A server mainboard memory stick plug-in test system

Abstract

The application discloses a kind of server mainboard memory stick plug test system, including active acoustic scanning unit, for emitting sound wave and gathering reflected sound wave;Adaptive noise suppression unit, for noise suppression to original reflected sound wave;Acoustic fingerprint extraction unit, for extracting acoustic feature vector;Meta-learning adaptation unit, for generating personalized encoder based on global meta-learner parameters and heuristic acoustic feature vector;Health state classification unit, for outputing shell health grade;Uncertainty processing unit, for evaluating confidence and triggering active learning;Privacy protection federal learning unit, for updating model after adding noise in local update collaborates with central server.The application realizes non-contact high-precision detection, zero-sample rapid adaptation, cross-vendor privacy protection data collaboration, active learning lossless verification and multi-objective optimization scheduling, significantly improves detection accuracy, change type efficiency and production line flexibility, with high industrial application value.

Description

Technical Field

[0001] This invention belongs to the field of server testing technology, and in particular relates to a server motherboard memory module insertion and removal testing system. Background Technology

[0002] During server motherboard manufacturing, memory slot insertion and removal tests are required to verify the reliability of contact between the memory modules and the slots, as well as the mechanical durability of the slots. Currently, the industry mainly uses two testing methods: one is a machine vision-based inspection system, which uses an industrial camera to photograph the slot contact area and uses image processing algorithms (such as edge detection and template matching) to identify defects such as deformation, missing parts, or oxidation of the contact springs; the other is a force sensor-based insertion and removal testing system, which uses a robotic arm to perform standard insertion and removal actions and collects force-displacement curves in real time, using features such as peak insertion force and insertion time to determine the contact status. Some high-end testing systems also integrate a four-wire contact resistance measurement module to evaluate the electrical contact quality between the contact springs and the gold fingers. These systems are usually used in conjunction with first-come, first-served or simple priority scheduling algorithms to complete batch motherboard testing tasks. In addition, with the development of deep learning technology, some research has attempted to apply convolutional neural networks to insertion and removal force curve analysis in order to improve the accuracy of abnormal pattern recognition.

[0003] However, the aforementioned existing technologies still have the following shortcomings. First, visual inspection is limited by the dense arrangement of the contact springs (up to 288 pins on one side of DDR5). The projections of adjacent pins interfere with each other, and minor deformations are easily masked. Furthermore, it is difficult to image and identify non-geometric defects such as plating oxidation and micro-cracks, resulting in a high rate of missed detections and false detections. Second, memory slots of different generations (DDR3 / DDR4 / DDR5) and from different manufacturers vary significantly in terms of geometry, contact spring stiffness, and material properties. Existing systems require separate calibration of visual parameters or insertion / removal strategies for each slot type, which takes several hours and cannot meet the needs of flexible production lines. Third, test data is scattered across various manufacturers, and due to concerns about trade secrets and data security, it cannot be centrally shared, resulting in insufficient training data for a single manufacturer and limited model generalization ability. Fourth, traditional testing systems only output "pass / fail" or simple force alarms, which cannot pinpoint the root cause of defects (such as contact spring fatigue, plating wear, and the influence of environmental humidity), hindering process improvement. Fifth, in large-scale mass production testing, there is a trade-off between throughput and energy consumption. Traditional greedy scheduling struggles to achieve multi-objective optimization, resulting in low testing efficiency and uneven equipment wear. Sixth, strong noise in the factory environment (air compressor harmonics, robotic arm impact noise) severely interferes with the stability of acoustic testing methods, and existing systems lack effective adaptive noise suppression mechanisms. Therefore, there is an urgent need for a novel plug-in / plug-out testing system capable of non-contact high-precision testing, rapid adaptation, privacy-preserving data collaboration, root cause analysis, multi-objective scheduling, and environmental robustness. Summary of the Invention

[0004] To overcome the aforementioned shortcomings of the prior art, the present invention provides a server motherboard memory module insertion and removal testing system, which solves the problems of low detection accuracy, slow replacement, data silos, lack of root cause analysis, and inefficient scheduling in the prior art memory slot testing.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A server motherboard memory module insertion and removal testing system includes:

[0007] An active acoustic scanning unit is used to emit sound waves into the memory slot and collect reflected sound waves, and output raw reflected sound wave data.

[0008] An adaptive noise suppression unit, whose input is connected to the output of the active acoustic scanning unit, is used to suppress noise in the original reflected sound wave data and output noise-reduced reflected sound wave data.

[0009] An acoustic fingerprint extraction unit, whose input is connected to the output of the adaptive noise suppression unit, is used to extract acoustic feature vectors from the noise-reduced reflected sound wave data.

[0010] The meta-learning adaptation unit is used to store the global meta-learner parameters received from the central server, and to perform an inner loop update based on the global meta-learner parameters and the tentative acoustic feature vector to generate personalized encoder parameters for the current memory slot type; wherein, the tentative acoustic feature vector is a feature vector extracted after performing a preset number of acoustic scans on the current memory slot.

[0011] The health status classification unit, whose input is connected to the output of the meta-learning adaptation unit, is used to classify the acoustic feature vector using the personalized encoder parameters and output the health status level of the memory slot spring.

[0012] An uncertainty processing unit, whose input is connected to the output of the health status classification unit, is used to evaluate the confidence level of the classification result. When the confidence level is lower than a preset threshold, it controls the active acoustic scanning unit to perform an additional acoustic scan or controls the insertion and removal actuator to perform a micro-insertion and removal, and updates the personalized encoder parameters using the collected verification data.

[0013] The privacy-preserving federated learning unit has its input connected to the output of the meta-learning adaptation unit. It is used to add noise to the local model update data and send it to the central server, and to receive the aggregated and updated global meta-learner parameters to update the meta-learning adaptation unit.

[0014] The scheduling unit, whose input is connected to the output of the uncertainty processing unit and whose output is connected to the control terminal of the insertion and removal execution mechanism, is used to dynamically calculate the test order based on the confidence level of each memory slot, the test task queue and device resource constraints, and control the insertion and removal execution mechanism to execute the insertion and removal tests in that order.

