Intelligent adaptive semiconductor packaging test optimization method

Abstract

The invention discloses an intelligent adaptive semiconductor packaging test optimization method, and relates to the technical field of semiconductor packaging test, and the method comprises the steps: S1, constructing a quantization topology sensing network, and collecting test parameters through a quantum tunneling effect dielectric sensing array and a topology photonic crystal waveguide field reconstruction technology; according to the intelligent self-adaptive semiconductor packaging test optimization method, self-consistent optimization of a high-dimensional dynamic test system is realized through a deep coupling architecture of quantum topology dynamic perception and bionic element learning decision and establishment of an intrinsic manifold space of test parameters, and the problem that the test efficiency is high under advanced packaging of semiconductor devices is solved. A traditional test technology has the problem of space-time frequency domain limitation in multi-dimensional parameter coupling detection.

Description

Technical Field

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

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

[0003] 1. Technical issues to be resolved

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

[0005] (II) Technical solution

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

[0007] S1: Build a quantized topological sensing network to collect high-dimensional, sub-microsecond response test parameters through quantum tunneling effect dielectric sensing array and topological photonic crystal waveguide field reconstruction technology;

[0008] S2: Using a non-Hermitian singular point sensing mechanism, a PT-symmetric microcavity array is implanted to respond ultra-sensitively to interface defects and capture nanoscale defects and environmental noise interference in real time;

[0009] S3: Based on the collected test parameters, the neuromorphic spike decision network SNN is used for dynamic decision optimization, and the pulse timing dependent plasticity STDP mechanism is used to synchronize the convergence of test parameter optimization and physical constraints;

[0010] S4: Build a meta-learning-physics hybrid optimization framework, optimize decision delays through dual-channel gradient manifolds, and adjust test system parameters in real time to break through the time, space, and frequency domain limitations of traditional test technology and improve test accuracy and efficiency;

[0011] S5: It adopts dynamic eigenmanifold space construction, maps high-dimensional test parameters to compact Lie group representation space through non-commutative Fourier transform, integrates thermodynamics, electrical signals and mechanical stress parameters, and realizes cross-domain parameter coupling optimization.

[0012] Preferably, the quantized topological sensor network includes: a nanoscale dielectric constant sensor network constructed by a single electron transistor SET, with a sensitivity of 1e -5 C / cm 2 The charge distribution of the dielectric layer in the 30GHz frequency band is sensed through quantum tunneling current, and the response time reaches sub-microsecond level (Δt<0.2μs). To build a sensor network, a single electron transistor SET is used as the basic detection unit. The SET device uses the quantum tunneling effect to achieve precise control of the transmission of a single electron. By achieving extremely low noise interference at the nanoscale, the device has extremely high sensitivity to small changes in the dielectric constant. In terms of specific implementation: a large number of SET arrays prepared by micro-nano processing technology are arranged on the chip surface, and each SET unit serves as an independent sensor node. The sensor network is connected through a high-density The SET is interconnected to form a quantized topological structure, which can realize continuous monitoring in space and use topology optimization algorithms to ensure high fidelity of signal transmission between nodes, avoiding information distortion caused by physical contact or transmission loss in traditional sensor networks. In a high-frequency test environment, the SET quantum tunneling effect is used to realize real-time monitoring of the charge distribution in the dielectric layer. The process mainly includes: setting the working voltage conditions so that the SET can effectively trigger the quantum tunneling phenomenon in the 30GHz frequency band. Since the quantum tunneling current is extremely sensitive to the changes in the local charge distribution in the dielectric layer, it can capture the charge distribution as small as 1e -5 C / cm 2The tunneling current signal collected by each node of the sensor array is 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 when measuring in the millimeter wave band due to limited frequency band. A specially designed high-speed readout system is used to control the overall response time of the SET sensor network to the sub-microsecond level (Δt<0.2μs). The implementation method includes: integrating a high-speed analog-to-digital converter ADC and a real-time data preprocessing module to ensure that the data collection, format conversion and preliminary feature extraction of each sensor node are completed in a very short time; using a parallel processing architecture, the signals from hundreds or even thousands of sensor nodes are synchronously input to the sensor network. The signals are fed into the back-end quantum topological data fusion module and transmitted through high-speed optical fiber or millimeter wave channels to achieve seamless connection from the sensor end to the data processing center. The original signals obtained from the SET sensor are mapped to a high-dimensional topological space using a nonlinear mapping algorithm, and stable coupling parameters are obtained through topological invariant analysis. In the mapping process, non-commutative Fourier transform and Lie group representation methods are used to uniformly transform thermodynamics, electrical signals, and mechanical stress parameters into a compact Lie group space to achieve tensor product fusion of cross-domain parameters and avoid information loss during dimensionality reduction. Finally, under this topological structure, a self-consistent dynamic parameter space is constructed to provide an accurate, continuous, and high-dimensional data foundation for subsequent real-time optimization using a bionic meta-learning decision engine.

[0013] 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 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 high frequency When the signal (30GHz) 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 (for example, 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.

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

[0015] The PT symmetric microcavity array is implanted in the hybrid bonding interface. Through the ultra-sensitive response characteristics of the singular points to microcracks, the sensitivity is increased by 3 orders of magnitude compared with traditional acoustic detection. It can detect 10nm-level interface defects online and resist the interference of packaging stress and environmental noise.

