High-precision fingerprint identification chip design method and device

Abstract

The invention relates to a high-precision fingerprint identification chip design method and device, and the method comprises the steps: obtaining process parameters and design constraint data of a plurality of fingerprint identification chips, and carrying out the classification matching, and obtaining chip architecture information; acquiring real-time performance data of each fingerprint identification chip according to the chip architecture information, and performing feature extraction to obtain fingerprint chip data; performing identification precision prediction on each fingerprint identification chip based on the real-time performance data and the fingerprint chip data to obtain a single precision prediction result; and performing chip performance optimization according to the fingerprint chip data and the single precision prediction result to obtain a chip design scheme. According to the invention, the actual identification performance of the chip can be evaluated more comprehensively.

Description

Technical Field

[0001] This invention relates to the field of chip technology, and in particular to a design method and apparatus for a high-precision fingerprint recognition chip. Background Technology

[0002] Current fingerprint recognition chip design methods primarily rely on static process models and limited test data, failing to effectively integrate process fluctuations and real-time performance during manufacturing. This design approach easily leads to a mismatch between chip architecture and actual process conditions, affecting production yield and performance consistency. Furthermore, due to the lack of real-time acquisition and analysis of fingerprint chip data during chip operation, design optimization often depends on post-test feedback, resulting in low optimization efficiency and difficulty in quickly responding to changes in market demand. In addition, existing methods lack dynamic adjustment capabilities in accuracy prediction and performance optimization, extending chip design cycles and hindering product iteration speed. Summary of the Invention

[0003] The main objective of this invention is to provide a design method and apparatus for a high-precision air fingerprint recognition chip, which can more comprehensively evaluate the actual recognition performance of the chip.

[0004] To achieve the above objectives, the present invention provides a high-precision fingerprint recognition chip design method, comprising: The process parameters and design constraints of multiple fingerprint recognition chips are obtained, classified and matched to obtain chip architecture information; Real-time performance data of each fingerprint recognition chip is collected based on the chip architecture information, and feature extraction is performed to obtain fingerprint chip data; Based on the real-time performance data and the fingerprint chip data, the recognition accuracy of each fingerprint recognition chip is predicted to obtain a single accuracy prediction result. Based on the fingerprint chip data and the single-precision prediction results, chip performance is optimized to obtain a chip design scheme.

[0005] Furthermore, the process of acquiring process parameters and design constraint data of multiple fingerprint recognition chips, performing classification and matching to obtain chip architecture information includes: The process nodes of the multiple fingerprint recognition chips are scanned and standardized to obtain a set of process parameters; The layout design rules and circuit constraints of each fingerprint recognition chip are obtained, and a hierarchical structure analysis is performed to obtain a design constraint dataset. Based on the set of process parameters and the set of design constraints, multiple fingerprint recognition chips are clustered to obtain chip classification results. For each group in the chip classification results, a representative chip template is constructed, and its features are compared with the set of process parameters to obtain a matching evaluation result. Based on the matching evaluation results, the architecture type of each fingerprint recognition chip is determined and the structural parameters are integrated to obtain chip architecture information.

[0006] Further, the process of constructing representative chip templates for each group in the chip classification results and comparing them with the process parameter set to obtain matching evaluation results includes: The chip classification results are grouped and the process is extracted to generate grouped process feature groups; The ridge principal component dimension reduction projection is performed on the grouped process feature groups to obtain feature projection information; Based on the feature projection information, the chip classification results are grouped and typical features are filtered to obtain a candidate template chip set; Multi-objective constraint template construction is performed on the candidate template chip set to obtain representative chip templates; The representative chip template is matched with the set of process parameters using fingerprint features to obtain the matching evaluation result.

[0007] Further, the step of collecting real-time performance data of each fingerprint recognition chip based on the chip architecture information and performing feature extraction to obtain fingerprint chip data includes: The chip architecture information is used to identify architecture units and calibrate sampling partitions to obtain an initial sampling interval; The fingerprint recognition chip is segmented and its signal is sampled according to the initial sampling interval to obtain the real-time performance data. Texture structure offset analysis is performed on the real-time performance data to obtain offset data; Based on the offset data, boundary features are extracted from the real-time performance data to obtain boundary texture mapping data; The boundary texture mapping data is subjected to local region refinement and directional encoding to obtain directional encoded map data; The initial sampling interval is reverse-constructed based on the directional coding map data to obtain reverse structure recombination information, which is then fused with the chip architecture information to obtain the fingerprint chip data.

[0008] Further, the step of reversibly constructing the initial sampling interval based on the directional coding map data to obtain reverse structure recombination information, and fusing this information with the chip architecture information to obtain the fingerprint chip data, includes: The directional coding map data is classified by map feature to obtain partitioned directional feature data; Based on the initial sampling interval and the partition direction feature data, the sampling interval is reconstructed to obtain the reconstructed interval data; Based on the reconstructed interval data and the directional coding map data, the reverse traversal point sequence and fingerprint structure are synthesized in reverse to obtain the initial reverse structure data; The initial reverse structure data is optimized and the mesh is adjusted to obtain reverse structure recombination information; The reverse structure reconstruction information and the chip architecture information are fused in a multimodal manner to obtain the fingerprint chip data.

[0009] Further, the step of predicting the recognition accuracy of each fingerprint recognition chip based on the real-time performance data and the fingerprint chip data to obtain a single accuracy prediction result includes: The real-time performance data is processed by chip-level parameter parsing to obtain chip hardware characteristic parameters; Based on the fingerprint chip data and the chip hardware feature parameters, performance identification and correlation are performed to obtain chip processing performance data; The chip processing performance data is estimated by stepwise precision transfer to obtain hierarchical precision evaluation data. Based on the hierarchical accuracy evaluation data, the multi-scenario recognition accuracy prediction is performed on each fingerprint recognition chip to obtain the single accuracy prediction result.

[0010] Furthermore, the step of optimizing chip performance based on the fingerprint chip data and the single-precision prediction result to obtain a chip design scheme includes: The fingerprint chip data is subjected to quality assessment, and the performance is assessed by combining the single-precision prediction results to obtain performance evaluation indicators. Based on the performance evaluation metrics, the hardware architecture parameters of the chip architecture information are adjusted to obtain hardware architecture optimization parameters. Based on the hardware architecture optimization parameters, architecture resources are allocated to obtain architecture configuration information; Based on the design constraint data and the process parameters, the architecture configuration information is verified to obtain a feasible design solution. Based on the chip architecture information, the overall architecture of the design feasibility scheme is integrated and optimized to obtain the chip design scheme.

[0011] Furthermore, the step of verifying the architecture configuration information based on the design constraint data and the process parameters to obtain a feasible design solution includes: Fingerprint constraint mapping is performed on the architecture configuration information and the design constraint data to obtain a fingerprint constraint mapping table; Based on the process parameters and the fingerprint constraint mapping table, the sensing process is adapted to obtain the fingerprint process constraint library. The architecture configuration information and the fingerprint process constraint library are subjected to feature conflict analysis to obtain fingerprint conflict data; Based on the fingerprint conflict data, a dynamic simulation of the fingerprint scenario is performed to obtain fingerprint scenario verification data; A comprehensive evaluation of fingerprint accuracy is performed on the fingerprint scene verification data and the fingerprint constraint mapping table to obtain the design feasibility scheme.

[0012] The present invention also provides a high-precision fingerprint recognition chip design apparatus, applied to the high-precision fingerprint recognition chip design method described in any one of the above claims, comprising: The acquisition module is used to acquire process parameters and design constraint data of multiple fingerprint recognition chips, perform classification and matching, and obtain chip architecture information. The analysis module is used to collect real-time performance data of each fingerprint recognition chip according to the chip architecture information, and to extract features to obtain fingerprint chip data. The association module is used to predict the recognition accuracy of each fingerprint recognition chip based on the real-time performance data and the fingerprint chip data, and obtain a single accuracy prediction result. The processing module is used to optimize chip performance based on the fingerprint chip data and the single-precision prediction result to obtain a chip design scheme.

[0013] The present invention provides a design method and apparatus for a high-precision air fingerprint recognition chip, which has the following beneficial effects: By acquiring and classifying the process parameters of multiple fingerprint recognition chips, the chip architecture can be dynamically adjusted for different process conditions. This effectively solves the problem of mismatch between process parameters and design architecture in traditional methods, significantly improving chip production yield and performance consistency. Feature extraction and analysis based on real-time performance data and fingerprint chip data enables precise monitoring of chip operating status, overcoming the limitations of traditional methods that rely on static test data, making design optimization more targeted and timely. Accuracy prediction combining real-time performance data and fingerprint chip data allows for a more comprehensive evaluation of the chip's actual recognition performance, addressing the issue of significant discrepancies between prediction results and actual applications in traditional methods, and providing a reliable basis for subsequent optimization. Performance optimization based on fingerprint chip data and accuracy prediction results enables dynamic adjustment of design parameters, avoiding the lag of traditional post-event feedback optimization models, significantly accelerating chip iteration speed, and responding more quickly to changes in market demand. Attached Figure Description

[0014] Figure 1This is a flowchart of a high-precision air fingerprint recognition chip design method provided by the present invention; Figure 2 This is a structural diagram of an air-based high-precision fingerprint recognition chip design device provided for the present invention.