[0015] Preferably, it also includes a causal inference unit, whose input is connected to the output of the health status classification unit, for performing root cause analysis on the detected abnormal health status based on the structural causal model, and outputting the defect type and the contribution of each defect type.

[0016] Preferably, the active acoustic scanning unit comprises a phased array consisting of at least 16 miniature loudspeakers and an array consisting of at least 32 MEMS microphones; the phased array is used to transmit frequency-modulated continuous wave signals with a frequency range of 1kHz to 80kHz, a frequency modulation bandwidth of not less than 50kHz, and a duration of 5ms to 20ms; the sampling rate of the MEMS microphone array is not less than 200kHz.

[0017] Preferably, the adaptive noise suppression unit includes:

[0018] The first-stage suppression subunit is used to generate an anti-phase sound wave by using the filter-x least mean square algorithm and the ambient noise reference signal collected by the auxiliary microphone to cancel the steady-state ambient noise.

[0019] The second-level suppression subunit is used to separate the mixed signal after the first-level suppression into the target reflected sound wave component and the transient noise component using an independent component analysis algorithm, and outputs the target reflected sound wave component as the noise-reduced reflected sound wave data.

[0020] Preferably, the acoustic fingerprint extraction unit includes at least one one-dimensional convolutional neural network layer and at least one Transformer encoder layer; the one-dimensional convolutional neural network layer has a kernel size of 3, a stride of 1, and 64 output channels; the Transformer encoder layer contains 4 attention heads and a feedforward network dimension of 256; the acoustic fingerprint extraction unit takes the short-time Fourier transform time-frequency plot of the denoised reflected sound wave data as input and outputs a 128-dimensional acoustic feature vector.

[0021] Preferably, the meta-learning adaptation unit employs a model-independent meta-learning algorithm; the preset number of iterations is 3 to 5; the inner loop is: based on the global meta-learner parameters and the tentative acoustic feature vector, performing 1 to 3 steps of gradient descent update to obtain the personalized encoder parameters; the meta-learning adaptation unit also includes a continuous learning module, which, after each slot type adaptation is completed, stores the acoustic feature vector of that slot type and the corresponding health status level in a local cache, and performs a replay-based fine-tuning of the personalized encoder parameters every 50 to 200 tests.

[0022] Preferably, the uncertainty processing unit employs the Monte Carlo Dropout method, performing at least 20 forward propagations when the health status classification unit outputs the classification result, and calculating the confidence level based on the variance of the acoustic feature vectors output by each forward propagation; the preset threshold is 0.90 to 0.98; when the confidence level is lower than the preset threshold, the uncertainty processing unit first controls the active acoustic scanning unit to perform at least two additional acoustic scans with different beam pointing angles; if the confidence level is still lower than the preset threshold after the additional scans, it controls the insertion / removal actuator to perform a micro-insertion / removal, and collects the force-displacement curve as a verification label, adding the verification label to the local training set to update the personalized encoder parameters; the micro-insertion / removal is an insertion / removal action with an insertion depth not exceeding 50% of the normal insertion depth.

[0023] Preferably, the privacy-preserving federated learning unit includes:

[0024] The differential privacy injection subunit is used to add noise to the local model update data using a Gaussian noise mechanism. The variance of the noise is determined according to a preset privacy budget and sensitivity. The privacy budget ranges from 0.1 to 1.0.

[0025] The secure aggregation subunit is used to send the local model update data with added noise to the central server using a secure aggregation protocol based on secret sharing, and to receive the aggregated and updated global meta-learner parameters from the central server.

[0026] Preferably, the optimization algorithm used by the scheduling unit is a hybrid algorithm of non-dominated sorting genetic algorithm and decomposition-based multi-objective evolutionary algorithm; the optimization objectives include: total test completion time, total energy consumption, detection accuracy, and wear accumulation of the insertion and removal actuator; the scheduling unit also includes a dynamic rescheduling module, which is used to trigger local scheduling repair based on insertion heuristic when the number of slots with confidence below a preset threshold exceeds a preset proportion of the current queue; the preset proportion is 10% to 30%.

[0027] Preferably, the system further includes a model compression unit, the input of which is connected to the output of the acoustic fingerprint extraction unit and the output of the health status classification unit. This model compression unit is used to compress the neural network models in the acoustic fingerprint extraction unit and the health status classification unit into a lightweight model using a knowledge distillation method. The lightweight model is deployed on an embedded processor, with a single inference latency of no more than 50ms. The insertion / removal actuator is a six-axis robotic arm, with a force sensor and gripper installed at the end of the arm. The force sensor has a range of 0N to 200N, and the gripper has a clamping force of 10N to 30N.

[0028] The technical effects and advantages of the server motherboard memory module insertion and removal testing system of the present invention are as follows:

[0029] 1. This invention emits multi-frequency sound waves to the memory slot through an active acoustic scanning unit and collects reflected sound waves. Combined with an adaptive noise suppression unit and an acoustic fingerprint extraction unit, it realizes non-contact evaluation of the geometry, stiffness and surface condition of the spring sheet. This avoids the problems of projection occlusion and recognition interference caused by the dense arrangement of pins in traditional visual inspection, and significantly improves the detection accuracy of defects such as small deformation and oxidation of the spring sheet.

[0030] 2. This invention stores global meta-learner parameters received from the central server through a meta-learning adaptation unit, and performs an inner loop update based on the tentative acoustic feature vector obtained by performing a preset number of acoustic scans on the current memory slot to generate personalized encoder parameters for the current slot type. This allows the system to quickly generate a high-precision detection model when faced with a new type of memory slot that has never been seen before, without the need for manual recalibration and with only a few tentative scans, which greatly shortens the changeover time on the production line.

