An intelligent adaptive semiconductor packaging test optimization method

Abstract

The present invention discloses an intelligent adaptive semiconductor packaging test optimization method, which relates to the technical field of semiconductor packaging testing, and includes: S1: constructing a quantization topological sensing network, and collecting test parameters through a quantum tunneling effect dielectric sensing array and a topological photonic crystal waveguide field reconstruction technology. This intelligent adaptive semiconductor packaging test optimization method, through a deep coupling architecture of quantum topological dynamic sensing and bionic element learning decision-making, realizes the self-consistent optimization of a high-dimensional dynamic test system by establishing an eigenmanifold space of test parameters, and solves the problem of the time-space-frequency domain limitation in the multi-dimensional parameter coupling detection of traditional test technologies under the advanced packaging of semiconductor devices.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor packaging and testing, and specifically provides an intelligent adaptive semiconductor packaging and testing optimization method. Background Art

[0002] At present, as semiconductor devices are developing rapidly towards high-density integration and heterogeneous integration, advanced packaging solutions represented by Chiplet technology have become the key path to continue Moore's Law; however, the complexity of the packaging system has increased exponentially, and traditional testing technologies are facing severe challenges. The core problem to be solved is that it is difficult to break through the spatio-temporal frequency domain limitations to achieve multi-dimensional parameter coupling detection. The current mainstream testing methods rely on discrete detection means, and there is a problem of inaccurate capture of cross-scale parameters. In the Chiplet heterogeneous integration scenario, the existing solutions based on the electrical performance tester ATE are difficult to capture transient signal distortion due to sampling frequency limitations, resulting in an increase in the undetected rate of early failures and insufficient warning capabilities for potential failure modes such as interface delamination and dielectric breakdown, which seriously restricts the commercialization process of highly reliable semiconductor devices. Therefore, it is urgent to develop an intelligent adaptive semiconductor packaging and testing optimization method to solve the above problems. Summary of the Invention

[0003] (I) Technical Problems to be Solved

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent adaptive semiconductor packaging and testing optimization method, which solves the problem of the spatio-temporal frequency domain limitations in multi-dimensional parameter coupling detection of traditional testing technologies under the advanced packaging of semiconductor devices.

[0005] (II) Technical Solutions

[0006] To achieve the above object, the present invention is implemented through the following technical solutions: an intelligent adaptive semiconductor packaging and testing optimization method, the method comprising:

[0007] S1: Construct a quantized topological sensing network, and collect test parameters with high-dimensional and sub-microsecond responses through the dielectric sensing array based on the quantum tunneling effect and the topological photonic crystal waveguide field reconstruction technology;

[0008] S2: Adopt a non-Hermitian exceptional point sensing mechanism, implant a PT-symmetric microcavity array to perform hypersensitive responses to interface defects, and capture nanoscale defects and environmental noise interference in real time;

[0009] S3: Based on the collected test parameters, use a neuromorphic pulse decision network SNN for dynamic decision optimization, and utilize the spike-timing-dependent plasticity STDP mechanism to synchronously converge the optimization of test parameters and physical constraints;

[0010] S4: Construct a meta-learning - physical hybrid optimization framework to optimize decision latency through a dual-channel gradient manifold, and adjust the test system parameters in real time to break through the spatio-temporal frequency domain limitations of traditional testing technologies and improve testing accuracy and efficiency;

[0011] S5: Adopt the construction of a dynamic eigenmanifold space, map high-dimensional test parameters to a compact Lie group representation space through non-commutative Fourier transform, and fuse thermodynamic, electrical signal, and mechanical stress parameters to achieve cross-domain parameter coupling optimization.

[0012] Preferably, the quantization topological sensing network includes: a nanoscale dielectric constant sensor network constructed by single-electron transistors SET, with a sensitivity of 1e -5 C / cm 2 and sense the charge distribution of the dielectric layer in the 30 GHz frequency band through quantum tunneling current, with a response time reaching the sub-microsecond level (Δt < 0.2 μs); to construct the sensing network, single-electron transistors SET are used as basic detection units. The SET device uses the quantum tunneling effect to achieve precise control of the transmission of single electrons, and by achieving extremely low noise interference at the nanoscale, the device has extremely high sensitivity to small changes in the dielectric constant; specifically, in implementation: a large number of SET arrays prepared through micro-nano processing technology are arranged on the chip surface, and each SET unit serves as an independent sensing node; the sensor network forms a quantization topological structure through high-density interconnection. This structure can achieve continuous monitoring in space and use topological optimization algorithms to ensure the high fidelity of signal transmission between nodes, avoiding information distortion problems introduced by physical contact or transmission loss in traditional sensor networks; in a high-frequency test environment, by utilizing the quantum tunneling effect of SET, real-time monitoring of the charge distribution of the dielectric layer is achieved. This process mainly includes: setting the working voltage conditions so that SET can effectively trigger the quantum tunneling phenomenon in the 30 GHz frequency band. Since the quantum tunneling current is extremely sensitive to changes in the local charge distribution within the dielectric layer, it can capture as small as 1e -5 C / cm 2Level changes; the tunneling current signals collected by each node of the sensor array are processed by a dedicated low-temperature amplification and noise filtering circuit to obtain data with a high signal-to-noise ratio. This method effectively overcomes the problem that traditional LCR meters cannot continuously capture signal distortion due to limited frequency bands when measuring in the millimeter-wave band; a specially designed high-speed readout system is adopted to control the overall response time of the SET sensing network within the sub-microsecond level (Δt < 0.2 μs). The implementation methods include: integrating a high-speed analog-to-digital converter ADC and a real-time data preprocessing module to ensure the acquisition, format conversion, and preliminary feature extraction of data from each sensing node within an extremely short time; using a parallel processing architecture to synchronously input signals from hundreds or even thousands of sensing nodes to the backend quantum topology data fusion module, and transmitting them through high-speed optical fibers or millimeter-wave channels to achieve seamless connection from the sensing end to the data processing center; using a non-linear mapping algorithm to map the original signals obtained from the SET sensors to a high-dimensional topological space, and obtaining stable coupling parameters through topological invariant analysis; during the mapping process, non-commutative Fourier transform and Lie group representation methods are adopted to uniformly transform thermodynamic, electrical signal, and mechanical stress parameters into a compact Lie group space to achieve tensor product fusion of cross-domain parameters and avoid losses during information dimensionality reduction; finally, under this topological structure, a self-consistent dynamic parameter space is constructed to provide an accurate, continuous, and high-dimensional data basis for subsequent real-time optimization using a bionic element learning decision engine.