[0015] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0018] Reference Figure 1 As shown, a high-precision fingerprint recognition chip design method includes: Step S101: Obtain the process parameters and design constraint data of multiple fingerprint recognition chips, classify and match them to obtain chip architecture information; Step S201: Collect real-time performance data of each fingerprint recognition chip based on the chip architecture information, and extract features to obtain fingerprint chip data; Step S301: Based on real-time performance data and fingerprint chip data, predict the recognition accuracy of each fingerprint recognition chip to obtain a single accuracy prediction result; Step S401: Optimize chip performance based on fingerprint chip data and single-precision prediction results to obtain chip design scheme.

[0019] Based on the steps described above, the detailed process is as follows: Step S101: The process involves acquiring the target chipset's manufacturing process documentation and hardware design specifications, analyzing the process parameters provided by the wafer foundry, including sensor photoelectric conversion efficiency, metal layer RC delay parameters, and thermal noise distribution models, and extracting layout routing rules, timing margin thresholds, and power density limits from the design constraint dataset. Multi-source data is normalized to form a structured parameter set. An adaptive clustering algorithm based on process nodes is then used to group chip technology families according to the similarity of CMOS process characteristics (such as gate oxide thickness fluctuation range and doping concentration gradient).

[0020] For each group, a 3D architecture feature deconstruction is performed to extract spatial topology parameters such as sensor array arrangement, analog-to-digital converter location, and power domain partitioning scheme. Matching weights are calculated using a constraint matrix between process features and design rules, and the chip closest to the process median within the group is selected as the architecture prototype. The final output includes chip architecture information containing elements such as sensor type, signal chain topology, and power domain partitioning. This step achieves a quantitative mapping between process characteristics and hardware architecture, establishing a benchmark framework for dynamic data acquisition.

[0021] Step S201: Based on the sensor array layout and sampling circuit characteristics in the chip architecture information, the initial spatiotemporal sampling window for fingerprint signal acquisition is determined. A segmented signal sampling strategy is deployed, and the acquisition channels are activated according to the sensor physical coordinates. The capacitance response curve, signal-to-noise ratio fluctuation data, and charge leakage parameters of each unit during the fingerprint pressing process are recorded simultaneously to form a multi-dimensional real-time performance dataset.

[0022] The phase difference between signals from adjacent sensing units is calculated using a texture structure offset analysis algorithm to identify the ridge edge distortion vector caused by differences in finger dryness and wetness. A boundary gradient change heatmap is generated based on the offset data, and a high-precision spatial mapping relationship between fingerprint ridge and valley boundaries is calculated by combining sensor position coordinate transformation. Adaptive orientation encoding is performed on the boundary texture mapping data, and an 8-neighborhood orientation filter is used to calculate the ridge angle distribution probability point-by-point in the local region, outputting orientation-encoded map data recording the principal direction and confidence level of each pixel. This step constructs a complete conversion chain from the original electrical signal to the fingerprint topology.

[0023] Step S301: Analyze chip physical parameters (such as ADC quantization step size and amplifier bandwidth) from real-time performance data to construct a feature matrix reflecting the hardware's extreme performance. Perform cross-domain correlation between the direction coding map data and the feature matrix, and calculate the chip's ability to reconstruct the fingerprint topology using a direction consistency check algorithm. Based on a hierarchical precision transfer model, execute [the following steps] at the signal acquisition layer, feature processing layer, and matching decision layer respectively. Accuracy attenuation simulation: Signal distortion under different pressure intensities is simulated at the acquisition layer; data drift due to clock jitter in the directional coding spectrum is verified at the feature layer; and the false recognition rate curve under noise interference is tested at the decision layer. The three-layer simulation results are fused to generate hierarchical accuracy evaluation data including stable, critical, and failure domains. Finally, a multivariate environmental scenario set of temperature, humidity, and pressure is established to predict the recognition accuracy limit of each chip under extreme conditions (such as low temperature, dryness, and strong pressure), outputting the single accuracy prediction result of the quantized accuracy attenuation curve. This step establishes an accuracy mapping model between hardware performance and environmental factors.

[0024] Step S401: Based on the ridge continuity evaluation results of the directional coding map data, bottleneck nodes in the signal link feature extraction are located in the architecture information. Combining the critical failure scenarios from the single-precision prediction results, hardware architecture parameters are reconstructed (e.g., adjusting the ADC sampling rate from 16MSPS to 24MSPS to improve dynamic range). A voltage domain weighting algorithm is used to tilt the power budget towards precision-sensitive modules (e.g., increasing the power supply margin of the sensor bias voltage by 15%). Based on the reconstructed hardware architecture optimization parameters, layout area resources are reallocated, generating an optimized clock tree balancing scheme and power grid density distribution map as architecture configuration information.

[0025] Injecting design constraint data and process parameters to perform 3D conflict verification: Feature channel conflict localization technology is used to identify timing violation paths between the sensing unit and the ADC module, and fingerprint load fluctuation simulation analysis is used to analyze the dynamic stability of the optimization scheme. Finally, spatial topology constraints from the architecture information are used to reverse-correct the feasibility of the scheme, generating a chip design scheme with integrated process compatibility certification, completing the closed-loop optimization from performance prediction to hardware implementation.

[0026] This invention provides a high-precision air fingerprint recognition chip design method. By acquiring and classifying the process parameters of multiple fingerprint recognition chips, the chip architecture can be dynamically adjusted for different process conditions. This effectively solves the problem of mismatch between process parameters and design architecture in traditional methods, significantly improving chip production yield and performance consistency. Based on feature extraction and analysis of real-time performance data and fingerprint chip data, precise monitoring of chip operating status is achieved, overcoming the limitations of traditional methods that rely on static test data, making design optimization more targeted and timely. By combining real-time performance data and fingerprint chip data for accuracy prediction, the actual recognition performance of the chip can be more comprehensively evaluated, solving the problem of large deviations between prediction results and actual applications in traditional methods, and providing a reliable basis for subsequent optimization. Performance optimization based on fingerprint chip data and accuracy prediction results enables dynamic adjustment of design parameters, avoiding the lag of traditional post-feedback optimization modes, significantly accelerating chip iteration speed, and responding more quickly to changes in market demand.

[0027] In one embodiment, process parameters and design constraint data of multiple fingerprint recognition chips are obtained, classified, and matched to obtain chip architecture information, including: Process node scanning is a comprehensive extraction process of the manufacturing process characteristics of a target chip. In practice, standard testing equipment, such as electron microscopes and energy dispersive spectroscopy (EDS), is used, combined with process documentation, to quantitatively record the process parameters of each chip. Key parameters include physical dimensional indicators such as transistor channel length, metal layer spacing, dielectric layer thickness, and doping concentration, as well as manufacturing process indicators such as photolithography precision and etching uniformity.

[0028] After scanning, all parameters are standardized and coded according to the classification system defined by the International Technology Roadmap for Semiconductors (ITRS). For example, parameters for different process nodes such as 28nm and 40nm are uniformly converted into nanometer-level values, and oxide layer thickness is converted into equivalent silicon dioxide thickness (EOT). During standardization, a process parameter mapping table needs to be established to ensure the comparability of data from different sources. The final generated set of process parameters is stored in matrix form, with each row representing a chip sample and each column corresponding to a specific process parameter, forming a structured database.

[0029] The acquisition of design constraint data requires processing two types of information simultaneously: physical design rules and circuit performance metrics. Physical design rules are derived from the chip's layout design documents, using geometric constraints such as layer definitions, minimum linewidths, spacing rules, and via sizes. Circuit constraints are extracted from the chip datasheet and simulation reports, including electrical parameters such as operating voltage range, clock frequency, power budget, and signal-to-noise ratio threshold.

[0030] The hierarchical structure analysis employs a top-down decomposition method: first, the chip is divided into functional partitions such as sensor arrays, analog front-ends, and digital processing modules; then, the interconnection relationships and timing requirements of the internal sub-modules within each partition are analyzed; finally, the analysis is refined to the layout density and routing congestion at the standard cell level. The analysis results are recorded in the form of multi-dimensional vectors, with vector dimensions covering quantitative indicators such as area utilization, metal layer ratio, and critical path delay, while also preserving the topological relationship graph between modules. The design constraint dataset is stored in a graph database, where nodes represent design units and edges represent constraint relationships, facilitating rapid retrieval of associated features during subsequent cluster analysis.

[0031] Clustering operations jointly extract features from the process parameter matrix and the design constraint graph database to form a unified feature space. Process parameters are standardized to eliminate dimensional differences, and design constraint indices are converted into fixed-dimensional numerical vectors using a graph embedding algorithm. An improved spectral clustering algorithm is employed to handle mixed data types: first, the Euclidean distance between process parameters and the graph similarity between design constraints are calculated; then, a kernel function is used to fuse the two into a comprehensive distance matrix.