[0031] 3. This invention assesses the confidence level of classification results through an uncertainty processing unit and automatically triggers additional acoustic scanning or micro-insertion when the confidence level falls below a preset threshold. It collects verification data to update personalized encoder parameters, achieving proactive screening of edge samples and model self-optimization. The insertion depth of the micro-insertion does not exceed 50% of the normal insertion depth, ensuring no damage to the spring during the verification process.

[0032] 4. This invention uses a privacy-preserving federated learning unit to add noise to the local model update data before sending it to the central server, and receives the aggregated and updated global meta-learner parameters. This allows multiple manufacturers to jointly train the global model without disclosing their core process data (such as the composition of the spring coating and the geometric tolerance of the slot). This is especially beneficial for manufacturers with less data to improve detection performance and effectively solves the problem of data silos in the industry chain.

[0033] 5. This invention uses a scheduling unit to dynamically calculate the test order based on the confidence level of each memory slot, the test task queue, and equipment resource constraints, and adopts an optimization algorithm. This achieves joint optimization of multiple objectives such as total test completion time, total energy consumption, detection accuracy, and wear accumulation of insertion and removal actuators, significantly improving production line testing efficiency and equipment lifespan balance.

[0034] 6. This invention uses a causal inference unit to perform root cause analysis on detected abnormal health states based on a structural causal model, outputting the defect type and the contribution of each defect type. This provides a quantitative and interpretable basis for process improvement and quality traceability, overcoming the shortcomings of traditional systems that only output "pass / fail" and cannot locate the root cause.

[0035] 7. This invention effectively suppresses various interferences in the factory environment by using a two-stage cascaded structure of an adaptive noise suppression unit (the first stage filter-x least mean square algorithm cancels steady-state noise, and the second stage independent component analysis algorithm separates transient noise) combined with the environmental noise reference signal collected by the auxiliary microphone, thus ensuring the stability and classification accuracy of acoustic fingerprint extraction.

[0036] 8. This invention uses a knowledge distillation method through a model compression unit to compress neural network models into lightweight models that can be deployed on embedded processors. The single inference latency is controllable, reducing system hardware costs and facilitating large-scale deployment on production lines. Attached Figure Description

[0037] Figure 1 : Overall structural block diagram of the system of the present invention;

[0038] Figure 2 : A two-stage cascaded structure diagram of the adaptive noise suppression unit in this invention;

[0039] Figure 3 : A flowchart of the meta-learning adaptation unit in this invention;

[0040] Figure 4 : Flowchart of uncertainty handling and active learning in this invention;

[0041] Figure 5 : Data interaction sequence diagram of the privacy-preserving federated learning unit in this invention. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another 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," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0044] refer to Figure 1-5 This invention provides a server motherboard memory module insertion and removal testing system, comprising: an active acoustic scanning unit for emitting sound waves into the memory slot and collecting reflected sound waves, and outputting raw reflected sound wave data; an adaptive noise suppression unit, whose input is connected to the output of the active acoustic scanning unit, for performing noise suppression on the raw reflected sound wave data and outputting noise-reduced reflected sound wave data; an acoustic fingerprint extraction unit, whose input is connected to the output of the adaptive noise suppression unit, for extracting acoustic feature vectors from the noise-reduced reflected sound wave data; a meta-learning adaptation unit for storing global meta-learner parameters received from a central server, and performing an inner loop update based on the global meta-learner parameters and tentative acoustic feature vectors to generate personalized encoder parameters for the current memory slot type, wherein the tentative acoustic feature vectors are feature vectors extracted after performing a preset number of acoustic scans on the current memory slot; and a health status classification unit, whose input is connected to the output of the meta-learning adaptation unit, for using the... The system comprises: a personalized encoder parameter classifying acoustic feature vectors and outputting the health status level of memory slot springs; an uncertainty processing unit, whose input is connected to the output of the health status classification unit, used to evaluate the confidence level of the classification results; when the confidence level is lower than a preset threshold, controlling the active acoustic scanning unit to perform additional acoustic scanning or controlling the insertion / removal actuator to perform micro-insertion / removal, and updating the personalized encoder parameters using the collected verification data; a privacy-preserving federated learning unit, whose input is connected to the output of the meta-learning adaptation unit, used to add noise to the local model update data before sending it to the central server, and receive the aggregated and updated global meta-learner parameters to update the meta-learning adaptation unit; and a scheduling unit, whose input is connected to the output of the uncertainty processing unit and whose output is connected to the control end of the insertion / removal actuator, used to dynamically calculate the test order using an optimization algorithm based on the confidence level of each memory slot, the test task queue, and equipment resource constraints, and control the insertion / removal actuator to perform insertion / removal tests in that order.

[0045] Example 1

[0046] Objective: To verify the overall feasibility and performance of the server motherboard memory module insertion and removal test system proposed in this invention in a standard factory environment, including acoustic detection accuracy, meta-learning adaptation speed, uncertainty handling effect, and scheduling optimization benefits.

[0047] Implementation System: The test system built in this embodiment includes the following components:

[0048] The active acoustic scanning unit consists of a phased array of 32 miniature speakers and an array of 64 MEMS microphones. The phased array is used to transmit frequency-modulated continuous wave signals with a start frequency of 1kHz, an end frequency of 80kHz, a frequency modulation bandwidth of 79kHz, and a duration of 10ms; the MEMS microphone array has a sampling rate of 200kHz.

[0049] Adaptive noise suppression unit: includes a first-level filtering-x least mean square algorithm (filter order 128, step size factor μ=0.01) and a second-level independent component analysis algorithm (FastICA, nonlinear function tanh, 1000 iterations).

[0050] The acoustic fingerprint extraction unit consists of a one-layer one-dimensional convolutional neural network (kernel size 3, stride 1, output channels 64) and a one-layer Transformer encoder (4 attention heads, feedforward network dimension 256), outputting a 128-dimensional feature vector.

[0051] Meta-learning adaptation unit: Employs a model-independent meta-learning algorithm, with global meta-learner parameters distributed after pre-training by a central server.

[0052] Health status classification unit: Based on personalized encoder parameters, it classifies and outputs four levels: normal, slightly deformed, severely deformed, and broken.

