GPU chip detection method and related equipment

Abstract

The invention discloses a GPU chip detection method and related equipment, and the method comprises the steps: injecting a low-duty-ratio pulse signal into a stream processor cluster, carrying out the linear frequency sweeping, and generating a frequency space phase resonance diagram; collecting error count and retransmission frequency data during ray tracing rendering, and determining a target focusing area; the target area flow processor is configured to be a phase emission array to generate a steerable electrothermal acoustic wave beam, and an air gap defect area is determined; inputting a spread spectrum signal, applying a random flipping sequence, and calculating air gap defect three-dimensional parameters through a multi-physics field inversion model; and adjusting clock frequency according to the air gap parameters, controlling offset beams, adjusting power consumption of a stream processor, and executing a test to judge the qualification of the GPU. According to the method, the nano air gap defect in the GPU structure can be effectively detected, and the accuracy and reliability of GPU quality detection are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor integrated circuit testing, and in particular to a method for detecting a GPU chip and related equipment. Background Art

[0002] Modern GPUs (graphics processing units) are highly complex hybrid integrated systems, typically combining a large-area main chip (responsible for core computing and graphics processing) with key components such as multi-layer wiring structures, vertical through-hole connections, high-density micro-connections, and high-bandwidth memory stacks through advanced packaging technologies. This structure utilizes a multi-layer, three-dimensional stacking design, integrating tens of millions of computing units with hundreds of high-speed signal paths, power distribution networks, and cooling systems within a single package, enabling ultra-high-speed parallel computing and graphics processing capabilities.

[0003] Currently, GPU quality inspection primarily relies on conventional methods such as X-ray fluoroscopy, ultrasonic imaging, and basic electrical parameter testing under no-load conditions. However, these methods have significant limitations in detecting potential defects in hybrid integrated structures. As GPU integration continues to increase, particularly in advanced processes such as chip-on-chip packaging, extremely tiny air gaps (thin gaps measuring only 50-100 nanometers wide and hundreds of microns long, known as "nanogaps") may remain between the silicon chip layer and the circuit interconnect layer. These tiny air gaps barely affect heat transfer under normal power consumption and are therefore ineffective against conventional X-ray and ultrasonic inspections. However, when GPUs operate at high-frequency clock speeds of 8-12 GHz, these air gaps interact with the mechanical vibrations generated by the electronic components, forming tiny resonant cavities that amplify the high-frequency vibration waves. These amplified vibration waves can then be transmitted through the circuit interconnect layers to the logic processing units, potentially causing data bit flips under certain conditions. This defect is particularly susceptible to high-frequency ray tracing calculations (a graphics rendering technique) performed by the GPU when the rendered scene contains complex transparency and refraction effects, but is rarely visible under standard testing conditions. Due to the hidden nature of the association between such structural defects and functional abnormalities, traditional quality inspection methods have difficulty effectively identifying such potential risks, resulting in some GPUs experiencing difficult-to-reproduce image defects and performance fluctuations in high-load rendering scenarios. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing GPU quality detection method cannot effectively identify nano-scale air gap structure defects.

[0005] A first aspect of the present invention provides a method for detecting a GPU chip, the method comprising: Inject low-duty-cycle pulse signals into the stream processor cluster and perform linear frequency sweep processing to obtain power data, temperature rise data, and phase data, and generate a frequency-space phase resonance diagram; Based on the frequency-space phase resonance map, collecting a first error count matrix and retransmission count data when the stream processor cluster performs ray tracing rendering to determine a target focus area; configuring a stream processor cluster in the target focal area as a phased transmit array, controlling the phased transmit array to generate a steerable electrothermal acoustic beam, collecting power consumption data, a second error count matrix, and scattering spectrum data, and determining an area where an air gap defect exists; In the region where the air gap defect exists, a spread spectrum signal is input to the phased transmit array and a random flip sequence is applied to the storage bus to obtain temperature strain data, scattered phase data, and error count timing data, and calculate the three-dimensional parameters of the air gap defect; The clock frequency is adjusted according to the three-dimensional parameters of the air gap defect and the phased emission array is controlled to generate a cancellation beam, the power consumption of the stream processor cluster is adjusted, a ray tracing test is performed and test data is collected to determine whether the GPU is qualified.