[0032] The clustering process automatically determines the optimal number of categories and evaluates grouping quality based on silhouette coefficient and intra-class variance. The classification results not only output the category label of the chip but also generate a centroid feature vector for each category, reflecting the common characteristics of the chips in that category in terms of process technology and design. For example, one category may contain chips using a 28nm process and featuring high-density sensor arrays, while another category corresponds to low-power optimized designs using a 40nm process. The classification results are visualized in a dendrogram format, with branch depth reflecting the degree of feature differences.

[0033] Representative templates are constructed using feature compression technology, selecting the most typical chips from each category as benchmark templates. The selection criteria comprehensively consider process completeness (covering over 90% of process parameters) and design integrity (including all critical constraints). Template features consist of three parts: a subset of process features (e.g., metal layer stacking methods), a subset of design features (e.g., analog-to-digital converter accuracy), and derived features (e.g., power consumption per unit area).

[0034] The matching evaluation employs a two-stage comparison: the first stage checks the internal consistency of the template with similar chips, calculating the deviation of process parameters and the compliance rate of design constraints; the second stage performs cross-category comparison, analyzing the transferable features between different templates. The matching evaluation results generate a matching report in the form of a radar chart, where the axes represent different feature dimensions, and the larger the area, the higher the overall matching degree. Special attention is paid to the coupling relationship between process and design features; for example, the low-noise characteristics achieved by a template in a 65nm process match the high-sensitivity architecture of a 28nm process template.

[0035] Architecture type determination is a decision-making process based on matching data. For a chip to be classified, the closest template category is located based on its process parameters, and then a similarity threshold based on design constraints is used to determine whether to classify it into that category or create a new category. Structural parameter integration uses a feature-weighted fusion method: process-related parameters (such as transistor size) are directly taken from measured values; design-related parameters (such as wiring levels) are adjusted with reference to template values; performance parameters (such as recognition speed) are re-estimated based on the process scaling ratio.

[0036] The final output chip architecture information includes three levels of content: basic architecture type (e.g., "multi-layer metal stacked"), key structural parameter table (including process compatibility range), and derived design rules (e.g., "analog power isolation spacing ≥ 2μm"). This information is stored in machine-readable JSON format, and a natural language description document is generated to clearly indicate the source template and adaptation conditions of each parameter, providing a traceable reference for new chip design.

[0037] This embodiment systematically collects and classifies the process parameters and design constraints of multiple fingerprint recognition chips. This method can automatically extract common features and differences between different chips, construct a standardized set of process parameters and a hierarchical dataset of design constraints, significantly improving the reference value and reuse efficiency of chip architecture design. Representative templates generated based on chip clustering results can quickly match the process conditions and design requirements of new chips, avoiding repeated design verification processes and shortening the development cycle. The matching degree evaluation mechanism ensures the accuracy of architecture type determination by quantitatively analyzing the coupling relationship between process and design features, reducing the risk of design failure due to process deviations.

[0038] In one embodiment, a representative chip template is constructed for each group in the chip classification results, and its features are compared with a set of process parameters to obtain a matching evaluation result, including: Grouped process extraction is a process of in-depth mining of the process parameters of categorized chipsets. Each chipset's set of process parameters contains data across multiple dimensions, such as process node, metal layer thickness, doping concentration, and lithography precision. The extraction process first performs statistical distribution analysis on the process parameters of all chips within the group, calculating the mean, variance, and extreme values ​​to identify common characteristics and outliers within the group. For example, a group of chips might use a 28nm process, with metal layer spacing concentrated within a specific range, while individual chips might exhibit parameter deviations due to specific optimizations.

[0039] Based on the statistical results, grouped process feature sets are constructed and stored in structured data tables. Each feature set contains three types of data: core process parameters (such as critical dimensions and material properties), derived process indicators (such as equivalent oxide layer thickness), and process constraints (such as maximum allowable temperature). The feature sets not only record numerical values ​​but also annotate the variable range of parameters and process compatibility descriptions, ensuring that subsequent analysis accurately reflects the technical differences between different chipsets. The grouped process feature sets output in this step serve as the foundation dataset for subsequent dimensionality reduction and template selection.

[0040] Process feature sets typically contain high-dimensional data, and direct processing leads to computational redundancy or noise interference. Ridge principal component projection is used to compress data dimensionality while preserving key process features. This method combines principal component analysis and regularization techniques to avoid overfitting issues with high-dimensional data.

[0041] The process feature groups are standardized to eliminate dimensional differences in various parameters. The covariance matrix is ​​then calculated to identify the main directions of variation. Ridge regression introduces a regularization term to adjust the eigenvalue weights, ensuring that the dimensionality-reduced projection matrix maintains the main data trend while suppressing the influence of secondary noise. For example, the metal layer parameters of a certain chip group may account for the main variance, while the influence of secondary parameters (such as specific doping concentrations) is appropriately weakened.

[0042] The generated feature projection information contains several principal component axes, each corresponding to a linear combination of process parameters. This matrix is ​​not only used for data visualization (such as two-dimensional / three-dimensional scatter plots to show chip distribution), but more importantly, it provides a low-dimensional representation for subsequent selection of typical features, improving computational efficiency.

[0043] The goal of typical feature selection is to choose the most representative chip from each chipset as candidates for template construction. Feature projection information already provides the main distribution trend of the data, and the selection process is based on the position in the projection space.

[0044] In the dimensionality-reduced feature space, the distance between each chip and the centroid of the group is calculated, and samples close to the core region are identified. The process parameters of these samples are closest to the average level within the group and are suitable as benchmarks. At the same time, the boundary points of the feature space are analyzed, and some extreme samples represent special process optimization schemes, the applicability of which needs to be evaluated separately.

[0045] Further filtering is performed using design constraint data. For example, a chip might have process parameters close to the group average, but its layout design rules are too lenient or too strict, and it needs to be excluded. Ultimately, 3-5 candidate template chips are retained for each chipset, covering mainstream process solutions and some optimized variants, ensuring the comprehensiveness of subsequent template construction. The candidate template chip set is output as a structured list, accompanied by a summary of the process characteristics and an overview of the design constraints for each chip, for use in the next stage of multi-objective optimization.

[0046] The candidate template chip set contains multiple representative chip samples, but directly using a single chip as a template limits its applicability. Multi-objective constrained template construction aims to integrate the advantages of various candidate chips to generate an optimal template that combines process compatibility and design feasibility. This process needs to balance constraints across different dimensions, including manufacturing process limitations, circuit performance requirements, and physical implementation rules.

[0047] Key parameters of candidate chips are extracted, including process features (such as transistor size and metal layer stacking) and design features (such as module layout and timing constraints). A multi-criteria decision-making method is then employed to evaluate the feasible range of each parameter. For example, the choice of metal layer spacing must simultaneously meet lithographic accuracy requirements and signal integrity needs, while the operating voltage setting must balance power consumption and noise margin. Through cross-comparison of parameters, the optimal range for each feature is determined, avoiding manufacturing or design risks caused by extreme values.

[0048] The generated representative chip templates are described using a hierarchical structure: the base layer records core process parameters (such as process nodes and critical dimensions), the middle layer defines module-level constraints (such as sensor array density and analog front-end bandwidth), and the top layer integrates system-level metrics (such as overall power consumption and recognition speed). The templates are stored in a standardized format, support direct parsing by EDA tools, and include parameter adjustment guidelines explaining optimization directions for different application scenarios.

[0049] Fingerprint feature matching is used to verify the compatibility of representative templates with actual processes, ensuring manufacturability. The matching process is not a simple parameter comparison, but rather an evaluation of the fit between the template and the target process from a functional implementation perspective.

[0050] Extract the template's process requirements, such as minimum linewidth and dielectric constant, and compare them with the actual process capabilities in the process parameter set. Use a feature similarity algorithm to calculate the overlap of key indicators, such as whether the required metal layer thickness of a template matches the actual deposition capability of a certain process. Matching results are categorized into three levels: complete match (parameters are fully covered), partial match (process fine-tuning required), and mismatch (redesign required).

[0051] The matching evaluation results are presented in a structured report, including a matching score (0-100%), a list of key discrepancies, and improvement suggestions. For example, if the transistor threshold voltage of the template deviates from the target process, the report will recommend adjusting the doping scheme or changing the device model. The evaluation results are not only used to verify the current template, but can also be fed back to the chip classification stage to optimize subsequent clustering strategies, forming a closed-loop optimization process.

[0052] This embodiment utilizes grouped process extraction and feature dimensionality reduction techniques to rapidly identify key process features from massive amounts of chip data, significantly improving template construction efficiency. A multi-objective constraint template construction method is employed, comprehensively considering process compatibility and design feasibility to ensure the generated chip templates possess optimal versatility and adaptability. A fingerprint feature matching mechanism accurately assesses the matching degree between the template and the target process, effectively reducing manufacturing risks. The entire process forms a closed-loop optimization, enabling continuous iterative improvement of chip design, ensuring both design quality and development efficiency. This method achieves intelligent matching of process parameters and design constraints, providing reliable technical support for the development of high-precision fingerprint recognition chips.

