficient is greater than the stability threshold, generating candidate packaging solutions includes:

[0092] When the high-temperature stability coefficient corresponding to the adhesive performance configuration scheme is greater than the preset stability threshold, the dispensing parameters are obtained according to the fluid dynamics model. The dispensing parameters include dispensing pressure, dispensing speed and dispensing position coordinates. Based on the input encapsulation structure size, viscosity characteristic level and adhesive surface tension, the control point sequence constructed by the dispensing parameters is output.

[0093] A sealing experiment was conducted based on a control point sequence. Sealing pressure data and adhesive curing morphology scanning data were collected. The sealing pressure data and adhesive curing morphology scanning data were then input into a preset airtightness prediction model, and the helium leak detection rate was output.

[0094] When the helium leak detection rate of the dispensing parameters is greater than or equal to the leak detection threshold, the spacing between control points is reduced according to the spacing control template.

[0095] When the helium leak detection rate of the dispensing parameters is less than the leak detection threshold and there are defects in the glue line, the glue line is continuous and without defects, including but not limited to the glue line being complete and without breaks, the glue width is 0.5-0.6mm, the glue weight of each PCB is between 0.28-0.35g, the glue must not touch the chip and solder wire, and the control point spacing is increased according to the spacing control template.

[0096] The process continues until the helium leak detection rate of the dispensing parameters is less than the leak detection threshold and the glue line is continuous and defect-free. Then, the updated control point sequence is output as the dispensing process sequence, and candidate packaging solutions are generated by combining the glue performance configuration scheme.

[0097] Specifically, calculating the average drift of all samples for each candidate packaging scheme and generating a complete packaging scheme includes:

[0098] The packaged sensor is packaged according to the candidate packaging scheme and qualified, and the packaged sensor is placed in a thermo-pressure vessel for full characteristic testing.

[0099] The raw data of the packaged sensor is collected, and the temperature-nonlinear compensation parameter lookup table is generated by the fitting algorithm to calibrate the signal accuracy.

[0100] Power consumption analysis is performed on the system that outputs calibrated signals. Power consumption is optimized by dynamically adjusting the sampling rate and intelligent sleep-wake strategy, and low-power configuration parameters are generated.

[0101] The packaged sensor was placed in a high-temperature environment, and the drift trend lines of the zero-point signal and sensitivity of the packaged sensor were calculated throughout the entire test cycle.

[0102] Calculate the average drift of all samples for each candidate packaging scheme, select the candidate packaging scheme with the smallest average drift, and generate a complete packaging scheme by combining the corresponding low-power configuration parameters.

[0103] like Figure 2 As shown, this embodiment provides a high-precision, low-power chip stacked sensor packaging system, the system comprising:

[0104] The adhesive data acquisition and preliminary evaluation module is used to acquire adhesive performance data in real time during the chip stacking and bonding process, obtain the initial signal transmission accuracy index, and determine the preliminary output loss assessment value.

[0105] The adhesive performance configuration module is used to process adhesive formulations and curing time series to determine adhesive performance configuration.

[0106] The high-temperature stability testing module is used to calculate the high-temperature stability coefficient by simulating the signal processing process under high-temperature conditions.

[0107] The candidate module is used to generate candidate packaging solutions when the high-temperature stability coefficient is greater than the stability threshold.

[0108] The packaging module is used to calculate the average drift of all samples for each candidate packaging scheme and generate a complete packaging scheme.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-precision low-power chip stack sensor packaging method, characterized in that, The method comprises: Real-time acquisition of glue performance data in the chip stacking and bonding process, the glue performance data including glue data and curing data, the glue data including glue material type and glue component ratio data, and the curing data including curing temperature and curing time, obtaining an initial signal transmission accuracy index, and determining a preliminary output loss evaluation value; Inputting the glue performance data into a pre-trained viscosity classification model, classifying the viscosity characteristics of the glue by the pre-trained viscosity classification model, and outputting a glue viscosity characteristic grade; Generating bonding process control parameters according to a preset parameter template, the parameter template including a parameter generation rule for generating the bonding process control parameters according to the glue viscosity characteristic grade; When the bonding process control parameters are in a preset parameter range, extracting curing characteristics of the glue performance data, the curing characteristics including curing temperature characteristics and curing time characteristics; Inputting the curing characteristics into a transmission prediction model, and outputting a corresponding initial signal transmission accuracy index by the transmission prediction model; When the initial signal transmission accuracy index is less than a preset transmission accuracy threshold, adjusting the curing time by changing the glue component ratio data, re-acquiring the curing data, obtaining updated glue performance data and a signal transmission accuracy index; According to the updated signal transmission accuracy index, calculating the preliminary output loss evaluation value, and the specific formula is: ； wherein, represents a preliminary output loss evaluation value, M represents the total number of collected glue performance data, represents the glue viscosity characteristic grade of the i-th glue performance data, represents the curing time of the i-th glue performance data, represents the initial signal transmission accuracy index corresponding to the i-th glue performance data, represents a loss evaluation coefficient; Processing glue formula and curing time sequence, and determining glue performance configuration; Calculating the high-temperature stability coefficient by simulating the signal processing link in a high-temperature environment; Based on the glue performance configuration scheme, calculating the high-temperature stability coefficient by simulating the signal processing link in a high-temperature environment, and the specific formula is: ； wherein, represents a high-temperature stability coefficient, represents a temperature change amount in a high-temperature environment, represents a signal standard deviation, represents a material thermal sensitivity coefficient, represents a glue layer deformation parameter; When the high-temperature stability coefficient is greater than a stability threshold, generating a candidate packaging scheme; Calculating the average value of the drift amount of all samples of each candidate packaging scheme and generating a complete packaging scheme.