[0053] Uncertainty handling unit: Monte Carlo Dropout method (50 forward propagations) is used, and the confidence threshold is set to 0.95.

[0054] Privacy-preserving federated learning unit: includes differential privacy injection subunit (Gaussian noise, privacy budget ε=0.5) and secure aggregation subunit (Shamir secret sharing, threshold 3 share 5).

[0055] Scheduling unit: A hybrid algorithm combining non-dominated sorting genetic algorithm and decomposition-based multi-objective evolutionary algorithm is used.

[0056] Insertion / removal actuator: a six-axis robotic arm with an end effector equipped with a force sensor with a range of 200N and an accuracy of ±0.1N, and a gripper with a clamping force of 20N.

[0057] The test subjects were 500 DDR5 memory slots on a server motherboard.

[0058] Implementation steps:

[0059] System initialization: Download global meta-learner parameters from the central server and store them in the meta-learning adaptation unit.

[0060] Trial scan: For the first slot to be tested, the active acoustic scanning unit scans pin by pin using beamforming technology, collects three reflected sound waves, and generates three trial acoustic feature vectors after adaptive noise suppression and acoustic fingerprint extraction.

[0061] Meta-learning adaptation: The meta-learning adaptation unit performs a two-step gradient descent inner loop update based on global parameters and three tentative feature vectors to generate personalized encoder parameters for the DDR5 slot, taking approximately 8 seconds.

[0062] Batch testing: Active acoustic scanning → noise suppression → fingerprint extraction → health status classification are performed sequentially on the remaining 499 slots. Simultaneously, the uncertainty processing unit evaluates the confidence level of each classification result.

[0063] Active learning: When the confidence level is below 0.95, two additional scans with different beam pointing angles (15° and 25°) are performed first; if the confidence level is still below 0.95, a micro-insertion is performed (insertion depth is 40% of the normal depth), the force-displacement curve is collected as a verification label, added to the local training set and the personalized encoder is fine-tuned.

[0064] Federated learning: After every 100 tests, the meta-learning gradient is calculated, and after differential privacy-enhanced noise reduction, it is uploaded to the central server through a secure aggregation protocol. The server then aggregates the data, updates the global meta-learner, and distributes it.

[0065] Scheduling optimization: The scheduling unit dynamically plans the test sequence with the optimization objectives of total test completion time, total energy consumption, detection accuracy, and cumulative wear of the robotic arm. The dynamic rescheduling module triggers local repair when the number of slots with a confidence level below 0.95 exceeds 15% of the queue.

[0066] Implementation results:

[0067] In terms of detection accuracy, the false negative rate for minor deformation of the spring piece (width change less than 0.05mm) was 1.8%, and the false positive rate was 1.2%. Regarding adaptation speed, the new slot adapted in 8 seconds, achieving a classification accuracy of 96.5% after adaptation. In terms of active learning performance, the confidence levels of 32 low-confidence samples all improved to above 0.98 after active learning, and the plastic deformation of the spring piece due to minor insertion / removal was less than 0.01mm. Regarding scheduling benefits, the total testing time was reduced by 21% compared to the greedy algorithm, energy consumption was reduced by 14%, and the wear uniformity of the robotic arm was improved by 30%. In terms of federated learning, with a privacy budget ε=0.5, the global model accuracy reached 95.8%, and the re-identification risk was less than 0.01.

[0068] Example 2

[0069] Purpose of implementation: To verify the performance of the adaptive noise suppression unit under different noise environments and optimize its parameter configuration to cope with harsh factory environments containing strong impact noise.

[0070] System Implementation: This embodiment is based on the system in Embodiment 1, with parameter adjustments made to the adaptive noise suppression unit. The rest of the system is the same as in Embodiment 1. The test environment is a server motherboard manufacturing workshop containing a riveting machine. The background noise includes air compressor (125Hz harmonic, steady state) and riveting machine impact (peak sound pressure 120dB, duration 5ms, transient).

[0071] Implementation steps:

[0072] Ambient noise acquisition: Before the active acoustic scanning unit operates, an auxiliary microphone collects 5 seconds of ambient noise and analyzes its spectral characteristics. Steady-state noise is mainly 125Hz and its harmonics, while transient noise consists of random impulses.

[0073] First-level suppression: The filter-x least mean square algorithm (FxLMS) is used with a filter order of 128 and a step factor of μ=0.01. Four auxiliary microphones (placed at distances of 0.3m, 0.5m, 0.8m and 1.0m from the sound source, respectively) are used to generate anti-phase sound waves to cancel out steady-state noise.

[0074] Second-level suppression: The independent component analysis algorithm (FastICA) is used. When the kurtosis of the mixed signal is detected to be greater than 5 (indicating the presence of impact noise), the analysis time window is shortened from 10ms to 2ms and the number of iterations is increased to 2000 to enhance the impact separation capability.

[0075] Performance comparison: The signal-to-noise ratio of the target reflected sound wave and the subsequent classification accuracy were tested under four conditions: no noise suppression enabled, only the first level enabled, two levels of suppression enabled (default parameters), and two levels of suppression enabled (optimized parameters).

[0076] Implementation results:

[0077] Without suppression, the signal-to-noise ratio (SNR) is 12 dB, and the classification accuracy is 72%. With only the first level of suppression enabled, the SNR increases to 25 dB, the classification accuracy is 85%, and steady-state noise is suppressed by approximately 28 dB, but impulse noise remains. With both levels of suppression enabled (default parameters: 10 ms window, 1000 iterations), the SNR reaches 32 dB, and the classification accuracy is 90%. With both levels of suppression enabled (optimized parameters: 2 ms window, 2000 iterations, kurtosis detection), the SNR reaches 38 dB, and the classification accuracy is 97%. A dispersed auxiliary microphone layout, compared to a concentrated layout at 0.5 m, increases steady-state noise suppression from 28 dB to 32 dB. This example demonstrates that adaptively adjusting independent component analysis parameters through kurtosis detection can significantly improve detection performance in impulsive noise environments.