[0006] Preferably, injecting a low duty cycle pulse signal into the stream processor cluster and performing linear frequency sweep processing to obtain power data, temperature rise data and phase data, and generating a frequency-space phase resonance diagram includes: Perform phase-shifted pulse injection processing on the partitioned stream processor cluster to obtain the partitioned power response matrix; According to the pulse injection timing, the clock frequency is linearly scanned and the regional temperature rise gradient matrix and phase delay matrix of each frequency point are collected; During the pulse injection interval, power data, temperature rise data, and phase data of the reference silent cluster are collected, and differential calibration processing is performed on the partition power response matrix, the regional temperature rise gradient matrix, and the phase delay matrix to generate a calibrated measurement matrix; The calibrated measurement matrix is ​​mapped according to the spatial index and the frequency index to generate a frequency-space phase resonance diagram.

[0007] Preferably, performing phase-shifted pulse injection processing on the partitioned stream processor cluster to obtain a partitioned power response matrix includes: Establish a pulse timing table for each partition stream processor cluster and set a fixed phase difference between adjacent partitions; Injecting low duty cycle pulse signals into the partitioned stream processor cluster in sequence according to the pulse timing table, and recording the injection index; Synchronously collect instantaneous power samples of the corresponding partition within each pulse injection window to generate a power sample group; The power sample groups are integrated according to the spatial index and the injection order to construct a partition power response matrix.

[0008] Preferably, the step of collecting a first error count matrix and retransmission count data based on the frequency-space phase resonance diagram when the stream processor cluster performs ray tracing rendering to determine a target focus area includes: Loading a periodic transparent-refractive ray tracing frame sequence to a stream processor cluster and synchronously calling frequency labels in the frequency-space phase resonance diagram, performing rotational scanning processing on a clock phase, and obtaining a first error count matrix and a retransmission count matrix that increase over time; Selecting reference non-textured frame data in a rendering sequence and performing time domain difference processing based on the first error count matrix and the retransmission number matrix to obtain a function domain difference anomaly matrix; Performing a three-dimensional convolution process on the functional domain differential anomaly matrix and the frequency-space phase resonance map according to spatial coordinates and time indexes to obtain a coupling peak matrix; A threshold cluster analysis is performed on the coupling intensity according to the coupling peak matrix, and a cluster center coordinate set is output as the target focus area.

[0009] Preferably, configuring the stream processor cluster in the target focal area as a phased transmit array, controlling the phased transmit array to generate a steerable electrothermal acoustic beam, collecting power consumption data, a second error count matrix, and scattering spectrum data, and determining an area where an air gap defect exists includes: Phase delay scheduling parameters are set for the stream processor cluster in the target focal area. The phased transmit array is driven according to the uniform angle and uniform frequency dual scanning scheme to generate an in-plane steered electrothermal acoustic beam, and the power ramp matrix corresponding to the scanning angle index and frequency index is obtained. During beam scanning, a scanning angle index and a rendering pipeline event timer are synchronously recorded, a second error count matrix and a radio frequency scatter spectrum matrix are collected, and the second error count matrix and the radio frequency scatter spectrum matrix are time-aligned according to the same angle index and frequency index; performing differential calibration processing on the power jump matrix, the second error count matrix, and the radio frequency scattering spectrum matrix to deduct background signals in non-target focus areas, thereby obtaining a calibrated multi-domain coupling matrix; Convolution correlation processing is performed according to the calibrated multi-domain coupling matrix to extract a coordinate set where the synchronous peak value of the power-error-scattering three domains exceeds a first threshold, and the area where the air gap defect exists is output through spatial clustering analysis.

[0010] Preferably, in the region where the air gap defect exists, a spread spectrum signal is input to the phased transmission array and a random flip sequence is applied to the storage bus to obtain temperature strain data, scattering phase data, and error count timing data, and calculate the three-dimensional parameters of the air gap defect, including: Inputting a spread spectrum code with a bandwidth equal to twice Δf based on the center frequency range into the region where the air gap defect exists and recording the spread spectrum index, driving the phase transmitting array to generate a broadband electrothermal acoustic beam according to the spread spectrum index, and acquiring scattered phase time series data corresponding to the spread spectrum index, where Δf represents an adjustable offset from the center frequency; Apply a random flip sequence synchronized with the spread spectrum index to the storage bus when the spread spectrum beam is in effect, and record the flip event address mapping and error count timing data; Synchronously collecting the temperature strain time series data output by the temperature strain array on the area where the air gap defect exists, and performing three-domain alignment processing on the scattering phase time series data, the error count time series data, and the flip event address mapping according to the spread spectrum index and the sampling timestamp to generate a multi-domain alignment data set; The multi-domain aligned dataset is input into an inversion model based on multi-physics field coupling, and an objective function with scattering phase offset, temperature strain amplitude, error count peak and flip event spatial distribution error as constraints is iteratively solved to output three-dimensional parameter data of air gap defects.