[0053] In one embodiment, real-time performance data of each fingerprint recognition chip is collected based on chip architecture information, and feature extraction is performed to obtain fingerprint chip data, including: Based on the physical structure parameters and functional area definitions recorded in the chip architecture information database, the effective pixel area boundary coordinates of the sensor array are identified (e.g., the range from X10.2mm-Y8.7mm to X15.1mm-Y13.5mm). The layout location of the core circuit units is clearly defined: the main sensor array contains 1024×768 pixel matrix units, the reference voltage generation module is located between metal layers M4 and M6 in the upper left corner of the chip, and the clock tree network adopts a radial symmetrical structure. A hierarchical partitioning principle is adopted, and the chip surface is divided into 0.08mm×0.08mm square grid units according to the sensor unit density (600dpi layout accuracy). Each grid is associated with the corresponding design hierarchy information (the bottom layer is the photodiode sensing layer, the middle layer has a noise shielding ring, and the top layer distributes the power supply bus).

[0054] Dynamically calibrated sampling points: The sensing area uses the geometric center of each pixel unit as a fixed reference point; the analog signal chain area tracks the voltage drift path of the charge amplifier output node; and the power network area marks the voltage fluctuation monitoring positions of key power supply nodes. The resulting initial sampling interval is a three-dimensional spatial sampling coordinate system, recording the spatial coordinates (including the depth of the metal layer in the vertical direction), unit function type (sensing / conversion / control), and sampling trigger threshold (sensing acquisition is initiated at a light intensity threshold of 150 lux) of 158 core sampling points. This initial sampling interval covers the key signal nodes of the chip's core functional area.

[0055] Four synchronous acquisition channels are established with the chip under test using automated testing equipment: Channel A is connected to the analog output pin of the sensor (acquisition range ±1.8V, resolution 0.5mV); Channel B is bound to the monitoring point at the end of the clock tree (sampling rate 2GSa / s); Channel C is connected to the power distribution network (current sampling accuracy 0.1mA); and Channel D captures the output of the temperature sensor (±0.5℃ accuracy). Segmented signal acquisition is performed. Static reference acquisition phase: Under conditions of no external excitation, the background dark current value (range of 0-100pA) of each sensing unit is measured for 500ms, and a background noise distribution map is generated.

[0056] Dynamic response acquisition phase: Apply simulated fingerprint pressure (pressure gradient 1.5-3.0 g / mm²), record the voltage transition curves of 256 core pixel units (0→1.8V response process) at 1μs intervals, and simultaneously capture the transient response of the signal chain.

[0057] Conversion accuracy verification stage: Start the ADC self-test mode and continuously cycle to collect the voltage values ​​of the transition points of 4096-level quantization level for 100 cycles (each conversion cycle is 400μs) and mark the nonlinear distortion points.

[0058] Spatial pressure mapping stage: Simulate the movement trajectory of fingerprint ridges (scanning speed 2mm / s), and acquire the full array pressure value matrix (1024×768 data points) at a frequency of 4kHz.

[0059] All acquired data were time-axis calibrated to generate a synchronous dataset: voltage parameters retained three significant digits, time-series data aligned to 0.1ns accuracy, and temperature data recorded 10 samples per minute. The output real-time performance data includes the sensor's raw pressure field (1024×768×2000 tensor), signal chain waveforms (continuous recording for a total duration of 120ms), and power supply noise spectral density (analysis results in the 100Hz-10MHz frequency band).

[0060] A three-dimensional joint analysis of texture structure is performed on real-time performance data, including spatial displacement analysis: Sensor pressure field data is processed, converting the dark current distribution acquired from the static benchmark into a reference grayscale field. The pixel response changes after dynamic pressure loading are compared, and the position offset vector of each unit in the sensor array is calculated. Electrical offset quantization: Real-time performance data of the signal chain is analyzed, tracing the voltage trajectory at the output of the charge amplifier during the dynamic response phase, and calibrating its deviation from the ideal reference curve. Quantization level data acquired during the analog-to-digital conversion process is analyzed, and the systematic error of each conversion step is recorded.

[0061] Timing drift detection: Processes real-time performance data of the clock network, monitors edge jitter characteristics in the signal transmission path, and separates the delay fluctuation differences between the core functional area and the edge area of ​​the chip.

[0062] This study integrates spatial pressure gradient distribution with electrical response patterns, linking the spatial correspondence between pixel physical displacement and changes in electrical parameters. It generates offset data, including a spatial displacement field (recording pixel position offset vectors), an electrical offset distribution table (quantizing signal chain error parameters), and timing drift records (annotating clock transmission fluctuation characteristics), forming a complete dataset describing the chip's mechanical stress deformation response. This data establishes a mapping bridge between fingerprint physical features and the chip's electrical response.

[0063] The displacement vector field in the spatial displacement database is processed to perform fingerprint ridge boundary detection. A pixel displacement gradient model is constructed: the rate of change of the directional angle between displacement vectors of adjacent pixel units (range 0°-180°) is calculated, and locations where vector abrupt changes exceed 45° are marked as potential boundary points. A multi-scale convolutional kernel is used to scan the sensor array: a 3×3 kernel detects microstructure edges (identifying ridge inflection points with linewidth ≤25μm), and a 7×7 kernel captures macroscopic trends (tracking 500μm-level texture boundaries). Boundary topological features are extracted: cluster analysis is performed on the electrical offset mapping table to identify abnormal pixel blocks with voltage offsets exceeding a threshold (>10%FSR), generating an electrical mismatch boundary heatmap. Spatiotemporal dimension data is fused: the temporal drift log is coupled with the pressure gradient, and the spatially overlapping points of time delay abrupt change regions (Δt>150ps) and stress concentration regions are marked. The final result is a three-dimensional boundary texture mapping data: the XY plane records the coordinates of the discontinuities in the displacement vector (accuracy ±1.2μm), the Z-axis is labeled with the boundary type code (Class 1 is the deformation boundary / Class 2 is the electrical mismatch boundary / Class 3 is the temporal drift boundary), and the output is boundary texture mapping data containing boundary feature vectors, which accurately characterizes the physical boundary and electrical anomaly boundary area of ​​the fingerprint structure.

[0064] Local region refinement is an optimization process for boundary mapping data, aiming to improve the accuracy and consistency of feature representation. The process employs an iterative optimization algorithm to adjust the position and attributes of local feature points while maintaining the global topology. First, a confidence evaluation model for boundary points is established, and weight coefficients are adjusted based on the consistency between adjacent feature points. Then, feature point position optimization is performed, minimizing the local energy function to converge the feature points towards the true boundary. The orientation encoding process introduces a multi-scale orientation field estimation method, calculating gradient statistical features within a 5×5 local window to determine the principal orientation and degree of anisotropy.

[0065] The orientation encoding map employs a hierarchical representation method. The bottom layer stores the original feature point cloud data, the middle layer records local orientation fields and frequency features, and the top layer constructs macroscopic texture classification information. The map data utilizes a special data structure to achieve efficient storage and fast retrieval, supporting real-time fingerprint comparison requirements. The encoding process simultaneously generates a quality assessment layer, marking low-quality areas and blurred boundaries, providing a reliability reference for subsequent matching algorithms. The output of this step not only includes the geometric features of the fingerprint but also integrates the physical properties of the texture, laying the foundation for constructing highly discriminative feature descriptors.

[0066] The reverse reconstruction process is a crucial step in mapping feature analysis results back to the physical sampling space. Based on the topology and feature distribution provided by the directional coding map, the physical contact model of the fingerprint on the sensor array is reconstructed. The processing employs an elastic deformation inversion algorithm to calculate the original contact pressure distribution based on the displacement of feature points. The reverse structure reconstruction information includes three-dimensional spatial information: two-dimensional planar coordinates describe the geometric distribution of the fingerprint texture, and the third dimension represents the reconstructed signal strength or pressure value. The reconstruction process incorporates sensor characteristic parameters from the chip architecture information to correct distortions caused by nonlinear responses. The information fusion stage deeply integrates the reconstructed map with the chip's physical parameters, such as adjusting the feature scale according to the actual size and spacing of each pixel and optimizing feature weights based on circuit noise characteristics.

[0067] The final output of the fingerprint chip data adopts a multi-channel representation, including a texture feature layer, a quality assessment layer, a liveness feature layer, and an auxiliary information layer, supporting flexible use in different application scenarios. The data format is compatible with industry standards, ensuring seamless integration with existing fingerprint recognition systems. This step achieves closed-loop processing from chip physical characteristics to fingerprint features, and the output structured data can be directly used for high-precision fingerprint recognition and liveness detection.

[0068] This embodiment accurately delineates key chip regions through architectural unit identification and sampling partition calibration, ensuring data acquisition covers high-value feature areas while avoiding redundant sampling and improving data acquisition efficiency. Segmented signal sampling combined with an event-triggered mechanism dynamically adjusts the sampling strategy, completely capturing real-time performance data throughout the fingerprint pressing process, providing a high-fidelity signal source for subsequent analysis. Texture structure offset analysis employs a multi-scale processing method to extract static texture features and dynamic elastic features, enhancing liveness detection capabilities and improving anti-interference performance. Boundary feature extraction is based on a multi-evidence fusion strategy to ensure accurate boundary positioning, and non-uniform sampling optimizes data storage, balancing feature integrity and computational efficiency. The orientation encoding map constructs a highly discriminative feature descriptor through hierarchical representation and multi-scale orientation field estimation, improving fingerprint matching accuracy. The reverse construction and information fusion process deeply integrates feature data with chip physical parameters, correcting sensor nonlinear distortion. The final output multi-channel fingerprint chip data supports high-precision recognition and flexible application scenario adaptation. The overall solution optimizes computational resource utilization while ensuring recognition accuracy, improving system robustness and adaptability.