[0013] A test probe integrating topological optical metamaterials is used to invert the electromagnetic field distribution within a three-dimensional stacked structure by varying the local density of optical states (LDOS) of photons, breaking through the diffraction limit and achieving a spatial resolution of 200 μm at 30 GHz. By using advanced nano-lithography and self-assembly techniques, a topological optical metamaterial structure is integrated at the end of the test probe. This structure consists of periodically arranged sub-wavelength units (meta-atoms), and its design is based on the principles of topological photonics, capable of generating non-trivial optical transmission modes. In the probe design, a modulation structure is embedded in the key area, making the local photon density of states extremely sensitive to the changes in the electromagnetic field distribution of the surrounding environment. By changing the arrangement pattern and geometric parameters of the metamaterial units, high-sensitivity detection of local electromagnetic field changes can be achieved. When the incident high-frequency signal (30 GHz) irradiates the probe, the local density of states within the metamaterial structure will change subtly with the electromagnetic field distribution inside the three-dimensional stacked structure. By pre-establishing a mapping model between the LDOS and the internal field distribution obtained through electromagnetic numerical simulation and experimental calibration, the continuous distribution of the electromagnetic field can be inversely calculated in real time. Using a reverse design algorithm, the measured LDOS change data is input into a preset non-linear mapping model, and through iterative solution and global optimization (such as using sparse representation or tensor decomposition techniques), accurate three-dimensional electromagnetic field distribution data can be obtained. This method uses topological invariants as constraints during the mapping process to ensure the stability and robustness of the inversion result in terms of the topological structure, avoiding the errors caused by the diffraction limit in traditional discrete measurement methods.

[0014] Preferably, the non-Hermitian exceptional point sensing mechanism includes:

[0015] PT-symmetric microcavity arrays are implanted at the hybrid bonding interface. Through the ultra-sensitive response characteristics of micro-cracks at the exceptional points, the sensitivity is improved by three orders of magnitude compared with traditional acoustic detection. It can online detect interface defects at the 10-nm level and resist the interference of packaging stress environmental noise.

[0016] Construction process of PT-symmetric microcavity array: The microcavity array includes multiple nanoscale microcavity units with the same size and optimized arrangement. Each microcavity unit is made of a special material with PT symmetry, and the material selection is based on the ability to achieve extremely high sensitivity to changes in electromagnetic fields, stress, and thermal distribution. Each microcavity in the microcavity array has a symmetrically configured cavity shape and has non-linear response characteristics. When local stress, temperature, or electromagnetic field changes cause microcracks, it can produce an amplification effect within the resonance frequency range, thereby significantly improving the sensitivity of defect detection. The microcavity array uses a global optimization algorithm to ensure the uniform distribution of each microcavity unit in space and maximize the spatial resolution. The spacing between each microcavity unit does not exceed 5 μm to ensure the high-resolution monitoring ability of the overall array for microcracks. The designed microcavity size of the array ranges from dozens of nanometers to hundreds of nanometers, which can match the microcracks, interface defects, or stress concentration areas in semiconductor packaging materials and provide precise capture of defects at the 10-nm level. The material used in the microcavity array is a high-sensitivity PT-symmetric material, which can produce a significant non-linear response when facing microcracks or interface defects. Especially at the Exceptional Points, the response to cracks reaches extremely high sensitivity, and its sensitivity improvement is at least three orders of magnitude higher than that of traditional acoustic detection techniques. The material selection of the microcavity is based on quantum optical and nanomechanical properties, which can enhance the amplification effect of small changes in electromagnetic fields or stress fields caused by cracks.

[0017] Through internal structure optimization, the microcavity unit can produce a strong resonance effect within a specific frequency range. When a small perturbation caused by local cracks or defects occurs, the microcavity array will exhibit a non-linear amplification effect, greatly enhancing the response to defects. This design can effectively capture the frequency shift caused by microcracks and achieve precise monitoring of interface defects. The resonance frequency range of the microcavity array covers a high-frequency band from several GHz to 30 GHz to ensure the ability to detect electromagnetic field changes occurring during the packaging process in real time.

[0018] Preferably, the neuromorphic pulse decision-making network SNN optimizes the test parameters through the following steps:

[0019] Sa: Encode 80-dimensional test parameters into neural pulse phases (0 - 2π);

[0020] Sb: Map the mechanical constraints of the packaging structure to synaptic weights (σ max ≤180 MPa corresponds to the weight threshold);

[0021] Sc: Convert the dynamic power consumption fluctuation into pulse firing rate modulation, (Δf = ±15 W / μs → pulse frequency adjustment gradient).

[0022] Preferably, the process of optimizing test parameters based on the neuromorphic pulse decision-making network SNN in step Sa includes: obtaining 80-dimensional test parameters, which include multi-dimensional signals from semiconductor package test equipment, and these signals involve multiple physical quantities such as electrical performance, thermal performance, mechanical stress, and interface defects; performing normalization processing on each test parameter, processing the original 80-dimensional test parameters through a standardization method to make them within the same range of measurement, so as to avoid the influence of differences between different parameter orders of magnitude on the neural pulse phase encoding process; mapping each normalized test parameter to a pulse phase value (0-2π), each test parameter is mapped to a pulse phase through a non-linear function, and the value range of the pulse phase is from 0 to 2π, so that the phase value of each pulse can reflect the intensity and characteristics of the corresponding test parameter, and this mapping function is designed using the spike-timing-dependent plasticity (STDP) mechanism commonly used in neuromorphic computing to ensure the accurate mapping of the phase value; using a neural pulse network with pulse phase encoding, converting the 80-dimensional test parameters into phase pulses and inputting them into the neuromorphic pulse neural network, each phase value corresponds to the pulse firing timing of a neuron, and these neurons are connected together through synapses to form a pulse neural network for synchronous learning and optimizing the parameters of the test system. The encoding method of the neural pulse phase value enables the output of each neuron to accurately reflect the changes in the input parameters; using the dynamic learning mechanism of the pulse neural network to optimize the test parameters, the synaptic weights in the neuromorphic pulse neural network are dynamically adjusted through the spike-timing-dependent plasticity STDP mechanism, and the timing and phase value of each pulse firing are transmitted through the pulse propagation in the network to form a feedback loop inside the network, optimizing the mapping relationship between the test parameters and physical quantities. This dynamic optimization can adapt to the real-time changing packaging environment and different test requirements; based on multi-dimensional coupling detection of neural pulse timing, through pulse phase encoding, the firing timing of each neuron will be adjusted according to the phase of the input signal to form a high-dimensional pulse timing pattern. Through this timing pattern, the SNN can achieve effective coupling and interaction between multiple physical dimensions, breaking through the single parameter monitoring in traditional methods and overcoming the spatio-temporal frequency domain limitation problem of multi-dimensional parameter coupling detection; updating the test strategy in real time, through the adaptive optimization mechanism of the SNN, the system can update the test parameters and optimize the test strategy in real time during the packaging test process; after each detection is completed, the pulse neural network will readjust the weights according to the feedback signal, thereby improving the test accuracy and reliability; especially in the case of complex packaging structures, the SNN can improve the ability to capture multi-dimensional effects such as micro-cracks, stress, and thermal distribution by learning the coupling relationship of various physical effects in the package in real time;Adopt an efficient multi-layer SNN architecture. The SNN performs hierarchical processing and optimization through a multi-layer neural network structure. Each layer of the neural network processes according to different dimensions of the input signal and finally outputs optimized test parameters. This hierarchical structure effectively enhances the SNN's processing ability for complex multi-dimensional data and ensures efficient and accurate parameter optimization.;