[0011] Preferably, the multi-domain aligned data set is input into an inversion model based on multi-physics field coupling, an objective function with scattering phase offset, temperature strain amplitude, error count peak and flip event spatial distribution error as constraints is iteratively solved, and three-dimensional parameter data of the air gap defect is output, including: A joint error function is constructed based on the scattering phase shift, temperature strain amplitude, error count peak and flip event spatial distribution; Set the air gap height, width, and length as the variables to be optimized, initialize the variable vector, and set the adaptive learning rate; Calculate the error function gradient in each iteration, update the variable vector according to the learning rate, and dynamically adjust the learning rate based on the convergence monitoring threshold; When each component of the error function is lower than the second threshold or the number of iterations reaches the upper limit, the current variable vector is output to obtain the three-dimensional parameter data of the air gap defect.

[0012] Preferably, adjusting the clock frequency according to the three-dimensional parameters of the air gap defect and controlling the phased transmit array to generate a cancellation beam, adjusting the power consumption of the stream processor cluster, performing a ray tracing test and collecting test data, and determining whether the GPU is qualified include: Performing vectorization processing on the three-dimensional parameter data of the air gap defect, generating a local clock offset vector and a phase cancellation template, and recording a corresponding compensation index; Writing the local clock offset vector into the target clock domain according to the compensation index, driving the phase transmitting array to transmit the cancellation beam according to the phase cancellation template, and collecting the beam stability index; Setting a load ratio coefficient of a target stream processor cluster according to the beam stability indicator, obtaining a power consumption adjustment plan, and collecting a power consumption matrix after adjustment; Running a transparent refraction ray tracing frame sequence test under the power consumption adjustment scheme, collecting a third error count matrix, a retransmission number matrix, a scattering spectrum matrix, and an adjusted power consumption matrix, and generating a retest indicator set; The retest indicator set is compared with the functional abnormality benchmark set before compensation by threshold value, and qualified data is generated when all indicators are lower than a third threshold value, otherwise a defect report is generated.

[0013] A second aspect of the present invention provides a GPU chip detection device, the GPU chip detection device comprising: The initial scanning module is used to inject a low-duty-cycle pulse signal into the stream processor cluster and perform linear frequency sweep processing to obtain power data, temperature rise data, and phase data, and generate a frequency-space phase resonance diagram; a target positioning module, configured to collect a first error count matrix and retransmission count data when the stream processor cluster performs ray tracing rendering based on the frequency-space phase resonance map, and determine a target focus area; a defect verification module, configured to configure the stream processor cluster in the target focus area into a phased transmit array, control the phased transmit array to generate a steerable electrothermal acoustic beam, collect power consumption data, a second error count matrix, and scattering spectrum data, and determine an area where an air gap defect exists; a parameter measurement module, configured to input a spread spectrum signal to the phased transmit array and apply a random flip sequence to a storage bus in the region where the air gap defect exists, obtain temperature strain data, scattered phase data, and error count timing data, and calculate three-dimensional parameters of the air gap defect; The quality judgment module is used to adjust the clock frequency according to the three-dimensional parameters of the air gap defect and control the phased emission array to generate a cancellation beam, adjust the power consumption of the stream processor cluster, perform ray tracing tests and collect test data, and determine whether the GPU is qualified.

[0014] A third aspect of the present invention provides a GPU chip detection device, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; the at least one processor calls the instructions in the memory so that the GPU chip detection device executes the steps of the above-mentioned GPU chip detection method.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the steps of the above-mentioned GPU chip detection method.

[0016] The technical solution provided in the embodiment of the present application, the specific detection process starts with injecting a low duty cycle pulse signal into the stream processor cluster and performing linear frequency sweep processing, obtaining power data, temperature rise data and phase data, and generating a frequency space phase resonance diagram. This step utilizes the physical phenomenon that the nano air gap exhibits unique resonance characteristics when the frequency changes. By applying phase-shifted pulses to each partitioned stream processor and synchronously scanning the clock frequency, the system can capture the response differences of the air gap to different frequency excitations. When the pulse signal frequency is close to the inherent resonant frequency of the air gap, the air gap area will produce significantly different power response and phase delay characteristics. These features are mapped to the frequency space after differential calibration processing, forming a panoramic picture reflecting the resonance characteristics of the internal structure of the chip, laying the foundation for subsequent positioning analysis.