[0016] The construction process of PT-symmetric microcavity array: the microcavity array includes a plurality of nano-scale microcavity units with the same size and optimized arrangement. Each microcavity unit is made of a special material with PT symmetry. The selection of the material is based on the ability to achieve extremely high sensitivity to changes in electromagnetic fields, stresses and thermal distributions. Each microcavity used in the microcavity array has a symmetrically configured cavity shape and has nonlinear response characteristics. When microcracks are caused by changes in local stress, temperature or electromagnetic fields, an amplification effect can be generated within the resonant 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 5μm to ensure The overall array has high-resolution monitoring capabilities for microcracks; the microcavity size of the array design ranges from tens of nanometers to hundreds of nanometers, which can match the microcracks, interface defects or stress concentration areas in semiconductor packaging materials, and provide accurate capture of 10nm-level defects; the material used in the microcavity array is a highly sensitive PT-symmetric material, which can produce significant nonlinear responses when facing microcracks or interface defects, especially at the singular points, and the response to cracks is extremely sensitive, and its sensitivity is improved by at least three orders of magnitude compared with traditional acoustic detection technology; the material selection of the microcavity is based on quantum optics and nanomechanics, which can enhance the amplification effect of small changes in electromagnetic fields or stress fields caused by cracks;

[0017] The microcavity unit can produce a strong resonance effect within a specific frequency range through internal structure optimization. When a small disturbance caused by a local crack or defect occurs, the microcavity array will show a nonlinear amplification effect, which greatly enhances the response to the defect. The design can effectively capture the frequency deviation caused by microcracks and realize accurate 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 that the electromagnetic field changes occurring during the packaging process can be detected in real time.

[0018] Preferably, the neuromorphic spike decision network SNN optimizes the test parameters by the following steps:

[0019] Sa: Encode the 80-dimensional test parameters as neural spike phases (0-2π);

[0020] Sb: Mapping package structural mechanical constraints to synaptic weights (σ max ≤180MPa corresponds to weight threshold);

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

[0022] Preferably, the process of optimizing the test parameters based on the neuromorphic pulse decision network SNN in step Sa includes: obtaining 80-dimensional test parameters, which include multi-dimensional signals from semiconductor packaging test equipment, and these signals involve multiple physical quantities such as electrical properties, thermal properties, mechanical stress, interface defects, etc.; normalizing each test parameter, processing the original 80-dimensional test parameters through a standardized method, so that they are within the same range, thereby avoiding the difference between different parameter magnitudes affecting the neural pulse phase encoding process; mapping each normalized test parameter to a pulse phase value (0-2π), and mapping each test parameter to a pulse phase value (0-2π) through a nonlinear function. The pulse phase is in the range of 0 to 2π, so that the phase value of each pulse can reflect the intensity and characteristics of the corresponding test parameters. The mapping function is designed using the pulse timing dependent plasticity (STDP) mechanism commonly used in neuromorphic computing to ensure the accurate mapping of the phase value; a neural pulse network with pulse phase encoding is used to convert the 80-dimensional test parameters into phase pulses and input them into the neuromorphic spike neural network. Each phase value corresponds to the pulse emission timing of a neuron. These neurons are connected together through synapses to form a spike neural network for synchronous learning and optimization of the parameters of the test system. The encoding method of the neural pulse phase value makes the input of each neuron The output can accurately reflect the changes in input parameters; the dynamic learning mechanism of the spiking neural network is used to optimize the test parameters. The synaptic weights in the neuromorphic spiking neural network are dynamically adjusted through the pulse timing dependent plasticity STDP mechanism. The timing and phase value of each pulse emission are transmitted through the pulse propagation in the network, forming a feedback loop inside the network to optimize the mapping relationship between test parameters and physical quantities. This dynamic optimization can adapt to the real-time changing packaging environment and different test requirements; based on the multi-dimensional coupling detection of neural pulse timing, through pulse phase encoding, the emission timing of each neuron will be adjusted according to the phase of the input signal to form a high-dimensional pulse timing model. Through this timing mode, SNN can achieve effective coupling and interaction between multiple physical dimensions, break through the single parameter monitoring in traditional methods, and overcome the time, space and frequency domain limitations of multi-dimensional parameter coupling detection; update the test strategy in real time. Through the adaptive optimization mechanism of SNN, the system can update the test parameters and optimize the test strategy in real time during the packaging test process; after each test is completed, the pulse neural network will readjust the weight according to the feedback signal to improve the test accuracy and reliability; especially in complex packaging structures, SNN can improve the ability to capture multi-dimensional effects such as microcracks, stress, and heat distribution by learning the coupling relationship of various physical effects in the package in real time;Adopting 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 the 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 mechanical constraint parameters of the packaging structure, which parameters include multiple physical quantities such as mechanical stress, mechanical load, and contact pressure in the semiconductor package, and the mechanical constraints are determined by factors such as the material, size, and shape of the packaging structure; normalizing the mechanical constraint parameters, and converting the original mechanical constraint parameters (such as the maximum stress σ max The normalization method is used to process it so that it can be jointly analyzed with other test parameters (such as electrical properties, thermal properties, etc.) on a unified scale; the mechanical constraint parameters are mapped to synaptic weights, and the normalized mechanical constraints (such as the maximum stress σ max ≤180MPa) is mapped to the synaptic weight. Specifically, the stress value is mapped to the synaptic connection strength in the neural spike network, and the stress threshold (such as σ max When the stress value is less than or equal to 180MPa), the relationship between the stress value and the synaptic weight is mapped through a nonlinear function to realize the dynamic influence of stress in the network; the dynamic update of the synaptic weight is realized through the synaptic 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 constraints change, the synaptic weight will also be adjusted accordingly, so that the neural network can accurately reflect the influence of mechanical constraints on the packaging structure;