[0023] Preferably, the process of mapping the mechanical constraints of the packaging structure to synaptic weights in step Sb includes: obtaining the mechanical constraint parameters of the packaging structure, which include multiple physical quantities such as mechanical stress, mechanical load, contact pressure, etc. in semiconductor packaging. The mechanical constraints are determined by factors such as the material, size, and shape of the packaging structure; normalizing the mechanical constraint parameters so that the original mechanical constraint parameters (such as the maximum stress σ max are processed by a normalization method so that they can be jointly analyzed with other test parameters (such as electrical performance, thermal performance, etc.) on a unified scale; mapping the mechanical constraint parameters to synaptic weights. The normalized mechanical constraints (such as the maximum stress σ max ≤ 180 MPa) are mapped to synaptic weights. Specifically, this stress value is mapped to the synaptic connection strength in the neural pulse network. When setting the stress threshold (such as σ max ≤ 180 MPa), the relationship between the stress value and the synaptic weight is mapped through a non-linear function to realize the dynamic influence of stress in the network; dynamic update of synaptic weights. Through the spike-timing-dependent plasticity (STDP) mechanism of the neuromorphic pulse neural network SNN, the synaptic weights will be updated in real time during the test process. As the mechanical constraint conditions change, the weights of the synapses will also be adjusted accordingly, so that the neural network can accurately reflect the influence of mechanical constraints on the packaging structure;

[0024] Co-optimization of synaptic weights and test parameters. The synaptic weights in the neural pulse network not only depend on mechanical constraints but also are jointly optimized with other physical test parameters (such as electrical performance, thermal effects, etc.) to ensure that the coupling relationship between multi-dimensional parameters in the test system can be accurately captured; multi-dimensional processing of mechanical constraint parameters through a multi-layer neural network. The SNN adopts a multi-layer neural network architecture. Each layer of neurons jointly adjusts the synaptic weights according to the input mechanical constraint parameters and other physical quantities. The information processed by each layer will provide more accurate feedback for the neurons in the lower layer to achieve co-optimization of multi-dimensional mechanical constraints and physical quantities. Adjust the test strategy based on the feedback mechanism. The feedback mechanism in the neural pulse network ensures that during the actual test process, the influence of mechanical constraints on the test parameters can be transmitted layer by layer through the network, adjust the test parameters in real time, and optimize the test strategy. This process not only improves the test accuracy but also can adaptively adjust the test process according to the actual mechanical changes of the packaging structure.

[0025] Preferably, the process of converting dynamic power consumption fluctuations into pulse firing rate modulation in step Sc includes: obtaining the power consumption data of the package test equipment, including dynamic power consumption fluctuation information, which reflects the energy consumption and heat dissipation characteristics of the package structure, materials, and devices during the test. The dynamic power consumption fluctuations are closely related to the changes in various physical quantities (such as current, voltage, temperature, etc.) during the test; normalizing the power consumption fluctuations, performing unified scale processing on the dynamic power consumption fluctuation signal and other test signals (such as electrical performance, mechanical stress, etc.), ensuring that the power consumption signal can act together with other physical quantities and be incorporated into the subsequent neural pulse coding process; converting the normalized dynamic power consumption fluctuations into pulse firing rate modulation. This process maps the dynamic power consumption fluctuations to the firing rate modulation signal in the neuromorphic pulse neural network. In this process, the intensity of the power consumption fluctuations is converted into the firing frequency (or pulse firing rate) of the pulses, that is, the amplitude of the power consumption fluctuations directly affects the firing frequency of the pulses. Higher power consumption fluctuations will result in more frequent pulse firing; establishing a mapping relationship between the pulse firing rate and the power consumption fluctuations through a non-linear function to ensure that the changes in the power consumption fluctuations can be effectively transmitted to the pulse firing process of the neural network; this mapping relationship can be obtained through experimental learning, enabling the characteristics of the power consumption fluctuations to be fully reflected and optimized in the network.

[0026] Preferably, the meta-learning - physical hybrid optimization framework optimizes the decision-making delay through the following process: establishing a continuous implicit function f θ (x,t) of the parameter space through the implicit neural representation INR, and realizing the covariant optimization of two types of gradients in the SO(3)×R n manifold space through the Lie group differential geometry method; reducing the decision-making delay from 300 ms to 8 ms on the basis of traditional optimization methods, significantly improving the test efficiency.

[0027] Preferably, the construction of the dynamic eigenmanifold space is realized through the following steps:

[0028] Mapping the high-dimensional test parameters to the compact Lie group representation space through non-commutative Fourier transform to avoid information loss caused by traditional PCA dimensionality reduction.

[0029] Performing tensor product fusion of cross-domain parameters in the group representation space to improve the coupling optimization effect of multi-dimensional parameters.

[0030] The optimization method breaks through the spatio-temporal frequency domain limitations of traditional test technologies, can capture and optimize the transient signal distortion in the package test process in real time, and improves the early warning ability of potential failure modes.

[0031] An intelligent adaptive semiconductor package test optimization system, applied to semiconductor package test equipment, includes:

[0032] A quantization topological sensing network for collecting test parameters with high-dimensional and sub-microsecond response;

[0033] A bionic element learning dynamic decision-making engine that optimizes test parameters and synchronously converges physical constraints based on a neuromorphic spiking decision-making network SNN;

[0034] A dynamic eigenmanifold space construction module for cross-domain parameter coupling optimization to achieve a self-consistent optimized test system;

[0035] Among them, when the bionic element learning dynamic decision-making engine dynamically adjusts test parameters, the test efficiency and accuracy are further improved through the following steps: Based on historical test data and actual feedback results, an enhanced learning mechanism is used to optimize the initial settings of test parameters, automatically identify and correct systematic errors in the test process; The deep reinforcement learning DRL algorithm is introduced to optimize the model parameters according to the loss function after each test, realizing real-time adjustment during the test process, ensuring that the test system can quickly adapt to different packaging structures and failure modes, and providing an accurate test optimization scheme;

[0036] During the test, the quantum tunneling effect dielectric sensing array in the quantization topological sensing network realizes real-time dynamic monitoring of the dielectric constant by adaptively adjusting the tunneling current intensity and frequency; Utilizing the high sensitivity of the quantum tunneling current to the charge distribution of the dielectric layer, the microscopic electrical characteristics of the material layer are feedback in real time, and the precursors of potential failure modes such as dielectric breakdown are captured in a timely manner, providing high-precision test data and providing a basis for further optimization decisions.

[0037] (III) Beneficial effects

[0038] The present invention provides an intelligent adaptive semiconductor package test optimization method with the following beneficial effects:

[0039] (I). For this intelligent adaptive semiconductor package test optimization method, this solution abandons the linear improvement path of traditional sensor stack superposition algorithm optimization. Through the deep coupling architecture of quantum topology dynamic sensing and bionic element learning decision-making, by establishing the eigenmanifold space of test parameters, self-consistent optimization of the high-dimensional dynamic test system is realized, and the problem of the time-space-frequency domain limitation of traditional test technologies in multi-dimensional parameter coupling detection under advanced semiconductor device packaging is solved.