[0017] Subsequently, error count matrices and retransmission count data are collected while the stream processor cluster executes ray tracing rendering to identify the target focus area. This step exploits the fact that nano-air gaps can trigger functional anomalies under high-frequency ray tracing calculations. By loading a sequence of transparent-refractive ray tracing frames while simultaneously performing a rotational scan of the clock phase, the system is able to capture the time-varying distribution of error events. These error events are then subjected to a three-dimensional convolution process with the frequency-spatial phase resonance map obtained in the previous step, effectively separating anomalies caused by air gaps from other random errors, thereby narrowing the suspected area to a more precise range.

[0018] Next, the stream processor cluster in the target focal area is configured as a phased transmit array, which is controlled to generate a steerable electrothermal acoustic beam. Power consumption data, error count matrix, and scattering spectrum data are collected to identify areas with air gap defects. This step reconfigures some stream processors into test tools, enabling them to generate directional electrothermal acoustic beams by precisely controlling phase delay parameters. When the beam hits the air gap area, the air gap amplifies the beam energy and produces significant responses simultaneously in three dimensions: power consumption, error count, and RF scattering. Through uniform angle and uniform frequency dual scanning and multi-domain signal alignment processing, the system can accurately locate the area where the air gap is located, improving detection accuracy to the order of hundreds of microns.

[0019] Subsequently, in the confirmed air gap defect area, a spread spectrum signal is input to the phased emission array and a random flip sequence is applied to the storage bus to obtain temperature strain data, scattering phase data and error count timing data, and the three-dimensional parameters of the air gap defect are calculated. The application of spread spectrum technology enables the system to simultaneously stimulate the response of the air gap at multiple frequencies, rather than the traditional single-frequency scanning, greatly improving the signal acquisition efficiency and signal-to-noise ratio. After synchronous acquisition and time alignment of multi-domain data, it is input into the inversion model based on multi-physics field coupling. The model uses scattering phase offset, temperature strain amplitude, error count peak and flip event spatial distribution as constraints. Through an iterative optimization algorithm, it can infer geometric parameters such as the height, width and length of the air gap, achieving a complete three-dimensional characterization of the defect with a resolution better than 10 nanometers.

[0020] Finally, based on the obtained three-dimensional parameters of the air gap defect, the clock frequency is adjusted and the phased transmit array is controlled to generate a cancellation beam. The power consumption of the stream processor cluster is adjusted, and a ray tracing test is performed and test data is collected to determine whether the GPU passes the test. This step not only verifies the accuracy of the previous test results but also provides an effective defect compensation strategy. By calculating the clock fine-tuning vector that deviates the air gap from the resonant condition and simultaneously generating a cancellation beam with opposite phase near the air gap area, the system can effectively suppress functional abnormalities caused by air gap resonance. The effectiveness of the compensation strategy is verified by running the same high-complexity rendering test as in the second step and comparing various performance indicators. If all indicators fall below the background noise level, the GPU is considered to have passed the test; otherwise, a detailed defect report is generated for subsequent analysis or repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0022] Figure 1 Schematic diagram of an embodiment of a method for detecting a GPU chip according to an embodiment of the present invention; Figure 2 Schematic diagram of an embodiment of a GPU chip detection device according to an embodiment of the present invention; Figure 3 Schematic diagram of an embodiment of a GPU chip detection device according to an embodiment of the present invention.

[0023] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0026] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, and must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0027] An embodiment of the present application provides a method for detecting a GPU chip. Figure 1 A flowchart of a method for detecting a GPU chip according to an embodiment of the present application is provided. In this embodiment, the method includes: See also Figure 1 ,Inject low duty cycle pulse signals into the stream processor cluster and perform linear frequency sweep processing to obtain power data, temperature rise data and phase data, and generate frequency space phase resonance diagram; In one embodiment of the present invention, injecting a low duty cycle pulse signal into the stream processor cluster and performing linear frequency sweep processing to obtain power data, temperature rise data, and phase data, and generate a frequency-space phase resonance diagram, includes: Perform phase-shifted pulse injection processing on the partitioned stream processor cluster to obtain the partitioned power response matrix; According to the pulse injection timing, the clock frequency is linearly scanned and the regional temperature rise gradient matrix and phase delay matrix of each frequency point are collected; During the pulse injection interval, power data, temperature rise data, and phase data of the reference silent cluster are collected, and differential calibration processing is performed on the partition power response matrix, the regional temperature rise gradient matrix, and the phase delay matrix to generate a calibrated measurement matrix; The calibrated measurement matrix is ​​mapped according to the spatial index and the frequency index to generate a frequency-space phase resonance diagram.