2. The high-precision low-power chip stack sensor packaging method according to claim 1, wherein The processing of the glue formula and the curing time sequence, and the determination of the glue performance configuration, comprise: When the preliminary output loss evaluation value is greater than an evaluation threshold, optimizing the glue formula; Extracting key formula characteristics from the glue performance data, the key formula characteristics including glue component ratio and curing time, and constructing a formula parameter set: When the glue viscosity characteristic grade corresponding to the formula in the formula parameter set is greater than a preset maximum viscosity threshold, performing cluster analysis on the formula according to the glue viscosity characteristic grade and the historical curing uniformity score; Selecting, from each cluster, a formula whose glue viscosity characteristic grade is within a preset range and whose historical curing uniformity score is the highest as a candidate optimized formula; Adjusting the temperature curve and the environmental humidity of the curing oven dynamically through a real-time monitoring system, recording the adjusted curing process parameters, the curing process parameters including temperature-time sequence and humidity value; Performing a curing experiment under the adjusted curing process parameters, acquiring glue performance data of the cured glue sample, and forming a curing condition optimization data set; Using a data analysis tool, comprehensively analyzing the glue performance data in the curing condition optimization data set, and evaluating the compatibility with the substrate or the element and the signal transmission quality; Based on the evaluation result, a glue performance configuration scheme is formed by selecting a combination of glue formula and curing process parameters that meet all preset key performance indicators.

3. The high-precision low-power chip stack sensor packaging method according to claim 2, wherein, When the high-temperature stability coefficient is greater than the stability threshold, a candidate packaging scheme is generated, including: When the high-temperature stability coefficient of the glue performance configuration scheme is greater than the preset stability threshold, the dispensing parameters are obtained according to the fluid mechanics model, including dispensing pressure, dispensing speed and dispensing position coordinates, and a control point sequence constructed by the dispensing parameters is output according to the packaging structure size, viscosity characteristic grade and glue surface tension; Based on the control point sequence, a sealing experiment is performed to collect sealing pressure data and glue solidification topography scanning data, and the sealing pressure data and glue solidification topography scanning data are input into a preset air tightness prediction model to output a helium leak detection rate; When the helium leak detection rate of the dispensing parameters is greater than or equal to the leak detection threshold, the control point spacing is reduced according to the spacing control template; When the helium leak detection rate of the dispensing parameters is less than the leak detection threshold and the glue line has defects, the control point spacing is increased according to the spacing control template; Until the helium leak detection rate of the dispensing parameters is less than the leak detection threshold and the glue line is continuous and defect-free, the updated control point sequence is output as a dispensing process sequence, and a candidate packaging scheme is generated in combination with the glue performance configuration scheme.

4. The high-precision low-power chip stack sensor packaging method according to claim 3, characterized in that, The average value of the drift of all samples of each candidate packaging scheme is calculated to generate a complete packaging scheme, including: According to the candidate packaging scheme, a packaged sensor is packaged, and the packaged sensor is placed in a temperature and pressure tank for full characteristic testing; Raw data of the packaged sensor is collected, and a temperature-nonlinear compensation parameter lookup table is generated by a fitting algorithm to calibrate signal accuracy; The system outputting the calibrated signal is analyzed for power consumption, and the power consumption is optimized by dynamically adjusting the sampling rate and an intelligent sleep-wake strategy to generate low-power configuration parameters; The packaged sensor is placed in a high-temperature environment, and the drift trend line of the zero signal and sensitivity of the packaged sensor over the entire test period is calculated; The average value of the drift of all samples of each candidate packaging scheme is calculated, the candidate packaging scheme with the smallest average drift is selected, and a complete packaging scheme is generated in combination with the corresponding low-power configuration parameters.

5. A high-precision low-power chip stack sensor packaging system using a high-precision low-power chip stack sensor packaging method according to any one of claims 1 to 4, characterized by, The system includes: a glue data acquisition and preliminary evaluation module for real-time acquisition of glue performance data during chip stacking and die bonding, obtaining an initial signal transmission accuracy index, and determining a preliminary output loss evaluation value; a glue performance configuration module for processing glue formula and curing time sequence to determine glue performance configuration; a high-temperature stability test module for calculating a high-temperature stability coefficient by simulating signal processing links in a high-temperature environment; a candidate module for generating a candidate packaging scheme when the high-temperature stability coefficient is greater than the stability threshold; and a packaging module for calculating the average value of the drift of all samples of each candidate packaging scheme to generate a complete packaging scheme.

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