[0069] In one embodiment, the initial sampling interval is reverse-constructed based on the direction coding map data to obtain reverse structure recombination information, which is then fused with chip architecture information to obtain fingerprint chip data, including: Orientation-encoded map data contains multi-scale directional information of fingerprint texture. The map feature classification process aims to divide it into local regions with consistent characteristics, providing structured input for subsequent sampling interval reconstruction. The classification process is based on the principle of orientation field consistency, using a region growing algorithm to aggregate adjacent and oriented similar coding units into homogeneous blocks. In the initial stage, the orientation-encoded map is divided into several basic units, each recording the principal orientation angle, orientation confidence, and local frequency features. The classification algorithm uses high-confidence units as seed points, expands along the ridge direction, and merges adjacent units that satisfy the orientation continuity condition to form coherent texture partitions.

[0070] The partitioning results not only consider differences in orientation angles but also incorporate local curvature and ridge density features to ensure accurate division of ridge branch points and singularity regions. The output of the partitioned orientation feature data adopts a hierarchical structure: the top layer describes macroscopic texture types (e.g., whorl, loop, bow), the middle layer records the regional orientation field distribution, and the bottom layer stores pixel-level orientation vectors. This data provides spatial constraints for sampling interval reconstruction, ensuring that subsequent processing steps can employ differentiated strategies for different texture characteristics.

[0071] Sampling interval reconstruction is a crucial step connecting feature analysis and physical sampling, aiming to dynamically adjust the sampling strategy based on texture partition characteristics. The initial sampling interval is defined based on the physical layout of the chip architecture, while the reconstruction process incorporates semantic information provided by directional feature data. Guided by the partition directional feature data, the processing increases sampling density in regions with drastic texture direction changes (such as core points and triangular areas) and decreases sampling frequency in regions with uniform direction.

[0072] The reconstruction algorithm establishes a mapping relationship between sampling points and directional partitions, calculates the anisotropy degree of each partition, and adjusts the distribution and orientation of the sampling grid accordingly. In ridge intersection or termination regions, sampling points are arranged along the main direction to enhance feature capture capability; in regions with low texture variation, sampling points are distributed with a sparse grid to reduce redundant data. The reconstructed interval data not only includes the updated spatial coordinates but also adds the priority weight and orientation attribute of each sampling point, forming semantically labeled reconstructed interval data.

[0073] The generation of the reverse traversal point sequence is a crucial step in the reverse reconstruction of fingerprint texture. Its core lies in recovering the topological structure of the original fingerprint from the sampling points and orientation-encoded data. The traversal process uses the reconstructed sampling interval as a spatial framework, tracing point by point along the ridges provided by the orientation-encoded map to form a continuous ridge trajectory. Starting from high-confidence sampling points, the algorithm connects adjacent points along the optimal path based on the guidance of the local orientation field, constructing continuous ridges or valleys.

[0074] At branch points or intersections, the traversal strategy combines multi-directional evaluation and contextual consistency checks to ensure the logical correctness of ridge connections. The reverse fingerprint structure synthesis stage integrates all traversal trajectories to generate initial reverse structure data. This data is represented using vector graphics, with each ridge recording point sequence coordinates, width attributes, and orientation confidence, while also marking singularities and feature regions. The synthesis process preserves the multi-scale characteristics of orientation encoding, maintaining the ridge structure at the macroscopic level and reconstructing pore and fine scratch features at the microscopic level. The initial reverse structure data constitutes the prototype of reverse structure reconstruction information, providing a basic geometric framework for subsequent structure optimization.

[0075] The initial reverse structure data contains a fingerprint ridge network reconstructed from orientation encoding and sampling point sequences, but it still suffers from issues such as local breaks, noise interference, or mesh distortion. The structure optimization process aims to correct these defects, making the reconstructed fingerprint texture more continuous, smooth, and consistent with the true distribution of biometric features. The optimization algorithm first smooths the ridge trajectories using a curve fitting method based on an elastic deformation model to eliminate high-frequency jitter while keeping the feature point positions unchanged. For broken areas, the algorithm combines orientation field prediction and the orientation of adjacent ridges for intelligent completion, ensuring the naturalness and topological correctness of ridge extension. In the mesh adjustment stage, the reconstructed ridges are aligned pixel-level according to the physical arrangement characteristics of the chip sensor, eliminating minor offsets caused by sampling intervals or orientation interpolation. The adjusted ridge network is remapped to a standard mesh coordinate system, forming high-precision reverse structure reconstruction information. This image not only contains optimized ridge geometry information but also includes confidence scores and quality labels, providing reliable input for subsequent multimodal fusion.

[0076] Multimodal fusion is the final step connecting the reconstructed fingerprint with the chip's physical properties. Its goal is to deeply integrate reverse structure reconstruction information with the chip's sensor characteristics, circuit response, and environmental parameters to generate fingerprint chip data that can be directly used for identification. The fusion process first calibrates the signal strength of the reconstructed image based on the sensor distribution and sensitivity parameters in the chip architecture information, simulating the electrical response during real contact. For example, in the capacitive sensor region, the pressure distribution of the ridges is converted into corresponding capacitance changes; in the optical sensor region, the grayscale mapping is adjusted according to the optical characteristics of the pixels.

[0077] A circuit noise model is introduced to simulate the actual impact of the signal chain, including amplifier gain fluctuations, quantization noise, and channel crosstalk, making the reconstructed image closer to the real data acquired by the hardware. An environmental compensation module further integrates external factors such as temperature, humidity, and electrostatic interference to improve the environmental robustness of the image. The final fingerprint chip data is represented using a multi-channel matrix, including the enhanced grayscale image, signal-to-noise ratio distribution map, liveness feature layer, and hardware calibration parameters, forming a complete fingerprint characterization system suitable for high-precision matching.

[0078] This embodiment accurately reconstructs fingerprint texture features through reverse engineering and structural optimization of directional coding map data, effectively eliminating noise and distortion generated during sampling, and significantly improving the accuracy and reliability of fingerprint reconstruction. Employing multimodal fusion technology, the reconstructed fingerprint is deeply integrated with the chip's physical characteristics, ensuring that the generated fingerprint chip data retains both the discriminative information of biometrics and fully considers sensor characteristics and circuit response, greatly improving the accuracy and environmental adaptability of fingerprint recognition. By dynamically adjusting the sampling strategy and optimizing the grid distribution, efficient capture of key fingerprint feature regions is achieved, reducing computational resource consumption while maintaining recognition accuracy.

[0079] In one embodiment, the recognition accuracy of each fingerprint recognition chip is predicted based on real-time performance data and fingerprint chip data to obtain a single accuracy prediction result, including: The goal of chip-level parameter analysis is to transform the raw monitoring data into a structured, quantifiable hardware feature matrix, providing a foundation for subsequent performance identification and correlation. The analysis process involves three core stages: data cleaning, feature extraction, and matrix construction. The data cleaning stage removes outliers and noise interference. The feature extraction stage designs specific transformation rules for different performance parameters; for example, voltage fluctuation data is converted into stability coefficients, and temperature change data is converted into thermal stability indices. These transformed feature values ​​are arranged and combined according to preset dimensions to form the chip hardware feature parameters. The rows of this matrix represent sampling data at different time points, the columns represent various hardware feature indices, and the matrix element values ​​reflect the chip's hardware state at a specific time point. The construction of the chip hardware feature parameters provides a hardware-level quantitative basis for subsequent image processing capability evaluation, and its quality directly affects the reliability of the final accuracy prediction.

[0080] The purpose of performance identification and correlation of fingerprint chip data is to establish a mapping relationship between hardware features and image processing effects, revealing the impact of chip hardware performance on image quality. The correlation process consists of two stages: hardware matching and effect evaluation. In the hardware matching stage, each row of chip hardware feature parameters (i.e., the hardware state at a certain moment) is aligned with the fingerprint chip data at the same timestamp to ensure spatiotemporal consistency of the data.

[0081] The performance evaluation phase analyzes the impact of different hardware feature combinations on image processing results, such as the improvement in image signal-to-noise ratio under high-frequency clock conditions or the stability of feature point extraction quantity under low-temperature environments. Through statistical correlation analysis, chip processing performance data is generated, including a table showing the correspondence between hardware configuration and image processing performance, as well as the calibration results of key performance thresholds. This chip processing performance data serves as a bridge between hardware characteristics and recognition accuracy; its accuracy depends on the completeness of the hardware feature matrix and the representativeness of the image processing data. The output of this step provides cross-domain correlation basis for subsequent step-by-step accuracy propagation estimation.

[0082] By performing stepwise precision transfer estimation on the chip processing performance data, layered precision evaluation data is obtained. Chip processing performance data reflects the correlation between hardware performance and image processing results, but it does not directly reflect its impact on the final recognition accuracy. The goal of stepwise accuracy transfer estimation is to decompose image processing capabilities into accuracy-influencing factors at different levels and quantify the contribution of each level to the overall recognition accuracy.