[0028] The following is a detailed description of the steps involved in the above embodiment: First, a stream processor cluster refers to a group of processing units within a GPU that performs parallel computations. It is physically divided into multiple spatial partitions. For example, a GPU with 4096 stream processors can be divided into 64 partitions. Off-phase pulse injection involves injecting pulse signals with a fixed time difference between adjacent partitions, typically set at a phase difference of 10-15 nanoseconds. In practice, low-power instruction pulses with a duty cycle of 5%-10% are injected into each partition sequentially through the GPU firmware interface according to a pre-established timing schedule. The duty cycle represents the percentage of the pulse duration over the total cycle. Within each pulse injection window (200-300 microseconds), on-chip power monitoring circuitry collects instantaneous power data for each partition. This data is then integrated according to the spatial coordinates and injection sequence to construct a partition power response matrix. The use of off-phase pulses, rather than in-phase pulses, avoids interference from global power supply fluctuations, enabling the power response characteristics of each partition to be accurately and independently captured, providing high-quality data for subsequent identification of localized anomalies.

[0029] Clock frequency linear sweeping is the process of changing the operating frequency of the GPU's built-in phase-locked loop (PLL) circuit at a constant rate. In practice, the sweep is performed at a linear rate of 10 MHz / s across the 7.5 GHz to 12.5 GHz frequency range, with 25 MHz steps and a settling time of 20 milliseconds at each frequency point. The frequency range is selected based on the relationship between the physical dimensions of the nanometer air gap and its resonant frequency. Air gaps of 50-100 nanometers typically resonate significantly in the 8-12 GHz frequency range. At each frequency point, out-of-phase pulse injection is repeated, while an on-chip temperature sensor array collects temperature data from each sub-region every 5 milliseconds. A temperature gradient matrix is ​​calculated in degrees Celsius per millisecond. This regional temperature gradient matrix reflects the thermal response characteristics of each sub-region at different frequencies. In parallel, an on-chip delay chain measurement circuit records the phase offset angle of each sub-region relative to the reference clock to form a phase delay matrix. This sweep ensures full coverage of the frequency spectrum that could trigger air gap resonance, accurately capturing the thermal and electrical response characteristics of each region during frequency changes.

[0030] The reference silent cluster refers to a stream processor region that is not actively activated during testing. It serves as a baseline for background noise and inherent system fluctuations. During the interval between each pulse injection (500-800 microseconds), a region at least two partition units away from the currently active partition is selected as the reference silent cluster, and power, temperature rise, and phase data are synchronously collected from this region in its idle state. Differential calibration eliminates environmental and inherent system noise by subtracting the reference value. Specifically, the power response of each partition at a specific frequency is subtracted from the power value of the reference silent cluster at the same frequency. The temperature rise gradient and phase delay data are processed similarly. For example, if the power value of a partition at 9.5 GHz is 125 mW, while the power value of the reference silent cluster at the same frequency is 115 mW, the calibrated value is 10 mW. This method effectively filters out interfering factors such as global background noise and temperature drift, increasing detection sensitivity from approximately 10 mW to approximately 1 mW, making subtle signal changes caused by the nano-air gap more clearly discernible.

[0031] The frequency-space phase resonance plot is a three-dimensional data visualization that shows the resonant response strength of different regions of the chip at various frequencies. The implementation first constructs a three-dimensional data structure, where the horizontal and vertical axes correspond to the physical locations of the GPU partitions, and the third dimension represents the frequency points during the frequency sweep. For each point in the data structure, a comprehensive resonance intensity value is calculated. This value is a weighted combination of the calibrated power, temperature rise gradient, and phase delay, with weight coefficients set to 0.4, 0.3, and 0.3, respectively, to balance the contributions of each physical quantity. For example, at a point with calibrated values ​​of 8 mW for power, 0.15°C / ms for temperature rise, and 12° for phase, the comprehensive resonance intensity is 8 × 0.4 + 0.15 × 100 × 0.3 + 12 × 0.3 = 8.2 (the temperature rise value is normalized by multiplying by 100 to account for unit differences). A spatial filtering algorithm is then applied to suppress random noise interference. Regions at each frequency level where the resonance intensity exceeds two standard deviations from the surrounding mean are identified and labeled as "potential scattering centers." This multi-dimensional information fusion method combines the different sensitivities of three physical quantities, power, temperature, and phase, to the air gap resonance, and generates a frequency-space phase resonance map with a resolution better than 100 μm. 2 The spatial resolution can accurately indicate areas inside the GPU where nano-air gap defects may exist, providing precise spatial positioning information for subsequent directional detection.