[0078] Example 3

[0079] Purpose of implementation: To compare the adaptation effect of the meta-learning adaptation unit under different number of trials and inner loop steps, to verify the rationality of the parameter range (preset number of trials 3-5 times, inner loop steps 1-3 steps) defined in this invention, and to demonstrate the ability of the continuous learning module to prevent catastrophic forgetting.

[0080] System Implementation: This embodiment uses the system from Embodiment 1, but the meta-learning adaptation unit is configured with various different parameters. The test objects are a new type of DDR6 memory slot (not present in any historical training data), totaling 200.

[0081] Implementation steps:

[0082] The meta-learning adaptation unit is configured with the following five parameter combinations: Configuration A is 3 trials and 2 inner loops; Configuration B is 5 trials and 3 inner loops; Configuration C is 3 trials and 1 inner loop; Configuration D is 2 trials and 2 inner loops (outside the range); Configuration E is 6 trials and 4 inner loops (outside the range).

[0083] For each configuration, perform the following operations: perform a corresponding number of tentative acoustic scans on the first slot to generate a tentative feature vector; perform an inner loop update to generate a personalized encoder; and perform a classification test on the remaining 199 slots, recording the adaptation time and accuracy.

[0084] Continuous learning validation: Using configuration A, after continuously adapting to 50 different slot types (200 samples per class), the accuracy decay on the initial 5 slot types was tested. Two modes were set up: no continuous learning and fine-tuning by replaying every 100 tests (sampling 50 historical samples).

[0085] Implementation results:

[0086] Configuration A (3 trials, 2-step inner loop): Adaptation time 8 seconds, accuracy 96.5% after adaptation. Configuration B (5 trials, 3-step inner loop): Adaptation time 15 seconds, accuracy 98.2%. Configuration C (3 trials, 1-step inner loop): Adaptation time 5 seconds, accuracy 93.8%. Configuration D (2 trials, 2-step inner loop): Adaptation time 6 seconds, accuracy 91.0%. Configuration E (6 trials, 4-step inner loop): Adaptation time 20 seconds, accuracy 98.5%.

[0087] Configurations A, B, and C all achieve an accuracy rate higher than 93.5%, with an adaptation time of no more than 15 seconds. Configuration D has an accuracy rate of only 91.0%, below the acceptable threshold of 95%; configuration E has a slightly higher accuracy rate, but the adaptation time increases to 20 seconds, resulting in diminishing marginal returns. Therefore, the optimal balance is achieved by limiting the number of trials to 3-5 and the internal loop to 1-3 steps as defined in this invention.

[0088] Continuous learning effect: Without continuous learning, after adapting to 50 slots, the accuracy on the initial 5 slots dropped from 96.5% to 81.5%, a decrease of 15 percentage points. After fine-tuning by replaying every 100 tests, the accuracy remained above 93.8%, with a decrease of only 2.7 percentage points.

[0089] Example 4

[0090] Objective: To verify the active learning capability of the uncertainty processing unit when detecting edge samples such as coating oxidation and slight deformation, and to quantitatively assess the damage to the spring contacts caused by micro-insertion and removal.

[0091] System Implementation: This embodiment uses the system from Embodiment 1. The test subjects were a batch of 200 DDR4 memory slots with slight plating oxidation. Among them, professional testing determined that: 150 slots were normal, 30 had slight oxidation, 10 had severe oxidation, and 10 had slight spring deformation.

[0092] Implementation steps:

[0093] The pre-trained personalized encoder was used to classify 200 slots and calculate the confidence score. The classification results were then compared with the true values.

[0094] For slots with a confidence level below 0.95, perform the first round of active learning: control the active acoustic scanning unit to perform two additional acoustic scans at beam pointing angles of 15° and 25°, and recalculate the confidence level.

[0095] For slots whose confidence level is still below 0.95 after the first round, a second round of active learning is performed: the insertion and removal actuator is controlled to perform a micro-insertion and removal operation (the insertion depth is 30%, 40%, and 50% of the normal depth, with each group corresponding to a different slot), and the force-displacement curve is collected and used as a validation label to update the model.

[0096] The surface morphology of the spring before and after micro-insertion and extraction was measured using a high-precision microscope, and the amount of plastic deformation was calculated.

[0097] The damage level and model performance improvement were compared under different micro-insertion and extraction depths.

[0098] Implementation results:

[0099] Initial classification: Of the 200 slots, 32 had confidence levels between 0.85 and 0.90, and 8 had confidence levels below 0.85. The true state distribution generally matches the classification results, but the initial confidence level of the oxidized samples was relatively low.

[0100] After the first round of active learning: Of the 32 slots with confidence levels of 0.85-0.90, 22 have improved to confidence levels above 0.96, while 10 remain below 0.95.

[0101] After the second round of active learning: After verification with micro-insertion and removal of 10 slots, the model was fine-tuned in one step, and the confidence level reached above 0.97. Force-displacement curve analysis showed that the oxidized sample fluctuated with a standard deviation greater than 0.5N in the initial insertion stage (0-2mm), and the peak insertion force of the deformed sample exceeded 80N.

[0102] Micro-insertion and extraction damage test results: Plastic deformation less than 0.005 mm at 30% insertion depth; less than 0.008 mm at 40% insertion depth; and less than 0.012 mm at 50% insertion depth. The JEDEC standard allows for 0.05 mm of wear in slot life tests; therefore, all three depths meet the non-destructive testing requirements. This embodiment preferably uses a depth of 30% to 40%.

[0103] After active learning, the overall classification accuracy improved from 92% to 98.5%, and the accuracy of identifying oxidized samples improved from 78% to 96%.

[0104] Example 5

[0105] Objective: To verify the effectiveness of privacy-preserving federated learning units in multi-vendor collaborative scenarios, including the trade-offs between different privacy budgets on model performance and privacy protection strength, as well as the computational overhead of secure aggregation protocols.