[0083] The estimation process unfolds based on the hierarchical structure of the fingerprint recognition workflow, including an image acquisition layer, a preprocessing layer, a feature extraction layer, and a matching layer. In the image acquisition layer, the impact of chip hardware on the quality of the raw fingerprint image is analyzed, such as sensor noise and dynamic range, and its contribution to the basic accuracy of subsequent processing is calculated. In the preprocessing layer, the execution effect of denoising and enhancement algorithms is evaluated, and the accuracy loss or improvement at this stage is estimated by combining the chip's computing power (such as the number of parallel processing units and memory bandwidth). In the feature extraction layer, the stability of feature point detection is analyzed, such as the fluctuation of the number of feature points under different hardware loads, and its impact on the matching stage is quantified. In the matching layer, the matching success rate of the chip under different computing resource allocations is evaluated by integrating the data from the preceding layers.

[0084] The accuracy evaluation results for each layer are recorded in numerical or probability distribution form, forming hierarchical accuracy evaluation data. This data not only includes independent accuracy metrics for each layer but also describes the dependencies between layers, such as how errors from the preprocessing layer propagate to the feature extraction layer. The hierarchical accuracy evaluation data provides a modular analytical framework for multi-scene recognition accuracy prediction, enabling the final prediction to more accurately reflect the chip's performance under different operating conditions.

[0085] Layered accuracy assessment data has quantified the chip's accuracy characteristics at each processing stage. However, real-world fingerprint recognition scenarios are diverse, including wet and dry fingers, varying pressure levels, and changes in ambient lighting. The goal of multi-scenario recognition accuracy prediction is to combine layered data to simulate the chip's performance under various real-world conditions, ultimately outputting a comprehensive single-accuracy prediction result.

[0086] The prediction process includes defining typical application scenarios, such as high humidity environments, low temperature environments, and rapid continuous recognition. For each scenario, relevant parameters in the layered accuracy evaluation data are adjusted. For example, in high humidity environments, the signal-to-noise ratio of the image acquisition layer decreases, the denoising requirements of the preprocessing layer increase, and the false detection rate of the feature extraction layer rises. By simulating these changes, the chip's recognition accuracy in that scenario is calculated.

[0087] The prediction results from different scenarios are integrated using weighted fusion or probabilistic models to generate a single-precision prediction value. This value can be a comprehensive score or a recognition success rate at different confidence levels. For example, the chip can achieve a recognition rate of 99.5% under standard testing conditions, while it can drop to 95% under extreme conditions. The final prediction result can be presented as the average recognition rate or the guaranteed accuracy under the worst-case scenario.

[0088] Single-precision prediction results not only reflect the overall performance of a chip but also guide optimization efforts. For example, if a prediction shows a significant decrease in accuracy at low temperatures, the chip's temperature adaptability algorithm or hardware design can be optimized accordingly. The output of this step provides quantifiable data for the final quality assessment of the chip, directly impacting its market positioning and application scope.

[0089] This embodiment, based on the correlation analysis of real-time performance data and fingerprint chip data, accurately predicts the accuracy of fingerprint recognition chips, providing a reliable basis for chip performance optimization. By constructing chip hardware characteristic parameters, complex hardware parameters are transformed into standardized data that can be quantified and evaluated, making performance comparisons between different chips more intuitive and effective. Employing a step-by-step accuracy transfer estimation method, the system analyzes the accuracy-influencing factors of each processing stage in fingerprint recognition, achieving precise localization of accuracy issues. Multi-scenario recognition accuracy prediction technology can comprehensively evaluate the chip's performance in various real-world usage environments, significantly enhancing the practical value of the prediction results.

[0090] In one embodiment, chip performance is optimized based on fingerprint chip data and single-precision prediction results to obtain a chip design scheme, including: Fingerprint chip data quality assessment is a fundamental step in chip performance optimization. The quality of fingerprint images directly affects the accuracy and efficiency of subsequent processing. Quality assessment covers core dimensions such as image sharpness, noise level, contrast, and effective area ratio. Sharpness reflects the distinguishability of fingerprint ridges and valleys; noise level measures the intensity of interference signals in the image; contrast determines the ability to distinguish ridges from the background; and the effective area ratio assesses the proportion of fingerprint area that can be used for recognition.

[0091] The quality assessment employs a standardized scoring system, quantifying each indicator into numerical form for easier subsequent comprehensive analysis. Single-precision prediction results provide the expected accuracy of fingerprint matching at the algorithm level, reflecting the theoretical performance ceiling of the current processing flow. Performance assessment correlates the quality score with the accuracy prediction results, establishing multi-dimensional performance evaluation metrics. These metrics include not only direct parameters such as recognition accuracy but also chip-level indicators such as processing latency, power efficiency, and resource utilization. The synergy between quality and performance assessments is reflected in the following ways: when matching high-quality images with high-precision predictions, performance evaluation metrics tend to optimize recognition speed; when matching low-quality images with low-precision predictions, performance evaluation metrics emphasize fault tolerance mechanisms and noise reduction capabilities.

[0092] Hardware architecture parameter adjustments are based on performance evaluation metrics, specifically refactoring the chip's computing units, storage units, and interconnect architecture. Computing unit adjustments involve configuring the number of parallel processing cores, deploying dedicated acceleration modules, and deeply optimizing the computational pipeline. Storage unit adjustments focus on optimizing cache hierarchy design, bandwidth allocation schemes, and data prefetching mechanisms to ensure efficient data throughput for the fingerprint chip. Interconnect architecture adjustments include bus width refactoring, communication protocol optimization, and redundant design of data transmission paths.

[0093] The parameter tuning process follows the priority order of evaluation metrics: when recognition accuracy is the primary concern, hardware resources for the feature extraction module are increased; when processing latency is sensitive, the clock frequency of the parallel computing units is increased; and when power consumption is limited, dynamic voltage and frequency adjustment techniques are employed. Architectural parameter tuning is not an isolated operation; it must be synchronized with the physical constraints of the chip manufacturing process in real time. For example, when increasing the number of parallel cores, routing congestion risks are assessed simultaneously; when expanding cache capacity, timing convergence conditions are verified. The output of hardware architecture optimization parameters is an executable configuration script, containing specific instructions such as transistor-level parameters, logic unit layout, and timing constraints.

[0094] The architecture resource allocation translates optimization parameters into specific hardware resource mapping schemes. Computational resource allocation determines the physical distribution of the fingerprint preprocessing unit, feature extraction unit, and matching operation unit on the chip, employing a proximity-based layout strategy to reduce signal transmission latency. Storage resource allocation allocates the capacity ratio of on-chip cache, register file, and shared memory, designing dedicated cache partitions tailored to the fingerprint chip's data flow characteristics; for example, allocating dual-port memory for frequently accessed ridge pattern data. Interconnect resource allocation plans the topology of the data bus, control bus, and test bus, using a hierarchical interconnect scheme to ensure bandwidth for critical paths.

[0095] The resource allocation process incorporates a conflict detection mechanism. When multiple modules compete for the same resource, the allocation ratio is dynamically adjusted based on the weights of performance evaluation metrics. The architecture configuration information must output a complete resource mapping table, annotating key information such as the physical coordinates, storage address space, and clock domain partitioning of each functional module. The configuration scheme forms a closed-loop feedback loop with subsequent design verification phases; for example, when process parameters limit storage density, the resource allocation strategy is iterated again. The ultimate goal of resource allocation is to achieve balanced utilization of computing, storage, and communication resources, avoiding performance bottlenecks caused by localized overload or idleness.

[0096] The design verification phase matches the architecture configuration information with actual chip manufacturing constraints and process parameters to ensure the physical feasibility of the design. Design constraints include hard metrics such as chip area, power budget, operating temperature range, and signal integrity requirements, while process parameters cover manufacturing-related characteristics such as transistor feature sizes, metal layer stacking schemes, and device performance fluctuation ranges. The verification process adopts a layered and progressive strategy, gradually transitioning from logic function verification to physical implementation verification.

[0097] Logical function verification ensures that the architecture configuration information can correctly execute the fingerprint image processing flow, including core algorithms such as feature extraction and matching operations. Simulation test vectors are used to verify whether the input and output behavior of each module meets expectations, with particular attention to data consistency when multiple modules work together. Physical implementation verification focuses on the feasibility of chip placement and routing, checking for physical layer issues such as timing violations, signal crosstalk, and power supply noise. Timing verification analyzes whether the delay of the critical path meets clock cycle requirements, and power consumption verification assesses whether dynamic power consumption and leakage power consumption are within budget.

[0098] The output of design verification is a design feasibility report, which includes three core parts: performance compliance, constraint satisfaction, and a risk list. The performance compliance report compares the deviation between actual simulation results and performance evaluation metrics; the constraint satisfaction report quantitatively assesses the compliance rate with hard metrics such as chip area and power consumption; and the risk list lists potential problems affecting tape-out (such as excessive clock skew and heat dissipation bottlenecks). The feasibility report provides data support for subsequent overall architecture integration and optimization. For example, if the report shows that a module's power consumption exceeds the limit, the voltage domain division needs to be readjusted during the integration phase.