[0040] (2) The intelligent adaptive semiconductor package testing optimization method converts dynamic power consumption fluctuations into pulse firing rate modulation. This technical solution innovatively embeds power consumption fluctuation information into a spiking neural network, breaking through the spatio-temporal frequency domain limitations of traditional testing methods and achieving multi-dimensional parameter coupling detection. This process dynamically adjusts the pulse firing rate, enabling package testing to provide dynamic feedback based on the impact of power consumption fluctuations on various physical effects, optimizing testing strategies and parameters, and improving the real-time performance, accuracy, and adaptive ability of testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic framework diagram of the whole invention;

[0042] Figure 2 It is a schematic flowchart of the communication protocol of the invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figure 1 and Figure 2 The present invention provides a technical solution: an intelligent adaptive semiconductor package testing optimization method, the method includes:

[0045] S1: Construct a quantization topology sensing network, and collect test parameters with high-dimensional and sub-microsecond-level responses through a quantum tunneling effect dielectric sensing array and a topology photonic crystal waveguide field reconstruction technology;

[0046] S2: Adopt a non-Hermitian exceptional point sensing mechanism, implant a PT-symmetric microcavity array to perform hypersensitive responses to interface defects, and capture nanoscale defects and environmental noise interference in real time;

[0047] S3: Based on the collected test parameters, use a neuromorphic spiking decision-making network SNN for dynamic decision optimization, and utilize the spike-timing-dependent plasticity STDP mechanism to synchronously converge the test parameter optimization and physical constraints;

[0048] S4: Construct a meta-learning-physics hybrid optimization framework, optimize the decision delay through a two-channel gradient manifold, and adjust the test system parameters in real time to break through the spatio-temporal frequency domain limitations of traditional testing technologies and improve the testing accuracy and efficiency;

[0049] S5: The construction of a dynamic eigenmanifold space is adopted. Through non-commutative Fourier transform, high-dimensional test parameters are mapped to a compact Lie group representation space, integrating thermodynamic, electrical signal, and mechanical stress parameters to achieve cross-domain parameter coupling optimization.

[0050] The quantized topological sensing network includes: a nanoscale dielectric constant sensor network constructed by single-electron transistors (SETs), with a sensitivity of 1e -5 C / cm 2 And the charge distribution of the dielectric layer in the 30 GHz frequency band is sensed through the quantum tunneling current, with a response time reaching the sub-microsecond level (Δt < 0.2 μs); to construct the sensing network, single-electron transistors (SETs) are used as the basic detection units. The SET device utilizes the quantum tunneling effect to achieve precise control of the transmission of single electrons. By achieving extremely low noise interference at the nanoscale, the device has extremely high sensitivity to small changes in the dielectric constant; specifically, in implementation: a large number of SET arrays prepared through micro-nano processing technology are arranged on the chip surface, and each SET unit serves as an independent sensing node; the sensor network forms a quantized topological structure through high-density interconnection. This structure can achieve continuous monitoring in space and uses topological optimization algorithms to ensure the high fidelity of signal transmission between nodes, avoiding information distortion problems introduced by physical contact or transmission loss in traditional sensor networks; in a high-frequency test environment, by utilizing the quantum tunneling effect of SETs, real-time monitoring of the charge distribution of the dielectric layer is achieved. This process mainly includes: setting the working voltage conditions so that SET can effectively trigger the quantum tunneling phenomenon in the 30 GHz frequency band. Since the quantum tunneling current is extremely sensitive to changes in the local charge distribution within the dielectric layer, it can capture as small as 1e -5 C / cm 2Level changes; the tunneling current signals collected by each node of the sensor array are processed by a dedicated low-temperature amplification and noise filtering circuit to obtain data with a high signal-to-noise ratio. This method effectively overcomes the problem that traditional LCR meters cannot continuously capture signal distortion due to limited frequency bands during millimeter-wave frequency band measurements; a specially designed high-speed readout system is adopted to control the overall response time of the SET sensing network within the sub-microsecond level (Δt < 0.2 μs). The implementation methods include: integrating a high-speed analog-to-digital converter ADC and a real-time data preprocessing module to ensure the acquisition, format conversion, and preliminary feature extraction of data from each sensing node within an extremely short time; using a parallel processing architecture to synchronously input signals from hundreds or even thousands of sensing nodes into the backend quantum topology data fusion module and transmit them through high-speed optical fibers or millimeter-wave channels to achieve seamless connection from the sensing end to the data processing center; traditional testing technologies often use discrete sampling methods in multi-dimensional parameter detection, which are prone to losing transient signal information. However, this solution realizes continuous and real-time monitoring of the dielectric layer charge distribution through quantum tunneling current detection technology, and then constructs the eigenmanifold of the parameters. The core lies in: using a non-linear mapping algorithm to map the original signals obtained from SET sensors to a high-dimensional topological space and obtaining stable coupling parameters through topological invariant analysis; during the mapping process, non-commutative Fourier transform and Lie group representation methods are used to uniformly transform thermodynamic, electrical signal, and mechanical stress parameters into a compact Lie group space to achieve tensor product fusion of cross-domain parameters and avoid losses during information dimensionality reduction; finally, under this topological structure, a self-consistent dynamic parameter space is constructed to provide an accurate, continuous, and high-dimensional data basis for subsequent real-time optimization using a bionic element learning decision engine.

[0051] Adopting SET and quantum tunneling current detection technologies fundamentally improves the sensitivity and bandwidth of dielectric constant measurement, enabling sub-microsecond-level response to be maintained even at the 30 GHz frequency band and breaking through physical dimension limitations; constructing a continuous and distributed detection system through a quantum topological sensing network, which does not rely on discrete sampling, thus effectively capturing transient signals and multi-dimensional parameter coupling phenomena and realizing continuous dynamic detection; adopting topological data fusion and non-linear mapping technologies to avoid cross-scale and cross-frequency band data mismatch problems that may occur in conventional multi-sensor data stitching, providing a new solution for multi-dimensional coupling detection under complex packaging structures and circumventing the bottleneck of conventional sensor stacking; by integrating quantum physical effects, nanoscale sensing technologies, and topological data processing methods, a new intelligent adaptive test optimization system is constructed, effectively breaking through the spatio-temporal frequency domain limitations of traditional testing technologies under high density and complex packaging, and improving the accuracy and real-time warning ability of packaging testing.

[0052] The test probe with integrated topological optical metamaterials is used to invert the electromagnetic field distribution in the three-dimensional stacked structure through the change of the local photon state density LDOS, breaking through the diffraction limit and achieving a spatial resolution of 200μm at 30GHz; using advanced nanolithography and self-assembly technology, a topological optical metamaterial structure is integrated at the end of the test probe. The structure consists of periodically arranged sub-wavelength units meta-atoms. Its design is based on the principles of topological photonics and can produce non-trivial optical transmission modes; in the probe design, a modulation structure is embedded in the key area to make the local photon state density extremely sensitive to the changes in the electromagnetic field distribution of the surrounding environment. By changing the arrangement and geometric parameters of the metamaterial units, highly sensitive detection of local changes in the electromagnetic field is achieved; when the incident When a high-frequency signal of 30 GHz is irradiated to the probe, the local state density within the metamaterial structure will undergo subtle changes with the electromagnetic field distribution inside the three-dimensional stacked structure. By pre-establishing a mapping model between the LDOS and the internal field distribution obtained based on electromagnetic numerical simulation and experimental calibration, the continuous distribution of the electromagnetic field can be inverted in real time. Using the reverse design algorithm, the measured LDOS change data is input into the preset nonlinear mapping model. After iterative solution and global optimization, that is, using sparse representation or tensor decomposition technology, accurate three-dimensional electromagnetic field distribution data is obtained. This method uses topological invariants as constraints in the mapping process to ensure that the inversion results remain stable and robust in the topological structure, avoiding errors caused by the diffraction limit in traditional discrete measurement methods.