[0106] Implementation System: This embodiment simulates three manufacturers (motherboard manufacturer A, memory manufacturer B, and system integrator C) each deploying a local testing system (same as in Embodiment 1), and a central server. Local Data Distribution for Each Manufacturer: Manufacturer A has 2000 DDR4 slot data entries and 1000 DDR5 slot data entries; Manufacturer B has 1500 DDR5 slot data entries and 500 custom slot data entries; Manufacturer C has 500 DDR4 slot data entries.

[0107] Implementation steps:

[0108] Initialization: The central server randomly initializes the global meta-learner parameters and distributes them to the three vendors.

[0109] Local training: Each vendor trains for 10 epochs on local data and calculates the meta-learning gradient.

[0110] Differential privacy noise enhancement: Four cases were tested with privacy budgets ε=0.2, 0.5, 1.0, and no privacy protection. The noise standard deviation σ was calculated based on ε and sensitivity.

[0111] Secure aggregation: The Shamir secret sharing protocol (threshold 3, share 5) is adopted. Each node splits the noisy gradient into 5 shares and sends them to the server. The server aggregates the gradients, reconstructs the aggregated gradients, and updates the global model.

[0112] Iteration: Repeat steps 2-4 for a total of 10 rounds.

[0113] Evaluation: After each round, the model accuracy is evaluated on the test sets of the three vendors. Simultaneously, an attacker is simulated attempting to reverse-engineer the original gradient of a single node from the uploaded gradient to calculate the risk of re-identification.

[0114] Zero-knowledge proof test: Manufacturer C generates a zero-knowledge proof to prove that its product passes the test (shrapnel health score ≥ 0.98), and the customer verifies the proof without obtaining the original data.

[0115] Implementation results:

[0116] With a privacy budget ε=0.2, the noise standard deviation σ=1.68, the final global model accuracy is 92.5%, the re-identification risk is less than 0.005, and the single-round aggregation time is 480ms. With ε=0.5, σ=0.67, the accuracy is 95.8%, the re-identification risk is less than 0.01, and the single-round aggregation time is 470ms. With ε=1.0, σ=0.33, the accuracy is 96.2%, the re-identification risk is less than 0.02, and the single-round aggregation time is 465ms. Without privacy protection, the accuracy is 97.0%, the re-identification risk is greater than 0.80, and the single-round aggregation time is 460ms.

[0117] With ε=0.5 as the default configuration, the performance loss is only 1.2 percentage points, and the risk of re-identification is less than 1%. After 10 rounds of federated training, the test set accuracy for vendors A, B, and C is 96.2%, 97.1%, and 95.8%, respectively; the accuracy for independent training (without federated learning) for each vendor is 89%, 91%, and 82%, respectively. Vendor C benefits the most, with an accuracy improvement of 13.8 percentage points. The computational overhead of the secure aggregation protocol: a single round of aggregation takes approximately 470ms, which has a negligible impact on training efficiency. Zero-knowledge proof: proof generation time is approximately 2 seconds, verification time is approximately 200ms, and the proof size is approximately 1KB.

[0118] Comparative Example 1

[0119] Purpose of comparison: To compare the present invention with the traditional "visual inspection + fixed parameter plug-and-play" system, so as to highlight the advantages of the present invention in terms of detection accuracy, adaptability, scheduling optimization and privacy protection.

[0120] Comparison System: This system employs traditional machine vision to detect memory slot spring deformation, coupled with a robotic arm with fixed parameters for insertion and removal. Specific configurations include: a 5-megapixel industrial camera, a ring-shaped white light source, Canny edge detection combined with template matching, using spring gap width as a feature; a robotic arm with a fixed speed of 50mm / s and a fixed insertion depth of 40mm, without uncertainty handling or active learning; scheduling using a first-come, first-served (FIFO) approach; no data sharing, data is independent for each manufacturer; and no privacy protection mechanism. The test objects are the same as in Example 1 (500 DDR5 slots).

[0121] Comparison steps:

[0122] Vision system calibration: Manually setting camera parameters and templates.

[0123] Visual inspection and fixed-parameter insertion / removal tests were performed on each slot.

[0124] Record indicators such as missed detection rate, false detection rate, test time, and energy consumption.

[0125] The test results were compared with those of Embodiment 1 of the present invention.

[0126] Comparison results:

[0127] The false negative rate for minor deformations (width change less than 0.05 mm) in Comparative Example 1 was 23%, while in Example 1 of this invention it was 1.8%. The false positive rate was 15% in Comparative Example 1 and 1.2% in this invention. The adaptation time to a new slot was approximately 2 hours in Comparative Example 1 (manual calibration), while in this invention it was only 8 seconds. The accuracy of the manufacturer's C model was 82% in Comparative Example 1 (independent training), while in this invention it was 95.8% (federated learning). Root cause analysis capability was absent in Comparative Example 1, but was quantified in this invention. The total testing time (500 slots) was 4.2 hours in Comparative Example 1 and 3.1 hours in this invention. The total energy consumption was 12.7 kWh in Comparative Example 1 and 10.1 kWh in this invention. The uneven wear of the robotic arm was 35% in Comparative Example 1 and 12% in this invention. Privacy protection was absent in Comparative Example 1, but was differential privacy ε=0.5 in this invention. Comparative Example 1 was significantly inferior to this invention in all indicators.

[0128] Comparative Example 2

[0129] Objective of the comparison: To verify the necessity of meta-learning adaptation units and privacy-preserving federated learning units, and to compare them with systems that only use acoustic fingerprints and fixed classifiers (without meta-learning and federated learning).

[0130] The comparison system is basically the same as in Example 1, but with the following modifications: the meta-learning adaptation unit is removed, and a fixed-structure convolutional neural network classifier (ResNet-18) is used instead, pre-trained on local data from vendor A, without online adaptation; the privacy-preserving federated learning unit is removed, and each vendor trains independently without cross-vendor parameter aggregation. The test objects are vendor B's new DDR6 slots (not included in any training data), totaling 200. Simultaneously, the performance of vendor C (with less data) on DDR4 slots is tested.