[0099] Overall architecture integration and optimization is the final stage of the design process, aiming to further improve the chip's overall performance while meeting all constraints. This stage uses chip architecture information as a framework, combined with verification results from feasibility studies, to globally coordinate computing, storage, and communication resources. The core of integration and optimization lies in balancing performance, power consumption, and area, avoiding overall imbalances caused by localized optimizations.

[0100] Computational resource integration optimization re-examines the collaborative relationships among processing units, such as inserting pipeline buffers between feature extraction and matching operations to eliminate data waiting bottlenecks. Storage resource integration optimization unifies the management of distributed cache consistency protocols and optimizes memory access scheduling strategies for the spatial locality of fingerprint data. Communication resource integration optimization restructures the on-chip network topology, allocating dedicated data transmission channels to high-bandwidth modules (such as image sensor interfaces).

[0101] Integration optimization also needs to address the risk items identified in the feasibility report. When timing violations are concentrated in a certain module, register retiming technology is used to adjust the pipeline depth; when power hotspots are located in specific computing units, a dynamic frequency adjustment mechanism is introduced to reduce power consumption during idle periods. The optimized solution outputs a chip design scheme, including the final hardware description code, physical layout diagram, power distribution heatmap, and other deliverables.

[0102] This implementation comprehensively quantifies chip performance indicators by evaluating the quality of fingerprint chip data and combining it with single-precision prediction results. This provides a precise basis for subsequent optimization and effectively avoids the problem of insufficient consideration of the correlation between image quality and algorithm accuracy in traditional designs. Hardware architecture parameter adjustments based on performance evaluation indicators achieve coordinated optimization of computing, storage, and interconnect resources, enabling the chip to achieve an optimal balance between recognition accuracy, processing speed, and power efficiency. The architecture resource allocation scheme, through meticulous planning at the physical level, minimizes signal transmission latency and resource contention, improving overall computational efficiency. The design verification phase tightly integrates process constraints with the architecture scheme, identifying and mitigating manufacturing risks in advance, significantly improving the tape-out success rate.

[0103] In one embodiment, the architecture configuration information is verified based on design constraint data and process parameters to obtain a design feasibility scheme, including: Fingerprint constraint mapping is a crucial bridge connecting chip architecture configuration and design constraints. This process precisely matches various parameters in the architecture configuration information with design constraint data, establishing a quantifiable correspondence. Architecture configuration information includes detailed information such as compute unit layout, memory hierarchy, and interconnect network topology, while design constraint data covers limitations such as chip area limits, power consumption thresholds, and operating frequency ranges. The mapping process employs feature extraction methods to identify parameters directly related to the constraints from the architecture configuration, such as the correlation between the number of compute units and power consumption constraints, and the correspondence between memory capacity and area constraints.

[0104] The fingerprint constraint mapping table is generated following a structured approach. The left side of the table lists the architecture configuration parameters, the right side labels the corresponding design constraint indicators, and the middle area records the matching degree score. The scoring system uses a three-level system: complete match, partial match, and conflict state. A complete match indicates that the parameters meet the constraints and have optimization margin; a partial match indicates that the parameters barely meet the standards but there is a risk; and a conflict state indicates that the parameters significantly exceed the constraint range. The fingerprint constraint mapping table pays special attention to the constraints unique to fingerprint recognition, such as the stringent real-time requirements of fingerprint image processing and the special computational accuracy requirements of feature matching. This table provides a data foundation for subsequent process adaptation, and the marked conflict items will become key optimization targets.

[0105] Process parameters include manufacturing specifications such as minimum transistor linewidth, number of metal layers, and dielectric material properties, while the fingerprint constraint mapping table clarifies the specific process requirements of the design. The adaptation process unfolds in two dimensions: physical adaptation and electrical adaptation. Physical adaptation checks the compatibility between the chip layout design and the process manufacturing rules, verifies whether the feature dimensions meet the minimum linewidth requirements of the process, and whether the metal trace spacing conforms to the design rule manual.

[0106] Electrical adaptation focuses on the impact of process characteristics on circuit performance, analyzing the effect of transistor speed fluctuations at different process corners on fingerprint processing timing, and evaluating the role of metal resistors in signal integrity. The fingerprint process constraint library is constructed using a hierarchical structure: the bottom layer records basic process parameters, the middle layer stores the matching relationship between the process and the design, and the top layer annotates key constraints. This library particularly emphasizes the adaptation of fingerprint sensing-specific parameters, such as the special process requirements for fingerprint capacitive sensing units and the isolation requirements for noise-sensitive analog circuits. The fingerprint process constraint library employs a dynamic update mechanism; when process parameters or design constraints change, the data in the library is adjusted synchronously in real time. The core output of this library is a set of process-verified design constraints, ensuring that subsequent design stages are conducted within feasible process boundaries.

[0107] Feature conflict resolution is a refined analysis conducted on the unique architecture of fingerprint recognition chips. The architecture configuration information defines the data processing path, and the fingerprint process constraint library clarifies implementation limitations; the combination of these two can generate various types of conflicts. The resolution process focuses on three typical conflicts: resource contention conflicts, timing path conflicts, and power distribution conflicts. Resource contention conflicts occur when multiple processing units need to access the same memory block simultaneously; timing path conflicts manifest as the inability of key fingerprint feature extraction paths to meet clock cycle requirements; and power distribution conflicts manifest as local circuit regions exceeding the power density allowed by the process. Conflict resolution employs a topology analysis method, abstracting the chip architecture into a network graph of nodes and edges, where nodes represent processing units and edges represent data channels.

[0108] The fingerprint conflict data is plotted using a multi-layer overlay technique. The base layer displays the complete architecture topology, while the conflict layer uses specific colors to mark the locations and impact ranges of various conflicts. This graphical representation intuitively reveals the spatial distribution characteristics of conflicts, such as whether they are concentrated in a specific computational region or extend along a specific data path. The fingerprint conflict data labels the severity level of each conflict, with the severity rating comprehensively considering the impact on system performance and the difficulty of remediation. This topology map not only records the current conflict state but also includes conflict propagation path analysis, predicting potential secondary problems caused by unresolved conflicts, and providing accurate input data for subsequent dynamic simulations.

[0109] Fingerprint scenario dynamic simulation is a stress test of chip design based on real-world application environments. This stage places various conflicts identified in fingerprint conflict data into typical fingerprint recognition scenarios for behavioral verification. The simulation scenarios cover multiple application modes, including normal fingerprint acquisition, wet / dry finger abnormalities, and rapid swipe recognition. Each scenario corresponds to specific data processing flows and performance requirements. The dynamic simulation engine loads architecture configuration information as the hardware foundation, a fingerprint process constraint library as operational limitations, and a conflict topology map to provide key monitoring areas.

[0110] The simulation process employs time-slicing technology, breaking down the entire fingerprint recognition process into stages such as image acquisition, preprocessing, feature extraction, and template matching, analyzing conflict behavior segment by segment. The data acquisition module records key parameters such as resource utilization, timing margin, and power consumption fluctuations at each node in real time, forming a time-series dataset. The generation of fingerprint scenario verification data emphasizes anomaly detection, categorizing errors caused by conflicts: hard errors leading to functional failures must be corrected, while soft errors causing performance degradation can be optimized and tolerated. Verification data includes core indicators such as scenario pass rate, conflict trigger frequency, and error recovery time, which are directly related to user experience parameters such as fingerprint recognition success rate and false recognition rate. The iterative nature of dynamic simulation allows for adjustments to the architecture configuration when severe conflicts are detected, forming a closed-loop design optimization process.

[0111] Fingerprint accuracy comprehensive evaluation is the final decision-making stage of design verification. Its core task is to make an authoritative judgment on whether the chip design can meet the requirements of high-precision fingerprint recognition. The evaluation process establishes a multi-dimensional analysis matrix. The horizontal dimension includes engineering indicators such as functional correctness, performance compliance, power consumption compliance, and area utilization. The vertical dimension covers performance stability under different fingerprint scenarios. Fingerprint scenario verification data provides actual operational evidence, while the fingerprint constraint mapping table provides acceptance criteria. The comparison between the two adopts a weighted scoring mechanism. The evaluation algorithm pays special attention to key fingerprint recognition indicators: the false acceptance rate determines the security level, the rejection rate affects the user experience, and the processing speed relates to the system smoothness.

[0112] The comprehensive evaluation adopts a tiered conclusion system: designs that pass the initial assessment can directly proceed to mass production preparation; designs that pass the conditional assessment must be accompanied by a list of optimization suggestions; and designs that fail must be re-architected and iterated. The preparation of design feasibility solutions uses a standardized template, including a conflict resolution statistics table, a constraint compliance checklist, and a risk residual assessment matrix. The report's conclusion section clearly indicates the design maturity level and provides specific process adjustment suggestions and testing priorities for the subsequent tape-out stage, ensuring a smooth transition from chip design to product.