[0032] In one embodiment of the present invention, performing phase-shifted pulse injection processing on the partitioned stream processor cluster to obtain a partitioned power response matrix includes: Establish a pulse timing table for each partition stream processor cluster and set a fixed phase difference between adjacent partitions; Injecting low duty cycle pulse signals into the partitioned stream processor cluster in sequence according to the pulse timing table, and recording the injection index; Synchronously collect instantaneous power samples of the corresponding partition within each pulse injection window to generate a power sample group; The power sample groups are integrated according to the spatial index and the injection order to construct a partition power response matrix.

[0033] The following is a detailed description of the steps involved in the above embodiment: The stream processor cluster is divided into multiple spatial partitions based on the GPU's physical layout. For example, a GPU with 4096 stream processors can be evenly divided into 64 partitions, each containing 64 stream processors. A pulse timing table is a data table that specifies the temporal order and phase relationship of pulse signals received by each partition. It contains three key parameters: partition identifier, pulse start time, and pulse duration. When creating the timing table, each partition is assigned a unique injection time point using the GPU firmware programming interface, and a fixed 12 nanosecond phase difference is set between adjacent partitions. This 12 nanosecond phase difference is chosen based on the relationship between the internal GPU signal propagation speed and the physical distance between partitions. This value is less than the inter-partition signal crosstalk threshold (approximately 20 nanoseconds) and greater than the system clock jitter (approximately 5 nanoseconds), ensuring that response signals from adjacent partitions do not interfere with each other and maintain timing continuity. Setting a fixed phase difference ensures that the response signals from the partitions exhibit a regular distribution in the time domain, facilitating subsequent separation of the partition signals and extraction of subtle anomalies.

[0034] A low-duty-cycle pulse signal is one in which the high-level duration within a cycle is a small fraction of the total cycle. This method uses a pulse signal with an 8% duty cycle. Through the GPU's firmware control interface, a specific instruction sequence is sent to each partition in sequence according to the pulse timing table established in the previous step, causing the target partition to enter a high-activity state for a short period of time. In specific implementations, each pulse signal duration is set to 20 microseconds, with a pulse interval of 230 microseconds, resulting in a low-duty-cycle operating mode with a total cycle of 250 microseconds. The injection index is a data item that records the sequence number of the pulse signal injection event and the corresponding partition identifier. It contains the partition coordinates (x, y), the injection timestamp t, and the sequence number n. For example, for an 8×8 partition layout, the injection index for the top-left partition is recorded as {(0, 0), 10:15:30.000123, 1}, indicating that the partition is located at coordinates (0, 0) and received the first pulse signal at timestamp 10:15:30.000123. The low 8% duty cycle design was chosen based on two factors: a lower duty cycle can reduce the impact of sustained high power consumption on the chip temperature and prevent the thermal accumulation effect from masking the subtle thermal response characteristics; at the same time, the pulse interval is long enough to ensure that each partition has fully recovered to a stable state before the next pulse arrives.

[0035] The pulse injection window refers to the period from the start to the end of the pulse signal. Instantaneous power sampling refers to the high-frequency sampling of the partition's power consumption within this window. Using the GPU's built-in power monitoring circuitry, the power of the currently active partition is sampled at 1-microsecond intervals within each 20-microsecond pulse injection window, obtaining 20 power data points. To ensure sampling accuracy, a high-precision analog-to-digital converter is used to acquire the power signal, with a resolution of 0.1 milliwatts and a sampling frequency of 1 MHz. A power sample group is a data set consisting of all power sampling points collected for the same partition within a pulse injection window, represented as a set of power values ​​at 20 sampling time points. The 1-microsecond sampling interval was chosen because the power fluctuation characteristics caused by nano-air gaps typically have a timescale of 3-5 microseconds. According to the Nyquist sampling theorem, the sampling frequency must be at least twice the characteristic frequency. Therefore, a sampling frequency of 1 MHz is sufficient to capture these subtle power fluctuations.