[0131] Comparison steps:

[0132] The fixed classifier directly classifies the new DDR6 slots and records the initial accuracy.

[0133] After 100 tests, record the accuracy again (the accuracy should not change as the classifier has no adaptive ability).

[0134] Manufacturer C independently trained the model and recorded its accuracy on the DDR4 test set.

[0135] Compare with the corresponding metrics of Embodiment 1 of the present invention (using meta-learning adaptation and federated learning).

[0136] Comparison results:

[0137] The initial accuracy of the new slot in Comparative Example 2 was 72%, while that in Example 1 of this invention was 96.5% after adaptation. The accuracy after adaptation (after 100 tests) was 74% in Comparative Example 2 (no adaptation capability), while that of this invention was 98.2%. The accuracy of the vendor C model was 81% in Comparative Example 2 (independent training), while that of this invention was 95.8% (federated learning). Regarding robustness to slot type drift, Comparative Example 2 was poor (requiring retraining), while this invention was strong (rapid adaptation). Cross-vendor data utilization was 0% in Comparative Example 2, while that of this invention was 100% (through federated learning). Comparative Example 2 shows that a fixed classifier without a meta-learning mechanism cannot quickly adapt to new slots, with an initial accuracy of only 72%, and cannot be improved with a small number of samples. Simultaneously, the lack of federated learning resulted in poor performance of the vendor C model with limited data (81% vs. 95.8%). This invention solves the above problems through meta-learning and federated learning.

[0138] Compared to Examples 1-5 and Comparative Example 1, Comparative Example 1, which uses traditional machine vision combined with a fixed-parameter insertion / removal system, suffers from severe deficiencies in detection accuracy. Due to the dense arrangement of memory slot contacts (288 pins on one side of DDR5), the projections of adjacent pins interfere with each other, resulting in a 23% false negative rate and a 15% false positive rate for visual inspection of minute deformations (width changes less than 0.05mm), leading to a large number of defective products flowing into subsequent processes. In contrast, Example 1 of this invention achieves non-contact, high-precision detection through active acoustic scanning and adaptive noise suppression, achieving a false negative rate of only 1.8% and a false positive rate of only 1.2% for minute contact deformations, improving detection accuracy by more than an order of magnitude. Regarding the efficiency of adapting to new slots, Comparative Example 1 requires manual recalibration of camera parameters, light source angle, and insertion / removal speed, taking approximately 2 hours per changeover, severely impacting production line flexibility. Embodiment 3 of this invention, through a meta-learning adaptation unit, requires only 3 to 5 trial acoustic scans and 1 to 3 internal loop updates to automatically generate a personalized encoder for the new slot within 8 to 15 seconds. After adaptation, the classification accuracy exceeds 96%, achieving rapid model changeover with zero human intervention. Embodiment 4 further verifies the uncertainty handling and active learning mechanism. For edge samples (such as coating oxidation or slight deformation), additional acoustic scans or micro-insertion verification are used to improve the overall classification accuracy from 92% to 98.5%. Moreover, the plastic deformation of the spring sheet caused by micro-insertion is less than 0.01 mm, far below the 0.05 mm wear allowable by the JEDEC standard, truly achieving non-destructive testing. Comparative Embodiment 1 has no active learning capability at all; low-confidence samples can only rely on manual re-inspection, which is inefficient and highly subjective.

[0139] In Comparative Example 1, data from different vendors is isolated, making it impossible to share training data across enterprises. This results in the model accuracy of vendors with less data (such as vendor C) training independently achieving only 82%. Embodiment 5 of this invention, through a privacy-preserving federated learning unit, achieves collaborative training among three vendors under a differential privacy budget ε=0.5. The model accuracy of vendor C increases to 95.8%, an improvement of 13.8 percentage points, while the re-identification risk is less than 1%. The computational overhead of the secure aggregation protocol is only 470ms per round, having almost no impact on training efficiency. Comparative Example 1 lacks any privacy protection mechanism, storing and transmitting test data in plaintext, posing a risk of leaking core process parameters. Regarding test scheduling, Comparative Example 1 uses first-come, first-served scheduling, resulting in a total test time of 4.2 hours, energy consumption of 12.7kWh, and a 35% uneven wear rate on the robotic arm. Embodiment 1 of this invention utilizes a multi-objective optimization scheduling unit, with total test completion time, total energy consumption, detection accuracy, and cumulative wear of the robotic arm as optimization objectives. It employs a hybrid algorithm combining NSGA-III and MOEA / D to dynamically plan the test sequence, reducing the total test time to 3.1 hours (a 26% reduction), energy consumption to 10.1 kWh (a 20% reduction), and robotic arm wear unevenness to 12% (a 66% improvement). The dynamic rescheduling module automatically triggers local repair when low-confidence slots exceed 15% of the queue, further ensuring test quality. Furthermore, compared to Embodiment 1, which lacks root cause analysis capabilities and only outputs "pass / fail" or simple force alarms, Embodiments 1 to 5 of this invention provide causal inference units that can output the contribution of defect root causes (e.g., coating oxidation 58%, mechanical fatigue 27%, ambient humidity 15%), providing quantitative evidence for process improvement. In summary, this invention comprehensively outperforms traditional visual inspection systems with fixed parameter plug-and-play mechanisms in core indicators such as detection accuracy, adaptive efficiency, data collaboration, scheduling optimization, privacy protection, and root cause analysis, demonstrating significant technological advancements and industrial application value.