[0053] Traditional test probes are limited by the diffraction limit and have difficulty achieving high spatial resolution. However, this solution uses topological optical metamaterial design to refine the effective sampling window of the probe to the submicron level. By utilizing local electromagnetic resonance effects and localized photon modes, the probe achieves a spatial resolution of up to 200μm in the 30GHz frequency band. An integrated dynamic phase modulator forms a controllable wavefront inside the probe, further breaking through the limitations of traditional optical systems and allowing the inversion process to capture local electromagnetic field distortions caused by tiny structural differences. This technology realizes a transition from traditional static sampling to dynamically controlled sampling, improving the test system's ability to capture transient signals.

[0054] A high-speed photoelectric detector and analog-to-digital conversion system are used to transmit the LDOS signal collected by the probe to the data processing unit in real time with sub-microsecond time resolution. Through a parallel computing platform, the LDOS data from multiple probe arrays are simultaneously input into a high-dimensional nonlinear inversion algorithm to quickly obtain a continuous distribution map of the electromagnetic field inside the entire three-dimensional stacked structure. This global data processing method greatly reduces the information omission and reconstruction errors caused by traditional discrete sampling.

[0055] This solution abandons the traditional "sensor stacking + post - processing discrete data stitching" solution. Instead, it directly realizes the continuous inversion of the electromagnetic field distribution at the physical level by integrating topological optical metamaterials, completely circumventing the limitations of the diffraction limit and cross - scale matching. By establishing a high - dimensional mapping relationship between the LDOS and the electromagnetic field distribution, combined with non - linear optimization and topological constraints, it realizes the coupled detection of multi - dimensional parameters, can simultaneously invert the mutual influence of multiple physical effects such as heat, electricity, and mechanics within the stacked structure, and provides a self - consistent dynamic test and optimization platform. At the 30GHz frequency band, it not only achieves a high spatial resolution of 200μm, but also greatly improves the response speed and signal - to - noise ratio of the system through fast dynamic focusing technology and real - time data processing, providing strong technical support for the advanced packaging testing of semiconductor devices.

[0056] The non - Hermitian exceptional point sensing mechanism includes:

[0057] PT - symmetric micro - cavity arrays are implanted at the hybrid bonding interface. Through the ultra - sensitive response characteristics of micro - cracks at the exceptional points, the sensitivity is improved compared with traditional acoustic detection. It can online detect interface defects at the 10nm level and resist the interference of packaging stress environmental noise.

[0058] The construction process of the PT - symmetric micro - cavity array: The micro - cavity array includes multiple nano - scale micro - cavity units with the same size and optimized arrangement. Each micro - cavity unit is composed of a special material with PT symmetry. The material selection is based on the ability to achieve extremely high sensitivity to changes in electromagnetic field, stress, and thermal distribution. Each micro - cavity in the micro - cavity array has a symmetrically configured cavity shape and has non - linear response characteristics. When local stress, temperature, or electromagnetic field changes cause micro - cracks, it can produce an amplification effect within the resonance frequency range, thus significantly improving the sensitivity of defect detection. The micro - cavity array uses a global optimization algorithm to ensure the uniform distribution of each micro - cavity unit in space and maximize the spatial resolution. The spacing between each micro - cavity unit does not exceed 5μm to ensure the high - resolution monitoring ability of the overall array for micro - cracks. The designed micro - cavity size of the array ranges from dozens of nanometers to hundreds of nanometers, which can match micro - cracks, interface defects, or stress concentration regions in semiconductor packaging materials, providing precise capture of 10nm - level defects. The material used in the micro - cavity array is a high - sensitivity PT - symmetric material, which can produce a significant non - linear response when facing micro - cracks or interface defects. Especially at the exceptional points, the response to cracks reaches extremely high sensitivity, and its sensitivity improvement is at least three orders of magnitude higher than that of traditional acoustic detection techniques. The material selection of the micro - cavity is based on quantum optical and nano - mechanical properties, which can enhance the amplification effect of small changes in the electromagnetic field or stress field caused by cracks.

[0059] Through internal structure optimization, the microcavity unit can generate a strong resonance effect within a specific frequency range. When minute perturbations caused by local cracks or defects occur, the microcavity array will exhibit a nonlinear amplification effect, significantly enhancing the response to defects. This design can effectively capture the frequency shift caused by microcracks and achieve precise monitoring of interface defects. The resonance frequency range of the microcavity array covers the high-frequency band from several GHz to 30 GHz to ensure the real-time detection of electromagnetic field changes occurring during the encapsulation process.

[0060] The output signals of the microcavity array are collected in real time through high-precision sensors and an analog-to-digital conversion system and input into the data processing module. Using a nonlinear optimization algorithm, the response signals of the microcavity array are precisely analyzed. Combining historical test data and environmental changes, the location information, size, and type of defects are output in real time. The data processing module can identify the evolution pattern of microcracks in the microcavity array signals and adjust the detection sensitivity through a feedback control system to adapt to changes in different encapsulation environments.

[0061] The microcavity array design can effectively resist the interference of environmental noise in complex encapsulation environments. Especially in the case of encapsulation stress, external vibration, or temperature changes, it can still maintain high-sensitivity detection of microcracks and interface defects. The PT symmetry and nonlinear response mechanism adopted by the microcavity array enable it to automatically adapt to external noise changes and ensure real-time detection effects in dynamic environments.

[0062] The size of the microcavity array is adjustable according to the requirements of encapsulation and adopts a modular design, enabling the scale of the array to be flexibly expanded according to different test needs. The size of each microcavity array module is adjustable to adapt to the test requirements of semiconductor devices with different encapsulation structures and sizes. The microcavity array can not only detect cracks but also monitor the stress distribution, thermal distribution, and electromagnetic field distribution during the encapsulation process in real time, and adjust the test parameters through a feedback mechanism to optimize the results of semiconductor encapsulation testing, ensuring high precision and high reliability of the final detection results.

[0063] By introducing PT symmetry and nonlinear resonance effects, the design of the microcavity array successfully solves the spatio-temporal frequency domain limitation problems encountered by traditional testing methods in encapsulation. Traditional technologies often rely on discretized testing methods, resulting in the inability to capture minute changes in defects such as microcracks in encapsulation materials in real time. The design of the microcavity array can monitor the stress and electromagnetic field distribution in the encapsulation structure in real time, significantly improving the spatial resolution and frequency response range of detection and breaking through the limitations of traditional technologies.

[0064] The design of the microcavity array makes it extremely sensitive to microcracks at the singular point and can play an important role in the detection of microcracks at the 10-nm level. This design not only solves the problem of insufficient sensitivity of traditional acoustic detection methods but also overcomes the difficulty of detection in the high-frequency band, ensuring high-precision detection ability within the 30-GHz frequency band.

[0065] Through the nonlinear response mechanism, the microcavity array can achieve multi-dimensional parameter coupling detection of electromagnetic fields, stress, and thermal changes. Compared with traditional single physical quantity monitoring methods, the microcavity array can simultaneously capture and analyze multiple physical effects, greatly improving the comprehensiveness and accuracy of package testing.