[0024] Synaptic weights and test parameters are optimized together. The synaptic weights in the neural pulse network not only rely on mechanical constraints, but also are optimized together with other physical test parameters (such as electrical properties, thermal effects, etc.) to ensure that the coupling relationship between multi-dimensional parameters of the test system can be accurately captured. Multi-dimensional processing of mechanical constraint parameters is performed through a multi-layer neural network. The SNN adopts a multi-layer neural network architecture. The neurons in each layer 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 to the neurons in the lower layer to achieve the coordinated optimization of multi-dimensional mechanical constraints and physical quantities. The test strategy is adjusted based on the feedback mechanism. The feedback mechanism in the neural pulse network ensures that in the actual test process, the influence of mechanical constraints on the test parameters can be transmitted through the network layer by layer, and the test parameters can be adjusted in real time to 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 the dynamic power consumption fluctuation into pulse firing rate modulation in step Sc includes: obtaining power consumption data of the packaging test equipment, including dynamic power consumption fluctuation information, which reflects the energy consumption and heat dissipation characteristics of the packaging structure, materials and devices during the test, and the dynamic power consumption fluctuation is closely related to the changes of various physical quantities (such as current, voltage, temperature, etc.) in the test; normalizing the power consumption fluctuation, and uniformly scaling the dynamic power consumption fluctuation signal with other test signals (such as electrical properties, mechanical stress, etc.) to ensure that the power consumption signal can work together with other physical quantities and be integrated into the subsequent neural pulse encoding process; converting the normalized dynamic power consumption fluctuation into pulse firing rate modulation, and the process maps the dynamic power consumption fluctuation into a firing rate modulation signal in the neuromorphic pulse neural network. In this process, the intensity of the power consumption fluctuation is converted into the firing frequency (or pulse firing rate) of the pulse, that is, the amplitude of the power consumption fluctuation directly affects the frequency of the pulse firing, and a higher power consumption fluctuation will lead to more frequent pulse firing; establishing a mapping relationship between the pulse firing rate and the power consumption fluctuation through a nonlinear function to ensure that the change of the power consumption fluctuation can be effectively transmitted to the pulse firing process of the neural network; the mapping relationship can be obtained through experimental learning, so that the characteristics of the power consumption fluctuation can be fully reflected and optimized in the network.

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

[0027] Preferably, the dynamic eigenmanifold space construction is achieved by the following steps:

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

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

[0030] The optimization method breaks through the time, space and frequency domain limitations of traditional testing technology, can capture and optimize transient signal distortion during packaging testing in real time, and improve the early warning capability of potential failure modes.

[0031] An intelligent adaptive semiconductor packaging test optimization system, applied to semiconductor packaging test equipment, comprising:

[0032] Quantum topological sensor networks for collecting high-dimensional, sub-microsecond response test parameters;

[0033] Bionic meta-learning dynamic decision engine, based on neuromorphic spike decision network (SNN) to optimize test parameters and simultaneously converge physical constraints;

[0034] Dynamic eigenmanifold space building module for cross-domain parameter coupling optimization to achieve self-consistent optimization test system;

[0035] Among them, the bionic meta-learning dynamic decision engine further improves test efficiency and accuracy through the following steps when dynamically adjusting test parameters: based on historical test data and actual feedback results, the reinforcement learning mechanism is used to optimize the initial settings of test parameters, and the systematic errors in the test process are automatically identified and corrected; the deep reinforcement learning DRL algorithm is introduced to optimize the model parameters according to the loss function after each test, and the real-time adjustment during the test process is realized to ensure that the test system can quickly adapt to different packaging structures and failure modes, and provide accurate test optimization solutions;

[0036] During the test process, the quantum tunneling effect dielectric sensing array in the quantized topological sensing network realizes real-time dynamic monitoring of the dielectric constant by adaptively adjusting the tunneling current intensity and frequency. By utilizing the high sensitivity of the quantum tunneling current to the charge distribution of the dielectric layer, the microscopic electrical properties of the material layer are fed back in real time, and the precursors of potential failure modes such as dielectric breakdown are captured in time, providing high-precision test data and providing a basis for further optimized decision-making.

[0037] (III) Beneficial effects

[0038] The present invention provides an intelligent adaptive semiconductor packaging test optimization method, which has the following beneficial effects:

[0039] (I) This intelligent adaptive semiconductor packaging test optimization method abandons the linear improvement path of traditional sensor stacking and algorithm optimization. Through the deep coupling architecture of quantum topological dynamic perception and bionic meta-learning decision-making, and by establishing the intrinsic manifold space of test parameters, it realizes the self-consistent optimization of the high-dimensional dynamic test system, and solves the problem of time-space frequency domain limitations of traditional test technology in multi-dimensional parameter coupling detection under advanced packaging of semiconductor devices.