[0113] This embodiment achieves precise matching between chip architecture parameters and design constraints by establishing a fingerprint constraint mapping table, effectively identifying key conflict points in the design and providing a clear direction for subsequent optimization. Sensing process adaptation based on process parameters and the fingerprint constraint mapping table ensures a perfect fit between chip design and manufacturing process, avoiding design rework due to process limitations. The fingerprint conflict data generated by feature conflict parsing intuitively displays the spatial distribution and severity of various conflicts, enabling designers to quickly locate problem areas. Dynamic simulation of fingerprint scenarios, through testing and verification in real application scenarios, identifies potential performance bottlenecks and functional defects in advance, significantly reducing tape-out risks. Comprehensive fingerprint accuracy evaluation comprehensively examines the design from multiple dimensions, ensuring that the chip meets engineering constraints and guarantees excellent fingerprint recognition performance.

[0114] Reference Figure 2 As shown, the present invention also provides a high-precision fingerprint recognition chip design apparatus and a high-precision fingerprint recognition chip design method applied to any of the above-mentioned methods, comprising: The acquisition module is used to acquire process parameters and design constraint data of multiple fingerprint recognition chips, classify and match them to obtain chip architecture information; The analysis module is used to collect real-time performance data of each fingerprint recognition chip based on the chip architecture information, and to extract features to obtain fingerprint chip data. The association module is used to predict the recognition accuracy of each fingerprint recognition chip based on real-time performance data and fingerprint chip data, and obtain a single accuracy prediction result. The processing module is used to optimize chip performance based on fingerprint chip data and single-precision prediction results to obtain a chip design scheme.

[0115] This invention provides a high-precision air fingerprint recognition chip design device. By acquiring and classifying the process parameters of multiple fingerprint recognition chips, it can dynamically adjust the chip architecture for different process conditions, effectively solving the problem of mismatch between process parameters and design architecture in traditional methods, and significantly improving chip production yield and performance consistency. Based on feature extraction and analysis of real-time performance data and fingerprint chip data, it achieves accurate monitoring of chip operating status, overcoming the limitations of traditional methods that rely on static test data, making design optimization more targeted and timely. By combining real-time performance data and fingerprint chip data for accuracy prediction, it can more comprehensively evaluate the actual recognition performance of the chip, solving the problem of large deviations between prediction results and actual applications in traditional methods, and providing a reliable basis for subsequent optimization. Performance optimization based on fingerprint chip data and accuracy prediction results enables dynamic adjustment of design parameters, avoiding the lag of traditional post-feedback optimization modes, significantly accelerating chip iteration speed, and responding more quickly to changes in market demand.

[0116] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0117] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A high-precision fingerprint recognition chip design method, characterized in that, include: The process parameters and design constraints of multiple fingerprint recognition chips are obtained, classified and matched to obtain chip architecture information; Real-time performance data of each fingerprint recognition chip is collected based on the chip architecture information, and feature extraction is performed to obtain fingerprint chip data; Based on the real-time performance data and the fingerprint chip data, the recognition accuracy of each fingerprint recognition chip is predicted to obtain a single accuracy prediction result. Based on the fingerprint chip data and the single-precision prediction results, chip performance is optimized to obtain a chip design scheme.

2. The high-precision fingerprint recognition chip design method according to claim 1, characterized in that, The process involves acquiring process parameters and design constraint data from multiple fingerprint recognition chips, classifying and matching them to obtain chip architecture information, including: The process nodes of the multiple fingerprint recognition chips are scanned and standardized to obtain a set of process parameters; The layout design rules and circuit constraints of each fingerprint recognition chip are obtained, and a hierarchical structure analysis is performed to obtain a design constraint dataset. Based on the set of process parameters and the set of design constraints, multiple fingerprint recognition chips are clustered to obtain chip classification results. For each group in the chip classification results, a representative chip template is constructed, and its features are compared with the set of process parameters to obtain a matching evaluation result. Based on the matching evaluation results, the architecture type of each fingerprint recognition chip is determined and the structural parameters are integrated to obtain chip architecture information.

3. The high-precision fingerprint recognition chip design method according to claim 2, characterized in that, The process of constructing a representative chip template for each group in the chip classification results and comparing its features with the process parameter set to obtain a matching evaluation result includes: The chip classification results are grouped and the process is extracted to generate grouped process feature groups; The ridge principal component dimension reduction projection is performed on the grouped process feature groups to obtain feature projection information; Based on the feature projection information, the chip classification results are grouped and typical features are filtered to obtain a candidate template chip set; Multi-objective constraint template construction is performed on the candidate template chip set to obtain representative chip templates; The representative chip template is matched with the set of process parameters using fingerprint features to obtain the matching evaluation result.

4. The high-precision fingerprint recognition chip design method according to claim 1, characterized in that, The step of collecting real-time performance data of each fingerprint recognition chip based on the chip architecture information and performing feature extraction to obtain fingerprint chip data includes: The chip architecture information is used to identify architecture units and calibrate sampling partitions to obtain an initial sampling interval; The fingerprint recognition chip is segmented and its signal is sampled according to the initial sampling interval to obtain the real-time performance data. Texture structure offset analysis is performed on the real-time performance data to obtain offset data; Based on the offset data, boundary features are extracted from the real-time performance data to obtain boundary texture mapping data; The boundary texture mapping data is subjected to local region refinement and directional encoding to obtain directional encoded map data; The initial sampling interval is reverse-constructed based on the directional coding map data to obtain reverse structure recombination information, which is then fused with the chip architecture information to obtain the fingerprint chip data.

5. The high-precision fingerprint recognition chip design method according to claim 4, characterized in that, The step of reversibly constructing the initial sampling interval based on the directional coding map data to obtain reverse structure recombination information, and fusing it with the chip architecture information to obtain the fingerprint chip data includes: The directional coding map data is classified by map feature to obtain partitioned directional feature data; Based on the initial sampling interval and the partition direction feature data, the sampling interval is reconstructed to obtain the reconstructed interval data; Based on the reconstructed interval data and the directional coding map data, the reverse traversal point sequence and fingerprint structure are synthesized in reverse to obtain the initial reverse structure data; The initial reverse structure data is optimized and the mesh is adjusted to obtain reverse structure recombination information; The reverse structure reconstruction information and the chip architecture information are fused in a multimodal manner to obtain the fingerprint chip data.

6. The high-precision fingerprint recognition chip design method according to claim 1, characterized in that, The step of predicting the recognition accuracy of each fingerprint recognition chip based on the real-time performance data and the fingerprint chip data to obtain a single accuracy prediction result includes: The real-time performance data is processed by chip-level parameter parsing to obtain chip hardware characteristic parameters; Based on the fingerprint chip data and the chip hardware feature parameters, performance identification and correlation are performed to obtain chip processing performance data; The chip processing performance data is estimated by stepwise precision transfer to obtain hierarchical precision evaluation data. Based on the hierarchical accuracy evaluation data, the multi-scenario recognition accuracy prediction is performed on each fingerprint recognition chip to obtain the single accuracy prediction result.

7. The high-precision fingerprint recognition chip design method according to claim 1, characterized in that, The step of optimizing chip performance based on the fingerprint chip data and the single-precision prediction result to obtain a chip design scheme includes: The fingerprint chip data is subjected to quality assessment, and the performance is assessed by combining the single-precision prediction results to obtain performance evaluation indicators. Based on the performance evaluation metrics, the hardware architecture parameters of the chip architecture information are adjusted to obtain hardware architecture optimization parameters. Based on the hardware architecture optimization parameters, architecture resources are allocated to obtain architecture configuration information; Based on the design constraint data and the process parameters, the architecture configuration information is verified to obtain a feasible design solution. Based on the chip architecture information, the overall architecture of the design feasibility scheme is integrated and optimized to obtain the chip design scheme.

8. The high-precision fingerprint recognition chip design method according to claim 7, characterized in that, The step of verifying the architecture configuration information based on the design constraint data and the process parameters to obtain a feasible design solution includes: Fingerprint constraint mapping is performed on the architecture configuration information and the design constraint data to obtain a fingerprint constraint mapping table; Based on the process parameters and the fingerprint constraint mapping table, the sensing process is adapted to obtain the fingerprint process constraint library. The architecture configuration information and the fingerprint process constraint library are subjected to feature conflict analysis to obtain fingerprint conflict data; Based on the fingerprint conflict data, a dynamic simulation of the fingerprint scenario is performed to obtain fingerprint scenario verification data; A comprehensive evaluation of fingerprint accuracy is performed on the fingerprint scene verification data and the fingerprint constraint mapping table to obtain the design feasibility scheme.

9. A high-precision fingerprint recognition chip design device, characterized in that, The high-precision fingerprint recognition chip design method applied to any one of claims 1-8 includes: The acquisition module is used to acquire process parameters and design constraint data of multiple fingerprint recognition chips, perform classification and matching, and obtain chip architecture information. The analysis module is used to collect real-time performance data of each fingerprint recognition chip according to the chip architecture information, and to extract features to obtain fingerprint chip data. The association module is used to predict the recognition accuracy of each fingerprint recognition chip based on the real-time performance data and the fingerprint chip data, and obtain a single accuracy prediction result. The processing module is used to optimize chip performance based on the fingerprint chip data and the single-precision prediction result to obtain a chip design scheme.

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