[0066] The neuromorphic pulse decision-making network SNN optimizes the test parameters through the following steps:

[0067] Sa: Encode 80-dimensional test parameters into neural pulse phases (0 - 2π);

[0068] Sb: Map the mechanical constraints of the package structure to synaptic weights (σ max ≤180 MPa corresponds to the weight threshold);

[0069] Sc: Convert the dynamic power consumption fluctuation into pulse firing rate modulation, (Δf = ±15 W / μs → pulse frequency adjustment gradient).

[0070] The process of optimizing test parameters based on the neuromorphic pulse decision-making network SNN in step Sa includes: obtaining 80-dimensional test parameters, which include multi-dimensional signals from semiconductor package test equipment, and these signals involve multiple physical quantities such as electrical performance, thermal performance, mechanical stress, and interface defects; normalizing each test parameter, processing the original 80-dimensional test parameters through a standardization method to make them within the same range of measurement, so as to avoid the influence of differences between different parameter orders of magnitude on the neural pulse phase encoding process; mapping each normalized test parameter to a pulse phase value (0 - 2π), each test parameter is mapped to a pulse phase through a non-linear function, and the pulse phase value range is from 0 to 2π, so that the phase value of each pulse can reflect the intensity and characteristics of the corresponding test parameter. This mapping function is designed using the spike-timing-dependent plasticity STDP mechanism commonly used in neuromorphic computing to ensure the accurate mapping of phase values; using a neural pulse network with pulse phase encoding, converting the 80-dimensional test parameters into phase pulses and inputting them into the neuromorphic pulse neural network. Each phase value corresponds to the spike firing timing of a neuron, and these neurons are connected together through synapses to form a pulse neural network for synchronous learning and optimizing the parameters of the test system. The encoding method of the neural pulse phase value enables the output of each neuron to accurately reflect the changes in the input parameters; using the dynamic learning mechanism of the pulse neural network to optimize the test parameters. The synaptic weights in the neuromorphic pulse neural network are dynamically adjusted through the spike-timing-dependent plasticity STDP mechanism. The timing and phase value of each spike firing are transmitted through the pulse propagation in the network, forming a feedback loop inside the network to optimize the mapping relationship between the test parameters and physical quantities. This dynamic optimization can adapt to the real-time changing packaging environment and different test requirements; multi-dimensional coupling detection based on neural pulse timing. Through pulse phase encoding, the firing timing of each neuron will be adjusted according to the phase of the input signal, forming a high-dimensional pulse timing pattern. Through this timing pattern, the SNN can achieve effective coupling and interaction between multiple physical dimensions, breaking through the single parameter monitoring in traditional methods and overcoming the spatio-temporal frequency domain limitation problem of multi-dimensional parameter coupling detection; real-time updating of the test strategy. Through the adaptive optimization mechanism of the SNN, the system can update the test parameters and optimize the test strategy in real time during the packaging test process; after each detection is completed, the pulse neural network will readjust the weights according to the feedback signal, thereby improving the test accuracy and reliability; especially in the case of complex packaging structures, the SNN can improve the ability to capture multi-dimensional effects such as micro-cracks, stress, and thermal distribution by real-time learning the coupling relationship of various physical effects in the packaging;Adopt an efficient multi-layer SNN architecture. The SNN performs hierarchical processing and optimization through a multi-layer neural network structure. Each layer of the neural network processes according to different dimensions of the input signal and finally outputs optimized test parameters. This hierarchical structure effectively enhances the SNN's processing ability for complex multi-dimensional data and ensures efficient and accurate parameter optimization.

[0071] By encoding 80-dimensional test parameters into neural pulse phase values, the limitations of traditional methods are broken through. Traditional test techniques usually measure each physical quantity with separate parameters and cannot handle the complex coupling between different parameters. By converting each test parameter into a pulse phase (0 - 2π), the SNN can incorporate the phase information of each test signal into the firing timing of neurons, so that the coupling relationship between different physical quantities can be reflected in the pulse timing, thus realizing multi-dimensional parameter detection.

[0072] By encoding 80-dimensional test parameters into pulse phases and utilizing the timing learning mechanism of the neuromorphic pulse neural network SNN, this technical solution breaks through the spatio-temporal frequency domain limitations of traditional test techniques in multi-dimensional parameter coupling detection and provides an efficient test method that can be optimized in real time and adapt to complex packaging environments. This innovative solution not only solves the common multi-dimensional interaction problems in packaging testing but also improves the real-time response ability and adaptive optimization ability of the system, ensuring high-precision and high-efficiency semiconductor packaging testing; realizing the synchronous convergence of test parameter optimization and physical constraints at the hardware level and improving the dynamic response ability of the test system.

[0073] The process of mapping the mechanical constraints of the packaging structure to synaptic weights in step Sb includes: obtaining the mechanical constraint parameters of the packaging structure, which include multiple physical quantities such as mechanical stress, mechanical load, contact pressure, etc. in semiconductor packaging, and the mechanical constraints are determined by factors such as the material, size, and shape of the packaging structure; normalizing the mechanical constraint parameters, normalizing the original mechanical constraint parameters, such as the maximum stress σ max Through normalization processing so that it can be jointly analyzed with electrical performance and thermal performance test parameters on a unified scale; mapping the mechanical constraint parameters to synaptic weights, the normalized mechanical constraints, such as the maximum stress σ max ≤180 MPa, are mapped to synaptic weights. Specifically, map this stress value to the synaptic connection strength in the neural pulse network, set the stress threshold, σ maxWhen the stress is ≤ 180 MPa, the relationship between the stress value and the synaptic weight is mapped through a non-linear function to realize the dynamic influence of stress in the network; the dynamic update of the synaptic weight is achieved through the spike-timing-dependent plasticity (STDP) mechanism of the neuromorphic spiking neural network (SNN). The synaptic weight will be updated in real time during the test. As the mechanical constraint conditions change, the weight of the synapse will also be adjusted accordingly, enabling the neural network to accurately reflect the influence of mechanical constraints on the packaging structure;

[0074] The co-optimization of synaptic weights and test parameters. The synaptic weights in the spiking neural network not only depend on mechanical constraints but also are jointly optimized with physical test parameters such as electrical properties and thermal effects to ensure that the coupling relationships between multi-dimensional parameters in the test system can be accurately captured; the multi-dimensional processing of mechanical constraint parameters is carried out through a multi-layer neural network. The SNN adopts a multi-layer neural network architecture. The neurons in each layer jointly adjust the synaptic weights according to the input mechanical constraint parameters and other physical quantities. The information processed by each layer provides more accurate feedback for the neurons in the lower layer to achieve the co-optimization of multi-dimensional mechanical constraints and physical quantities. Based on the feedback mechanism, the test strategy is adjusted. The feedback mechanism in the spiking neural network ensures that during the actual test process, the influence of mechanical constraints on test parameters can be transmitted layer by layer through the network, adjusting the test parameters in real time and optimizing the test strategy. This process not only improves the test accuracy but also can adaptively adjust the test process according to the actual mechanical changes of the packaging structure;

[0075] By mapping the mechanical constraints of the packaging structure to synaptic weights, this solution breaks through the spatio-temporal frequency domain limitations of traditional test methods and realizes the real-time dynamic optimization of the mechanical effects of the packaging structure. Combining the timing learning mechanism and multi-dimensional parameter coupling optimization of the neuromorphic spiking neural network (SNN), this solution can not only accurately capture the mechanical changes of the packaging structure but also adjust the test strategy in real time, improving the test accuracy and efficiency. Compared with traditional technologies, this solution has significant advantages in multi-physical effect coupling analysis and dynamic test optimization, solving the problem that traditional methods cannot adapt and optimize in real time in complex packaging environments.