[0040] (ii) This intelligent adaptive semiconductor packaging test optimization method converts dynamic power consumption fluctuations into pulse emission rate modulation. This technical solution innovatively embeds power consumption fluctuation information into the neural pulse neural network, breaking through the time, space and frequency domain limitations of traditional testing methods and realizing multi-dimensional parameter coupling detection. This process adjusts the pulse emission rate in real time, so that the packaging test can dynamically feedback the impact of power consumption fluctuations on various physical effects, optimize test strategies and parameters, and improve the real-time, accuracy and adaptability of the test. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0042] Figure 2 It is a flow chart of the communication protocol of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 and Figure 2 The present invention provides a technical solution: an intelligent adaptive semiconductor packaging test optimization method, the method comprising:

[0045] S1: Build a quantized topological sensing network to collect high-dimensional, sub-microsecond response test parameters through quantum tunneling effect dielectric sensing array and topological photonic crystal waveguide field reconstruction technology;

[0046] S2: Using a non-Hermitian singular point sensing mechanism, a PT-symmetric microcavity array is implanted to respond ultra-sensitively to interface defects and capture nanoscale defects and environmental noise interference in real time;

[0047] S3: Based on the collected test parameters, the neuromorphic spike decision network SNN is used for dynamic decision optimization, and the pulse timing dependent plasticity STDP mechanism is used to synchronize the convergence of test parameter optimization and physical constraints;

[0048] S4: Build a meta-learning-physics hybrid optimization framework, optimize decision delays through dual-channel gradient manifolds, and adjust test system parameters in real time to break through the time, space, and frequency domain limitations of traditional test technology and improve test accuracy and efficiency;

[0049] S5: It adopts dynamic eigenmanifold space construction, maps high-dimensional test parameters to compact Lie group representation space through non-commutative Fourier transform, integrates thermodynamics, electrical signals and mechanical stress parameters, and realizes cross-domain parameter coupling optimization.

[0050] The quantized topological sensing network includes: a nanoscale dielectric constant sensor network constructed by single electron transistor SET, with a sensitivity of 1e -5 C / cm 2 The charge distribution of the dielectric layer in the 30GHz frequency band is sensed through quantum tunneling current, and the response time reaches sub-microsecond level (Δt<0.2μs). To build a sensor network, a single electron transistor SET is used as the basic detection unit. The SET device uses the quantum tunneling effect to achieve precise control of the transmission of a single electron. By achieving extremely low noise interference at the nanoscale, the device has extremely high sensitivity to small changes in the dielectric constant. In terms of specific implementation: a large number of SET arrays prepared by micro-nano processing technology are arranged on the chip surface, and each SET unit serves as an independent sensor node. The sensor network is connected through a high-density The SET is interconnected to form a quantized topological structure, which can realize continuous monitoring in space and use topology optimization algorithms to ensure high fidelity of signal transmission between nodes, avoiding information distortion caused by physical contact or transmission loss in traditional sensor networks. In a high-frequency test environment, the SET quantum tunneling effect is used to realize real-time monitoring of the charge distribution in the dielectric layer. The process mainly includes: setting the working voltage conditions so that the SET can effectively trigger the quantum tunneling phenomenon in the 30GHz frequency band. Since the quantum tunneling current is extremely sensitive to the changes in the local charge distribution in the dielectric layer, it can capture the charge distribution as small as 1e -5 C / cm 2The tunneling current signal collected by each node of the sensor array is 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 when measuring in the millimeter wave band due to limited frequency band; a specially designed high-speed readout system is used to control the overall response time of the SET sensor network to sub-microsecond level (Δt<0.2μs), and its implementation methods include: integrating a high-speed analog-to-digital converter ADC and a real-time data preprocessing module to ensure that the data of each sensor node is collected, format converted and initially extracted in a very short time; using a parallel processing architecture, the signals from hundreds or even thousands of sensor nodes are synchronously input to the back-end quantum topological data fusion module, and transmitted through high-speed optical fiber or millimeter wave channels to achieve seamless transmission from the sensor end to the data processing center. connection; Traditional testing technology often uses discrete sampling methods in multi-dimensional parameter detection, which is prone to losing transient signal information. However, this scheme uses quantum tunneling current detection technology to achieve continuous and real-time monitoring of dielectric layer charge distribution, and then constructs the intrinsic manifold of the parameters. Its core lies in: using a nonlinear mapping algorithm to map the original signal obtained from the SET sensor to a high-dimensional topological space, and obtaining stable coupling parameters through topological invariant analysis; in the mapping process, non-commutative Fourier transform and Lie group representation methods are used to uniformly transform thermodynamics, electrical signals and mechanical stress parameters into a compact Lie group space, realizing tensor product fusion of cross-domain parameters and avoiding information loss during dimensionality reduction; finally, under this topological structure, a self-consistent dynamic parameter space is constructed, providing an accurate, continuous and high-dimensional data foundation for subsequent real-time optimization using a bionic meta-learning decision engine.

[0051] The use of SET and quantum tunneling current detection technology has fundamentally improved the sensitivity and bandwidth of dielectric constant measurement, allowing it to maintain sub-microsecond response at the 30GHz frequency band, breaking through the limitations of physical dimensions; a continuous and distributed detection system is constructed through a quantized topological sensor network, which does not rely on discrete sampling, thereby effectively capturing transient signals and multi-dimensional parameter coupling phenomena and realizing continuous dynamic detection; the use of topological data fusion and nonlinear mapping technology avoids the cross-scale and cross-frequency band data mismatch problems that may occur in conventional multi-sensor data splicing, provides a new solution for multi-dimensional coupling detection under complex packaging structures, and avoids the bottleneck of conventional sensor stacking; by integrating quantum physics effects, nanoscale sensing technology and topological data processing methods, a new intelligent adaptive test optimization system is constructed, which effectively breaks through the time, space and frequency domain limitations of traditional testing technology under high-density and complex packaging, and improves the accuracy of packaging testing and real-time early warning capabilities.