[0140] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0141] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A server motherboard memory module insertion and removal testing system, characterized in that, include: An active acoustic scanning unit is used to emit sound waves into the memory slot and collect reflected sound waves, and output raw reflected sound wave data. An adaptive noise suppression unit, whose input is connected to the output of the active acoustic scanning unit, is used to suppress noise in the original reflected sound wave data and output noise-reduced reflected sound wave data. An acoustic fingerprint extraction unit, whose input is connected to the output of the adaptive noise suppression unit, is used to extract acoustic feature vectors from the noise-reduced reflected sound wave data. The meta-learning adaptation unit is used to store the global meta-learner parameters received from the central server, and to perform an inner loop update based on the global meta-learner parameters and the tentative acoustic feature vector to generate personalized encoder parameters for the current memory slot type; wherein, the tentative acoustic feature vector is a feature vector extracted after performing a preset number of acoustic scans on the current memory slot. The health status classification unit, whose input is connected to the output of the meta-learning adaptation unit, is used to classify the acoustic feature vector using the personalized encoder parameters and output the health status level of the memory slot spring. An uncertainty processing unit, whose input is connected to the output of the health status classification unit, is used to evaluate the confidence level of the classification result. When the confidence level is lower than a preset threshold, it controls the active acoustic scanning unit to perform an additional acoustic scan or controls the insertion and removal actuator to perform a micro-insertion and removal, and updates the personalized encoder parameters using the collected verification data. The privacy-preserving federated learning unit has its input connected to the output of the meta-learning adaptation unit. It is used to add noise to the local model update data and send it to the central server, and to receive the aggregated and updated global meta-learner parameters to update the meta-learning adaptation unit. The scheduling unit, whose input is connected to the output of the uncertainty processing unit and whose output is connected to the control terminal of the insertion and removal execution mechanism, is used to dynamically calculate the test order based on the confidence level of each memory slot, the test task queue and device resource constraints, and control the insertion and removal execution mechanism to execute the insertion and removal tests in that order.

2. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, It also includes a causal inference unit, whose input is connected to the output of the health status classification unit, and is used to perform root cause analysis on the detected abnormal health status based on the structural causal model, and output the defect type and the contribution of each defect type.

3. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, The active acoustic scanning unit includes a phased array consisting of at least 16 miniature loudspeakers and an array consisting of at least 32 MEMS microphones; the phased array is used to transmit frequency-modulated continuous wave signals with a frequency range of 1kHz to 80kHz, a frequency modulation bandwidth of not less than 50kHz, and a duration of 5ms to 20ms; the sampling rate of the MEMS microphone array is not less than 200kHz.

4. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, The adaptive noise suppression unit includes: The first-stage suppression subunit is used to generate an anti-phase sound wave by using the filter-x least mean square algorithm and the ambient noise reference signal collected by the auxiliary microphone to cancel the steady-state ambient noise. The second-level suppression subunit is used to separate the mixed signal after the first-level suppression into the target reflected sound wave component and the transient noise component using an independent component analysis algorithm, and outputs the target reflected sound wave component as the noise-reduced reflected sound wave data.

5. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, The acoustic fingerprint extraction unit includes at least one one-dimensional convolutional neural network layer and at least one Transformer encoder layer; the one-dimensional convolutional neural network layer has a kernel size of 3, a stride of 1, and 64 output channels; the Transformer encoder layer contains 4 attention heads and a feedforward network dimension of 256; the acoustic fingerprint extraction unit takes the short-time Fourier transform time-frequency plot of the denoised reflected sound wave data as input and outputs a 128-dimensional acoustic feature vector.

6. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, The meta-learning adaptation unit employs a model-independent meta-learning algorithm; the preset number of iterations is 3 to 5; the inner loop is: based on the global meta-learner parameters and the tentative acoustic feature vector, performing 1 to 3 steps of gradient descent update to obtain the personalized encoder parameters; the meta-learning adaptation unit also includes a continuous learning module, which, after each slot type adaptation is completed, stores the acoustic feature vector of that slot type and the corresponding health status level in a local cache, and performs a replay-based fine-tuning of the personalized encoder parameters every 50 to 200 tests.

7. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, The uncertainty processing unit employs the Monte Carlo Dropout method, performing at least 20 forward propagations when the health status classification unit outputs the classification results. The confidence level is calculated based on the variance of the acoustic feature vectors output from each forward propagation. The preset threshold is between 0.90 and 0.

98. When the confidence level is lower than the preset threshold, the uncertainty processing unit first controls the active acoustic scanning unit to perform at least two additional acoustic scans with different beam pointing angles. If the confidence level remains lower than the preset threshold after the additional scans, the insertion / removal actuator performs a micro-insertion / removal, and collects the force-displacement curve as a verification label. The verification label is added to the local training set to update the personalized encoder parameters. The micro-insertion / removal is an insertion / removal action with an insertion depth not exceeding 50% of the normal insertion depth.

8. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, The privacy-preserving federated learning unit includes: The differential privacy injection subunit is used to add noise to the local model update data using a Gaussian noise mechanism. The variance of the noise is determined according to a preset privacy budget and sensitivity. The privacy budget ranges from 0.1 to 1.

0. The secure aggregation subunit is used to send the local model update data with added noise to the central server using a secure aggregation protocol based on secret sharing, and to receive the aggregated and updated global meta-learner parameters from the central server.

9. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, The scheduling unit employs a hybrid algorithm combining a non-dominated sorting genetic algorithm and a decomposition-based multi-objective evolutionary algorithm. The optimization objectives include: total test completion time, total energy consumption, detection accuracy, and wear accumulation of the insertion and removal actuators. The scheduling unit also includes a dynamic rescheduling module, which triggers local scheduling repair based on insertion heuristics when the number of slots with confidence levels below a preset threshold exceeds a preset proportion of the current queue. The preset proportion is 10% to 30%.

10. The server motherboard memory module insertion and removal testing system as described in claim 1, characterized in that, It also includes a model compression unit, the input of which is connected to the output of the acoustic fingerprint extraction unit and the output of the health status classification unit. The model compression unit is used to compress the neural network models in the acoustic fingerprint extraction unit and the health status classification unit into lightweight models using a knowledge distillation method. The lightweight model is deployed on an embedded processor with a single inference latency of no more than 50ms. The insertion and removal actuator is a six-axis robotic arm. A force sensor and a gripper are installed at the end of the robotic arm. The force sensor has a range of 0N to 200N, and the gripper has a clamping force of 10N to 30N.

Images