[0076] The process of converting dynamic power consumption fluctuations into pulse firing rate modulation in step Sc includes: obtaining the power consumption data of the package test equipment, including dynamic power consumption fluctuation information, which reflects the energy consumption and heat dissipation characteristics of the package structure, materials, and devices during the test. The dynamic power consumption fluctuations are closely related to the changes in various physical quantities during the test: current, voltage, and temperature; normalizing the power consumption fluctuations, unifying the scale of the dynamic power consumption fluctuation signal with the electrical performance and mechanical stress test signals to ensure that the power consumption signal can interact with other physical quantities and be incorporated into the subsequent neural pulse coding process; converting the normalized dynamic power consumption fluctuations into pulse firing rate modulation. This process maps the dynamic power consumption fluctuations to the firing rate modulation signal in the neuromorphic pulse neural network. In this process, the intensity of the power consumption fluctuations is converted into the firing frequency of the pulses, that is, the amplitude of the power consumption fluctuations directly affects the firing frequency of the pulses. Higher power consumption fluctuations will result in more frequent pulse firing; establishing a mapping relationship between the pulse firing rate and the power consumption fluctuations through a non-linear function to ensure that the changes in the power consumption fluctuations can be effectively transmitted to the pulse firing process of the neural network; this mapping relationship can be obtained through experimental learning, enabling the characteristics of the power consumption fluctuations to be fully reflected and optimized in the network;

[0077] Dynamically adjusting the pulse firing rate. During the test, the pulse firing rate will be adjusted in real-time according to the changes in the power consumption fluctuations, enabling the network to respond dynamically according to the power consumption changes and thus optimizing the test parameters; the role of pulse firing rate modulation in test optimization. Through the modulation of the pulse firing rate, the neuromorphic pulse neural network can convert the power consumption fluctuations into the control signal of the network; this control signal can adjust the synaptic weights and the activation patterns of the neurons in the network, thereby affecting the decision-making process in the network and optimizing the test parameters and strategies. Especially in the multi-dimensional parameter coupling detection, it provides a new adjustment method.

[0078] Optimizing the test strategy based on the feedback mechanism of the pulse firing rate during the package test. The pulse firing rate modulation is not only used to optimize a single test parameter but also to coordinate the mutual influences between different physical quantities, namely electrical, thermodynamic, and mechanical parameters; this feedback mechanism can dynamically adjust each parameter in the package according to the power consumption fluctuations, achieving efficient detection of multi-dimensional parameter coupling; by converting the dynamic power consumption fluctuations into pulse firing rate modulation, this technical solution innovatively embeds the power consumption fluctuation information into the neural pulse neural network, breaking through the spatio-temporal frequency domain limitations of traditional test methods and realizing multi-dimensional parameter coupling detection; this process enables the package test to dynamically feedback according to the influence of the power consumption fluctuations on each physical effect by adjusting the pulse firing rate in real-time, optimizing the test strategy and parameters, and improving the real-time performance, accuracy, and adaptive ability of the test.

[0079] The meta-learning - physical hybrid optimization framework optimizes decision latency through the following process: establishing a continuous implicit function f of the parameter space through the implicit neural representation (INR). θ (x, t), and realizing the covariant optimization of two types of gradients in the SO(3)×R n manifold space through the Lie group differential geometry method; reducing the decision latency from 300 ms to 8 ms on the basis of traditional optimization methods, significantly improving the test efficiency.

[0080] The construction of the dynamic eigenmanifold space is realized through the following steps:

[0081] Mapping high-dimensional test parameters to the compact Lie group representation space through non-commutative Fourier transform, avoiding information loss caused by traditional PCA dimensionality reduction.

[0082] Performing tensor product fusion of cross-domain parameters in the group representation space to improve the coupling optimization effect of multi-dimensional parameters.

[0083] The optimization method breaks through the spatio-temporal frequency domain limitations of traditional test technologies, can capture and optimize transient signal distortions in the package test process in real time, and improves the early warning ability of potential failure modes.

[0084] An intelligent adaptive semiconductor package test optimization system, applied to semiconductor package test equipment, includes:

[0085] A quantization topological sensing network for collecting test parameters with high-dimensional and sub-microsecond-level responses;

[0086] A bionic meta-learning dynamic decision engine that optimizes test parameters based on the neuromorphic pulse decision network (SNN) and synchronously converges physical constraints;

[0087] A dynamic eigenmanifold space construction module for cross-domain parameter coupling optimization to realize a self-consistent optimized test system;

[0088] Among them, when the bionic element learning dynamic decision-making engine dynamically adjusts the test parameters, it further improves the test efficiency and accuracy through the following steps: Based on historical test data and actual feedback results, it optimizes the initial settings of test parameters using an enhanced learning mechanism to automatically identify and correct systematic errors in the test process; it introduces the deep reinforcement learning DRL algorithm to optimize the model parameters according to the loss function after each test, realizing real-time adjustment during the test process, ensuring that the test system can quickly adapt to different packaging structures and failure modes, and providing precise test optimization solutions; in the quantum tunneling effect dielectric sensing array of the quantum topology sensing network during the test, it realizes real-time dynamic monitoring of the dielectric constant by adaptively adjusting the tunneling current intensity and frequency; using the high sensitivity of the quantum tunneling current to the charge distribution in the dielectric layer, it provides real-time feedback on the microscopic electrical characteristics of the material layer, promptly captures the precursors of potential failure modes such as dielectric breakdown, provides high-precision test data, and provides a basis for further optimizing decisions. This solution abandons the traditional linear improvement path of sensor stack superposition algorithm optimization. Through the deep coupling architecture of quantum topology dynamic sensing and bionic element learning decision-making, by establishing the eigenmanifold space of test parameters, it realizes the self-consistent optimization of the high-dimensional dynamic test system, and solves the problem of the spatio-temporal frequency domain limitations in the multi-dimensional parameter coupling detection of traditional test technologies under the advanced packaging of semiconductor devices.

[0089] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including the element.