[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-discrete data splicing" solution, but instead realizes the continuous inversion of the electromagnetic field distribution directly at the physical level by integrating topological optical metamaterials, completely avoiding the limitations of diffraction limit and cross-scale matching; by establishing a high-dimensional mapping relationship between LDOS and electromagnetic field distribution, combined with nonlinear optimization and topological constraints, the coupled detection of multi-dimensional parameters is realized, and the mutual influence of multiple physical effects such as thermal, electrical, and mechanical in the stacked structure can be inverted simultaneously, providing a self-consistent dynamic test optimization platform; in the 30GHz frequency band, not only a high spatial resolution of 200μm is achieved, but also through fast dynamic focusing technology and real-time data processing, the response speed and signal-to-noise ratio of the system are greatly improved, providing strong technical support for advanced packaging testing of semiconductor devices.

[0056] Non-Hermitian singularity sensing mechanisms include:

[0057] A PT symmetrical microcavity array is implanted at the hybrid bonding interface. The ultra-sensitive response characteristics to microcracks at the singular points are used to improve the sensitivity compared with traditional acoustic detection. It can detect 10nm-level interface defects online and resist the interference of packaging stress and environmental noise.

[0058] The construction process of PT-symmetric microcavity array: The microcavity array includes multiple nano-scale microcavity units with the same size and optimized arrangement. Each microcavity unit is made of special materials with PT symmetry. The selection of materials is based on the ability to achieve extremely high sensitivity to changes in electromagnetic fields, stress and thermal distribution. Each microcavity used in the microcavity array has a symmetrically configured cavity shape and has nonlinear response characteristics. When microcracks are caused by changes in local stress, temperature or electromagnetic fields, an amplification effect can be generated within the resonant 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 5μm to ensure the overall The body array has high-resolution monitoring capability for microcracks; the size of the microcavity designed in the array ranges from tens of nanometers to hundreds of nanometers, which can match the microcracks, interface defects or stress concentration areas in the semiconductor packaging materials, and provide accurate capture of 10nm-level defects; the material used in the microcavity array is a highly sensitive PT-symmetric material, which can produce significant nonlinear responses when facing microcracks or interface defects, especially at singular points, and the response to cracks reaches extremely high sensitivity, and its sensitivity is improved by at least three orders of magnitude compared with traditional acoustic detection technology; the material selection of the microcavity is based on quantum optics and nanomechanics, which can enhance the amplification effect of small changes in the electromagnetic field or stress field caused by cracks.

[0059] The microcavity unit can produce a strong resonance effect within a specific frequency range through internal structural optimization. When a small disturbance caused by a local crack or defect occurs, the microcavity array will show a nonlinear amplification effect, which greatly enhances the response to the defect. This design can effectively capture the frequency shift caused by microcracks and achieve accurate monitoring of interface defects. The resonant frequency range of the microcavity array covers a high frequency band from several GHz to 30 GHz to ensure that the electromagnetic field changes occurring during the packaging process can be detected in real time.

[0060] The output signal of the microcavity array is collected in real time by high-precision sensors and analog-to-digital conversion systems, and input into the data processing module. The response signal of the microcavity array is accurately analyzed by nonlinear optimization algorithms. The location information, size and type of defects are output in real time in combination with historical test data and environmental changes. The data processing module can identify the evolution mode of microcracks in the microcavity array signal and adjust the detection sensitivity through feedback control systems to adapt to changes in different packaging environments.

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

[0062] The size of the microcavity array is adjustable according to the packaging requirements, and the modular design allows the scale of the array to be flexibly expanded according to different testing requirements; the size of each microcavity array module is adjustable to meet the testing requirements of semiconductor devices with different packaging 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 packaging process in real time, and adjust the test parameters through a feedback mechanism to optimize the results of the semiconductor packaging test, ensuring the high accuracy and high reliability of the final test results.

[0063] The design of the microcavity array successfully solves the time-space frequency domain limitation problem encountered by traditional test methods in packaging by introducing PT symmetry and nonlinear resonance effect. Traditional technologies often rely on discrete test methods, which makes it impossible to capture the tiny changes of defects such as microcracks in packaging materials in real time. The design of the microcavity array can monitor the stress and electromagnetic field distribution in the packaging structure in real time, significantly improving the spatial resolution and frequency response range of the detection, breaking through the limitations of traditional technologies.

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

[0065] Through the nonlinear response mechanism, the microcavity array can realize multi-dimensional parameter coupling detection of electromagnetic fields, stress and thermal changes; compared with the traditional single physical quantity monitoring method, the microcavity array can capture and analyze multiple physical effects at the same time, greatly improving the comprehensiveness and accuracy of packaging testing.

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

[0067] Sa: Encode the 80-dimensional test parameters as neural spike phases (0-2π);

[0068] Sb: Mapping package structural mechanical constraints to synaptic weights (σ max ≤180MPa corresponds to weight threshold);