[0036] The spatial index refers to the two-dimensional coordinate position of a partition within the GPU's physical layout, while the injection order refers to the temporal sequence of pulse signal injection. A data processing program organizes the power sample groups for each partition obtained in the previous step according to their spatial coordinates (x, y) and injection sequence number n, forming a three-dimensional data structure. The partition power response matrix is ​​a comprehensive data matrix containing the power response data for all partitions within their respective pulse injection windows, recording power values ​​in three dimensions: coordinates, injection order, and time point. For example, for an 8×8 partition layout, 10 injection cycles are performed, with 20 time points collected each time. The resulting partition power response matrix is ​​8×8×10×20, containing 12,800 data points. This matrix organization unifies spatial and temporal information in a structured dataset, enabling subsequent analysis of the power response characteristics of each partition from multiple dimensions and effectively identifying areas of anomalous response. Specifically for detecting nano-air gap defects, the spatiotemporal correlation information contained in this matrix can reveal unique power response patterns in the air gap region at specific pulse frequencies, such as increased power fluctuation amplitude or prolonged decay time.

[0037] Please continue reading Figure 1 , based on the frequency-space phase resonance map, collecting a first error count matrix and retransmission count data when the stream processor cluster performs ray tracing rendering, and determining a target focus area; In one embodiment of the present invention, the step of collecting a first error count matrix and retransmission count data based on the frequency-space phase resonance map when the stream processor cluster performs ray tracing rendering to determine a target focus area includes: Loading a periodic transparent-refractive ray tracing frame sequence to a stream processor cluster and synchronously calling frequency labels in the frequency-space phase resonance diagram, performing rotational scanning processing on a clock phase, and obtaining a first error count matrix and a retransmission count matrix that increase over time; Selecting reference non-textured frame data in a rendering sequence and performing time domain difference processing based on the first error count matrix and the retransmission number matrix to obtain a function domain difference anomaly matrix; Performing a three-dimensional convolution process on the functional domain differential anomaly matrix and the frequency-space phase resonance map according to spatial coordinates and time indexes to obtain a coupling peak matrix; A threshold cluster analysis is performed on the coupling intensity according to the coupling peak matrix, and a cluster center coordinate set is output as the target focus area.

[0038] The following is a detailed description of the steps involved in the above embodiment: A periodic transparent-refractive ray tracing frame sequence is a set of rendering scenes looped at specific intervals to test the GPU's ability to handle complex light behavior. These scenes contain numerous computational tasks that require calculating the bending (refraction) effects of light passing through transparent objects. This specialized rendering scenario puts the GPU under high load, stimulating the resonance effect of the nano-air gap. In implementation, a test sequence consisting of 30 consecutive frames is loaded through the GPU driver. Each frame features 15-20 transparent objects with varying refractive indices (1.2-2.4) (such as glass, water, and diamonds), along with a multi-layered cascade of ray refraction paths (4-8 levels). This ensures that the ray requires multiple calculations of refraction direction changes within a single rendering pass. Frequency labels are a set of primary resonant frequency values ​​corresponding to each region in the frequency-space phase resonance diagram. These labels record the frequency points at which each region exhibited significant resonance effects in the previous step. During the synchronization call, the frequency values ​​corresponding to regions marked as "potential scattering centers" are extracted to form a set of frequency labels. Rotational scanning processing involves sequentially varying the phase angle of a clock signal while maintaining a constant frequency. The on-chip phase-locked loop (PLL) control circuitry cyclically adjusts the clock phase of each partition within a range of 0°-360° in 15° steps, maintaining each angular position for 3 milliseconds. During the scanning process, the GPU's built-in error detection and correction circuitry and texture cache monitor collect error counts and texture retransmission counts for each partition every 1 millisecond, forming two data matrices that increment over time. This method effectively stimulates the resonance effect of the air gap under high-frequency operating conditions. When the phase wave interacts with the tiny resonant cavity formed by the air gap, it causes bit-flip errors in the local logic circuits, making the air gap defect area appear as a distinct anomaly in the data matrix.