[0090] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent adaptive semiconductor packaging test optimization method, characterized in that, The method includes the following: S1: Construct a quantized topological sensing network, and collect test parameters through a dielectric sensing array based on the quantum tunneling effect and a topological photonic crystal waveguide field reconstruction technology; S2: Adopt a non-Hermitian exceptional point sensing mechanism, implant a PT-symmetric microcavity array to perform a hypersensitive response to interface defects, and capture nanoscale defects and environmental noise interference in real time; S3: Based on the collected test parameters, use a neuromorphic pulse decision network SNN for dynamic decision optimization, and utilize the spike-timing-dependent plasticity STDP mechanism to synchronously converge the test parameter optimization and physical constraints; the neuromorphic pulse decision network SNN optimizes the test parameters through the following steps: Sa: Encode 80-dimensional test parameters into neural pulse phases; Sb: Map the mechanical constraints of the package structure to synaptic weights; Sc: Convert the dynamic power consumption fluctuation into a spike firing rate modulation; The process of converting the dynamic power consumption fluctuation into a spike firing rate modulation in the step Sc includes: obtaining the power consumption data of the package test device, including dynamic power consumption fluctuation information; performing normalization processing on the power consumption fluctuation, and mapping the normalized dynamic power consumption fluctuation to a firing rate modulation signal in a neuromorphic pulse neural network; establishing a mapping relationship between the spike firing rate and the power consumption fluctuation through a non-linear function; S4: Construct a meta-learning-physics hybrid optimization framework to optimize decision latency through a dual-channel gradient manifold and adjust the test system parameters in real time; the meta-learning-physics hybrid optimization framework optimizes decision latency through the following process: establish a continuous implicit function f θ (x, t) of the parameter space through an implicit neural representation INR, and realize the covariant optimization of two types of gradients in the SO(3)×R n manifold space; S5: Adopt the construction of a dynamic eigenmanifold space, map high-dimensional test parameters to a compact Lie group representation space through a non-commutative Fourier transform, and fuse thermodynamic, electrical signal, and mechanical stress parameters; The construction of the dynamic eigenmanifold space is achieved through the following steps: Map high-dimensional test parameters to a compact Lie group representation space through a non-commutative Fourier transform; Perform a tensor product fusion operation on cross-domain parameters within the group representation space.

2. The intelligent adaptive semiconductor package test optimization method according to claim 1, wherein: The quantized topological sensing network includes: a nanoscale dielectric constant sensor network constructed by single-electron transistors SET, and sense the dielectric layer charge distribution in the 30 GHz frequency band through the quantum tunneling current. Arrange the SET array on the chip surface, and each SET unit serves as an independent sensing node; the sensor network forms a quantized topological structure through high-density interconnection, set the working voltage condition to enable the SET to trigger the quantum tunneling phenomenon at the 30 GHz frequency band; the tunneling current signals collected by each node of the sensor array are processed by a cryogenic amplification and noise filtering circuit to obtain high signal-to-noise ratio data, integrate a high-speed analog-to-digital converter ADC and a real-time data preprocessing module to complete the acquisition, format conversion, and preliminary feature extraction of the data of each sensing node; utilize a parallel processing architecture to synchronously input the signals from hundreds or even thousands of sensing nodes to the backend quantum topology data fusion module, use a non-linear mapping algorithm to map the original signals obtained from the SET sensors to a high-dimensional topological space, and obtain coupling parameters through topological invariant analysis; adopt a non-commutative Fourier transform and a Lie group representation method to transform thermodynamic, electrical signal, and mechanical stress parameters into the Lie group space to construct a dynamic parameter space; A test probe integrating topological optical metamaterials is used to invert the electromagnetic field distribution within a three-dimensional stacked structure through the change of the local density of optical states (LDOS). By using nanolithography and self-assembly techniques, a topological optical metamaterial structure is integrated at the end of the test probe. The structure consists of periodically arranged sub-wavelength units (meta-atoms). In the probe design, a modulation structure is embedded in the key area to change the arrangement pattern and geometric parameters of the metamaterial units. When the incident high-frequency signal irradiates the probe, the local density of states changes. A mapping model between the LDOS and the internal field distribution obtained based on electromagnetic numerical simulation and experimental calibration is established in advance. Using the inverse design algorithm, the measured LDOS change data is input into the preset non-linear mapping model. After iterative solution and global optimization, the three-dimensional electromagnetic field distribution data is obtained.

3. The intelligent adaptive semiconductor package test optimization method according to claim 2, wherein: The non-Hermitian exceptional point sensing mechanism includes: implanting a PT-symmetric microcavity array at the hybrid bonding interface. The microcavity array includes microcavity units with the same size. Each microcavity of the microcavity array has a symmetrically configured cavity shape and has non-linear response characteristics. Through the global optimization algorithm, the microcavity units are evenly distributed in space, and the spacing does not exceed 5 μm. The microcavity size ranges from dozens of nanometers to hundreds of nanometers.

4. An intelligent adaptive semiconductor packaging test optimization method according to claim 3, characterized in that: The process of optimizing the test parameters based on the neuromorphic spiking decision-making network (SNN) in step Sa includes: obtaining 80-dimensional test parameters, including multi-dimensional signals from semiconductor package test equipment, and normalizing each test parameter; mapping each normalized test parameter to a pulse phase value of 0 - 2π; using a spiking neural network encoded by pulse phases, where each phase value corresponds to the spike firing timing of a neuron, and the neurons are connected together through synapses to form a spiking neural network for synchronous learning and optimizing the parameters of the test system. Utilize the dynamic learning mechanism of the spiking neural network to optimize the test parameters, and based on the multi-dimensional coupling detection of the spiking neural timing, update the test strategy in real time; adopt an efficient multi-layer SNN architecture.

5. An intelligent adaptive semiconductor package test optimization method according to claim 4, characterized in that: The process of mapping the mechanical constraints of the package structure to synaptic weights in step Sb includes: obtaining the mechanical constraint parameters of the package structure, including mechanical stress, mechanical load, and contact pressure in semiconductor packaging, and normalizing the mechanical constraint parameters; mapping the mechanical constraint parameters to synaptic weights, mapping the stress value to the synaptic connection strength in the spiking neural network, and when setting the stress threshold, mapping the relationship between the stress value and the synaptic weight through a non-linear function. Through the spike-timing-dependent plasticity (STDP) mechanism of the neuromorphic spiking neural network (SNN), it is updated in real time during the test. As the mechanical constraint conditions change, the synaptic weights are adjusted accordingly; perform multi-dimensional processing of the mechanical constraint parameters through a multi-layer neural network. The SNN adopts a multi-layer neural network architecture, and the neurons in each layer jointly adjust the synaptic weights according to the input mechanical constraint parameters and physical quantities. The information processed by each layer provides feedback for the neurons in the lower layer.

6. An intelligent adaptive semiconductor packaging test optimization system, which is applied to semiconductor packaging test equipment and is used to implement an intelligent adaptive semiconductor packaging test optimization method as described in claim 1, characterized in that, Including: Quantized topological sensing network module: used to collect test parameters through the dielectric sensing array based on the quantum tunneling effect and the topological photonic crystal waveguide field reconstruction technology; Non-Hermitian exceptional point sensing module: Used for implanting PT-symmetric microcavity arrays, it responds hypersensitively to interface defects and captures nanoscale defects and environmental noise interference in real time; Bionic element learning dynamic decision engine module: Based on the collected test parameters, it uses a neuromorphic spiking decision network SNN for dynamic decision optimization, and utilizes the spike-timing-dependent plasticity STDP mechanism to synchronously converge the test parameter optimization and physical constraints; Meta-learning-physics hybrid optimization framework module: Optimizes decision delay through a two-channel gradient manifold and adjusts the test system parameters in real time; Dynamic eigenmanifold space construction module: Maps high-dimensional test parameters to a compact Lie group representation space through non-commutative Fourier transform, integrating thermodynamic, electrical signal, and mechanical stress parameters.

Images