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

[0070] The process of optimizing the test parameters based on the neuromorphic pulse decision network SNN in step Sa includes: obtaining 80-dimensional test parameters, which include multi-dimensional signals from semiconductor packaging test equipment, and these signals involve multiple physical quantities such as electrical properties, thermal properties, mechanical stress, interface defects, etc.; normalizing each test parameter, processing the original 80-dimensional test parameters through a standardized method to make them within the same range, thereby avoiding the difference between different parameter magnitudes affecting the neural pulse phase encoding process; mapping each normalized test parameter to a pulse phase value (0-2π), and each test parameter is mapped to a pulse phase through a nonlinear function, The pulse phase ranges from 0 to 2π, so that the phase value of each pulse can reflect the intensity and characteristics of the corresponding test parameters. The mapping function is designed using the pulse timing-dependent plasticity STDP mechanism commonly used in neuromorphic computing to ensure accurate mapping of the phase value; a neural pulse network with pulse phase encoding is used to convert the 80-dimensional test parameters into phase pulses and input them into the neuromorphic spike neural network. Each phase value corresponds to the pulse emission timing of a neuron. These neurons are connected together through synapses to form a spike neural network for synchronous learning and optimization of the test system parameters. The encoding method of the neural pulse phase value enables the output of each neuron to be accurate. Reflect the changes in input parameters; use the dynamic learning mechanism of the spiking neural network to optimize the test parameters. The synaptic weights in the neuromorphic spiking neural network are dynamically adjusted through the pulse timing-dependent plasticity STDP mechanism. The timing and phase value of each pulse emission are transmitted through the pulse propagation in the network, forming a feedback loop within the network to optimize the mapping relationship between test parameters and physical quantities. This dynamic optimization can adapt to real-time changing packaging environments and different test requirements; based on the multi-dimensional coupling detection of neural pulse timing, through pulse phase encoding, the emission 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 mode, SNN can achieve effective coupling and interaction between multiple physical dimensions, break through the single parameter monitoring in traditional methods, and overcome the time, space and frequency domain limitations of multi-dimensional parameter coupling detection; update the test strategy in real time. Through the adaptive optimization mechanism of SNN, the system can update the test parameters and optimize the test strategy in real time during the packaging test process; after each test is completed, the pulse neural network will readjust the weight according to the feedback signal to improve the test accuracy and reliability; especially in complex packaging structures, SNN can improve the ability to capture multi-dimensional effects such as microcracks, stress, and heat distribution by learning the coupling relationship of various physical effects in the package in real time;Adopting an efficient multi-layer SNN architecture, SNN performs hierarchical processing and optimization through a multi-layer neural network structure. Each layer of the neural network processes the input signal according to different dimensions, and finally outputs the optimized test parameters. This hierarchical structure effectively enhances SNN's ability to process 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 have been broken. Traditional testing technologies usually measure each physical quantity as a separate parameter and cannot handle the complex coupling between different parameters. By converting each test parameter into a pulse phase (0-2π), SNN can integrate 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, thereby realizing multi-dimensional parameter detection.

[0072] By encoding 80-dimensional test parameters into pulse phases and utilizing the timing learning mechanism of the neuromorphic spiking neural network (SNN), this technical solution breaks through the spatiotemporal and frequency domain limitations of traditional testing technology in multi-dimensional parameter coupling detection, and provides an efficient testing method that can optimize 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 system's real-time response capability and adaptive optimization capability, ensuring high-precision and high-efficiency semiconductor packaging testing; it achieves synchronous convergence of test parameter optimization and physical constraints at the hardware level, and improves the dynamic response capability of the test system.

[0073] 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, which include multiple physical quantities such as mechanical stress, mechanical load, and contact pressure in the semiconductor package. The mechanical constraints are determined by factors such as the material, size, and shape of the package structure; normalizing the mechanical constraint parameters, and converting the original mechanical constraint parameters, such as the maximum stress σ max The normalization method is used to process it so that it can be jointly analyzed with the electrical and thermal performance test parameters on a unified scale; the mechanical constraint parameters are mapped to synaptic weights, and the normalized mechanical constraints, such as the maximum stress σ max ≤180MPa, is mapped to the synaptic weight. Specifically, the stress value is mapped to the synaptic connection strength in the neural pulse network, and the stress threshold, σ maxWhen the stress value is ≤180MPa, the relationship between the stress value and the synaptic weight is mapped through a nonlinear function to realize the dynamic influence of stress in the network; the dynamic update of the synaptic weight is realized through the synaptic timing-dependent plasticity STDP mechanism of the neuromorphic spiking neural network SNN. The synaptic weight will be updated in real time during the test process. As the mechanical constraints change, the synaptic weight will also be adjusted accordingly, so that the neural network can accurately reflect the influence of mechanical constraints on the packaging structure;

[0074] Synaptic weights and test parameters are coordinated and optimized. The synaptic weights in the neural pulse network not only rely on mechanical constraints, but also are jointly optimized with physical test parameters such as electrical performance and thermal effects 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 is performed through a multi-layer neural network. SNN adopts a multi-layer neural network architecture. The neurons in each layer adjust 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 to the neurons in the lower layer to achieve the coordinated optimization of multi-dimensional mechanical constraints and physical quantities. The test strategy is adjusted based on the feedback mechanism. The feedback mechanism in the neural pulse network ensures that in the actual test process, the influence of mechanical constraints on test parameters can be transmitted layer by layer through the network, and the test parameters can be adjusted in real time to 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.

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

[0076] The process of converting dynamic power consumption fluctuation into pulse firing rate modulation in step Sc includes: obtaining power consumption data of packaging test equipment, including dynamic power consumption fluctuation information, which reflects the energy consumption and heat dissipation characteristics of packaging structure, materials and devices during the test, and the dynamic power consumption fluctuation is closely related to the changes of various physical quantities in the test: current, voltage, and temperature; normalizing the power consumption fluctuation, and uniformly scaling the dynamic power consumption fluctuation signal with the electrical performance and mechanical stress test signal to ensure that the power consumption signal can work together with other physical quantities and be integrated into the subsequent neural pulse encoding process; converting the normalized dynamic power consumption fluctuation into pulse firing rate modulation, and the process maps the dynamic power consumption fluctuation into a firing rate modulation signal in a neuromorphic pulse neural network. In this process, the intensity of the power consumption fluctuation is converted into the firing frequency of the pulse, that is, the amplitude of the power consumption fluctuation directly affects the frequency of the pulse firing, and a higher power consumption fluctuation will lead to more frequent pulse firing; establishing a mapping relationship between the pulse firing rate and the power consumption fluctuation through a nonlinear function to ensure that the change of the power consumption fluctuation can be effectively transmitted to the pulse firing process of the neural network; the mapping relationship can be obtained through experimental learning, so that the characteristics of the power consumption fluctuation can be fully reflected and optimized in the network;

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

[0078] During the packaging test process, the test strategy is optimized based on the feedback mechanism of the pulse firing rate. The pulse firing rate modulation is not only used to optimize a single test parameter, but also to coordinate different physical quantities, namely the mutual influence between electrical, thermodynamic and mechanical parameters. The feedback mechanism can dynamically adjust various parameters in the package according to power consumption fluctuations, and realize efficient detection of multi-dimensional parameter coupling. By converting dynamic power consumption fluctuations into pulse firing rate modulation, this technical solution innovatively embeds power consumption fluctuation information into the neural pulse neural network, breaking through the time, space and frequency domain limitations of traditional testing methods and realizing multi-dimensional parameter coupling detection. This process adjusts the pulse firing rate in real time, so that the packaging test can dynamically feedback the impact of power consumption fluctuations on various physical effects, optimize test strategies and parameters, and improve the real-time, accuracy and adaptability of the test.

[0079] The meta-learning-physics hybrid optimization framework performs decision delay optimization through the following process: a continuous implicit function f in the parameter space is established through the implicit neural representation INR θ (x, t), and through the Lie group differential geometry method, in SO(3)×R n Covariant optimization of two types of gradients is achieved in the manifold space; based on the traditional optimization method, the decision delay is reduced from 300ms to 8ms, significantly improving the test efficiency.

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

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

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

[0083] The optimization method breaks through the time, space and frequency domain limitations of traditional testing technology, and can capture and optimize transient signal distortion during packaging testing in real time, thereby improving the early warning capability of potential failure modes.

[0084] An intelligent adaptive semiconductor packaging test optimization system, applied to semiconductor packaging test equipment, comprising:

[0085] Quantum topological sensor networks for collecting high-dimensional, sub-microsecond response test parameters;

[0086] Bionic meta-learning dynamic decision engine, based on neuromorphic spike decision network (SNN) to optimize test parameters and simultaneously converge physical constraints;

[0087] Dynamic eigenmanifold space building module for cross-domain parameter coupling optimization to achieve self-consistent optimization test system;

[0088] Among them, the bionic meta-learning dynamic decision-making engine further improves the test efficiency and accuracy through the following steps when dynamically adjusting the test parameters: based on historical test data and actual feedback results, the reinforcement learning mechanism is used to optimize the initial settings of the test parameters, and the systematic errors in the test process are automatically identified and corrected; the deep reinforcement learning DRL algorithm is introduced to optimize the model parameters according to the loss function after each test, so as to realize real-time adjustment during the test process, ensure that the test system can quickly adapt to different packaging structures and failure modes, and provide accurate test optimization solutions; the quantum tunneling effect dielectric sensing array in the quantized topological sensing network realizes real-time dynamic monitoring of the dielectric constant by adaptively adjusting the tunneling current intensity and frequency during the test process; the high sensitivity of the quantum tunneling current to the charge distribution of the dielectric layer is utilized to feedback the microscopic electrical characteristics of the material layer in real time, so as to timely capture the precursors of potential failure modes such as dielectric breakdown, provide high-precision test data, and provide a basis for further optimization of decisions. This solution abandons the linear improvement path of traditional sensor stacking and algorithm optimization. Through the deep coupling architecture of quantum topological dynamic perception and bionic meta-learning decision-making, and by establishing the intrinsic manifold space of test parameters, it realizes self-consistent optimization of high-dimensional dynamic test systems, and solves the problem of time, space and frequency domain limitations of traditional testing technology in multi-dimensional parameter coupling detection under advanced packaging of semiconductor devices.

[0089] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0090] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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 comprises: S1: Build a quantized topological sensing network and collect test parameters through quantum tunneling effect dielectric sensing array and topological photonic crystal waveguide field reconstruction technology; S2: Using a non-Hermitian singular point sensing mechanism, a PT-symmetric microcavity array is implanted to respond ultra-sensitively to interface defects and capture nanoscale defects and environmental noise interference in real time; S3: Based on the collected test parameters, the neuromorphic spike decision network SNN is used for dynamic decision optimization, and the pulse timing dependent plasticity STDP mechanism is used to synchronize the convergence of test parameter optimization and physical constraints; S4: Build a meta-learning-physics hybrid optimization framework to optimize decision delays through dual-channel gradient manifolds and adjust test system parameters in real time; S5: A dynamic eigenmanifold space is used to construct the high-dimensional test parameters, which are mapped to a compact Lie group representation space through non-commutative Fourier transform, integrating thermodynamics, electrical signals, and mechanical stress parameters.

2. The intelligent adaptive semiconductor packaging test optimization method according to claim 1, characterized in that: The quantized topological sensor network includes: a nanoscale dielectric constant sensor network constructed by single electron transistors SET, and the charge distribution of the dielectric layer in the 30GHz frequency band is sensed by quantum tunneling current, and the SET array is arranged on the chip surface, and each SET unit is used as an independent sensor node; the sensor network forms a quantized topological structure through high-density interconnection, and the working voltage conditions are set so that the SET triggers the quantum tunneling phenomenon in the 30GHz frequency band; the tunneling current signal collected by each node of the sensor array is processed by a low-temperature amplification and noise filtering circuit to obtain high signal-to-noise ratio data, and a high-speed analog-to-digital converter ADC and a real-time data preprocessing module are integrated to complete the data collection, format conversion and preliminary feature extraction of each sensor node; using a parallel processing architecture, the signals from hundreds or even thousands of sensor nodes are synchronously input into the back-end quantum topological data fusion module, and the original signal obtained from the SET sensor is mapped to a high-dimensional topological space by using a nonlinear mapping algorithm, and the coupling parameters are obtained by topological invariant analysis; using non-commutative Fourier transform and Lie group representation methods, the thermodynamics, electrical signals and mechanical stress parameters are converted into Lie group space to construct a dynamic parameter space; The test probe with integrated topological optical metamaterials is used to invert the electromagnetic field distribution in a three-dimensional stacked structure through the change of the localized photon state density LDOS. Nanolithography and self-assembly technology are used to integrate the topological optical metamaterial structure 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 and geometric parameters of the metamaterial units. When the incident high-frequency signal irradiates the probe, the localized state density changes. A mapping model between the LDOS and the internal field distribution obtained based on electromagnetic numerical simulation and experimental calibration is pre-established. Using the reverse design algorithm, the measured LDOS change data is input into the preset nonlinear mapping model. After iterative solution and global optimization, the three-dimensional electromagnetic field distribution data is obtained.

3. The intelligent adaptive semiconductor packaging test optimization method according to claim 2, characterized in that: The non-Hermitian singular point sensing mechanism includes: implanting a PT symmetric microcavity array at a hybrid bonding interface, wherein the microcavity array includes microcavity units of the same size, each microcavity of the microcavity array has a symmetrically configured cavity shape and has nonlinear response characteristics. Through a global optimization algorithm, the microcavity units are uniformly distributed in space, with a spacing of no more than 5 μm, and the microcavity size ranges from tens of nanometers to hundreds of nanometers.

4. The intelligent adaptive semiconductor packaging test optimization method according to claim 3, characterized in that: The neuromorphic spike decision network SNN optimizes the test parameters by the following steps: Sa: Encode the 80-dimensional test parameters as neural pulse phases; Sb: maps package structural mechanical constraints to synaptic weights; Sc: Convert dynamic power consumption fluctuations into pulse rate modulation.

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

6. The intelligent adaptive semiconductor packaging test optimization method according to claim 5, characterized in that: The process of mapping the mechanical constraints of the packaging structure to synaptic weights in the step Sb includes: obtaining the mechanical constraint parameters of the packaging structure, including mechanical stress, mechanical load, and contact pressure in the semiconductor package, 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 neural pulse network, and when setting the stress threshold, mapping the relationship between the stress value and the synaptic weight through a nonlinear function, and updating it in real time during the test process through the synaptic timing-dependent plasticity STDP mechanism of the neuromorphic spiking neural network SNN, and adjusting the synaptic weights accordingly as the mechanical constraint conditions change; performing 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, and the information processed by each layer provides feedback to the neurons in the lower layer.

7. The intelligent adaptive semiconductor packaging test optimization method according to claim 6, characterized in that: The process of converting dynamic power consumption fluctuation into pulse firing rate modulation in step Sc includes: acquiring power consumption data of packaging test equipment, including dynamic power consumption fluctuation information; normalizing the power consumption fluctuation, and mapping the normalized dynamic power consumption fluctuation into a firing rate modulation signal in a neuromorphic pulse neural network; and establishing a mapping relationship between pulse firing rate and power consumption fluctuation through a nonlinear function.

8. The intelligent adaptive semiconductor packaging test optimization method according to claim 7, characterized in that: The proposed meta-learning-physics hybrid optimization framework performs decision delay optimization through the following process: a continuous implicit function f in the parameter space is established through the implicit neural representation INR θ (x, t), and through the Lie group differential geometry method, in SO(3)×R n Covariant optimization of two types of gradients is achieved in manifold space.

9. The intelligent adaptive semiconductor packaging test optimization method according to claim 8, characterized in that: The dynamic eigenmanifold space construction is achieved by the following steps: The high-dimensional test parameters are mapped to the compact Lie group representation space through non-commutative Fourier transform; Perform tensor product fusion operations on cross-domain parameters in the group representation space.

10. An intelligent adaptive semiconductor packaging test optimization system, applied to semiconductor packaging test equipment, characterized in that: include: Quantum topological sensor networks for collecting high-dimensional, sub-microsecond response test parameters; Bionic meta-learning dynamic decision engine, based on neuromorphic spike decision network (SNN) to optimize test parameters and simultaneously converge physical constraints; Dynamic eigenmanifold space building module for cross-domain parameter coupling optimization to achieve self-consistent optimization test system; Among them, when dynamically adjusting the test parameters, the bionic meta-learning dynamic decision engine uses a reinforcement learning mechanism to optimize the initial settings of the test parameters based on historical test data and actual feedback results, and automatically identifies and corrects systematic errors in the testing process; it introduces a deep reinforcement learning DRL algorithm to optimize the model parameters according to the loss function after each test and adjust them in real time.

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