[0039] Reference textureless frames are simplified rendering frames intentionally inserted into the test sequence, lacking complex texture mapping and transparency-refraction calculations. These frames maintain the same geometry and lighting conditions, but remove specific computational overhead that triggers air gap resonance. Specifically, a reference textureless frame is inserted every five frames in a 30-frame test sequence. These reference frames replace all materials with simple, monochromatic opaque materials, removing all transparency and refraction calculations. Temporal differencing involves subtracting data from a normal complex rendering frame and an adjacent reference textureless frame, acquired under the same conditions, to eliminate background noise and basic errors caused by general computations. For each partition coordinate (x, y) and phase angle θ, the formula is: Difference value (x, y, θ) = Number of complex frame errors (x, y, θ) - Number of reference frame errors (x, y, θ). The functional domain difference anomaly matrix is ​​a data set that reflects the additional errors incurred by each partition when performing specific functional operations (transparency-refraction calculations) after temporal differencing. The matrix contains two components: the difference in error counts and the difference in retransmission counts, each organized by partition coordinates and phase angles. This differential processing method accurately isolates anomalies that only appear during transparent-refractive ray tracing calculations, filtering out background errors caused by typical computational loads. This increases detection sensitivity from approximately 50 times / second to approximately 5 times / second, significantly improving the accuracy of identifying nano-air gap defects.

[0040] Three-dimensional convolution is a mathematical operation that analyzes the local correlation between two data structures with spatial and temporal dimensions, used to discover spatiotemporal patterns in the data. In this step, the functional domain differential anomaly matrix (reflecting functional anomalies) and the frequency-space phase resonance map (reflecting physical property anomalies) are aligned according to a common spatial coordinate system, and then a three-dimensional convolution operation is performed. Specifically, a 3×3×3 convolution kernel is set with the highest weight (1.0) at the center and gradually decreasing weights (0.8, 0.5, 0.3) outward. This kernel is moved across the entire data space, and a local weighted sum is calculated. For each local region covered by the convolution kernel, the weighted product of the functional domain differential anomaly value and the phase resonance strength is calculated. The coupling peak matrix is ​​a data matrix generated after the three-dimensional convolution process, representing the spatial-temporal correlation strength between functional anomalies and physical resonance phenomena. High-value regions in this matrix indicate locations that simultaneously meet two conditions: physical resonance and functional error rate anomalies. The convolution kernel size of 3×3×3 was chosen based on experimental observations that the physical influence of nanoscale air gaps typically spans two to three adjacent partitions. This three-dimensional convolution method, combining physical properties with functional performance, accurately identifies systematic anomalies caused by air gaps, effectively eliminating random errors and anomalies caused by factors other than air gaps, and reducing the false positive rate from approximately 8% to approximately 1.5%.

[0041] Threshold cluster analysis is a data processing method that sets a threshold based on data values ​​and groups spatially adjacent data points that exceed the threshold into the same category. In specific implementation, the numerical distribution of the coupling peak matrix is ​​first statistically analyzed, and the global mean μ and standard deviation σ are calculated. A threshold T = μ + 2.5σ is set. All points with values ​​greater than the threshold T are extracted to form a set of candidate outliers. A density-based spatial clustering algorithm is then applied to group candidate outliers with a Euclidean distance less than two partition units into the same cluster. Finally, the geometric center of each cluster (the average x-coordinate and the average y-coordinate of each point) is calculated to obtain a set of cluster center coordinates. The target focus area is the GPU area requiring fine-grained inspection. It is determined by defining a circular area with a radius of three partition units centered on each cluster center. For example, if the cluster analysis results in a cluster center with coordinates (12, 15), the corresponding target focus area covers the partitions within the rectangular area from (9, 12) to (15, 18). The 2.5σ threshold was chosen based on its correlation with a false alarm rate of approximately 0.62% in a normal distribution, maintaining high detection sensitivity while keeping false alarms within an acceptable range. This method consolidates discrete outliers into physically continuous regions, reducing the influence of random noise and ensuring that detection results are more consistent with the physical distribution characteristics of nano-air gap defects. Spatial positioning accuracy is improved from millimeters to microns, providing accurate spatial positioning information for subsequent precision inspection and repair.

[0042] Please continue reading Figure 1 , configuring the stream processor cluster in the target focal area into a phased transmit array, controlling the phased transmit array to generate a steerable electrothermal acoustic beam, collecting power consumption data, a second error count matrix, and scattering spectrum data, and determining an area where an air gap defect exists; In one embodiment of the present invention, configuring the stream processor cluster in the target focal area as a phased transmit array, controlling the phased transmit array to generate a steerable electrothermal acoustic beam, collecting power consumption data, a second error count matrix, and scattering spectrum data, and determining an area where an air gap defect exists includes: Phase delay scheduling parameters are set for the stream processor cluster in the target focal area. The phased transmit array is driven according to the uniform angle and uniform frequency dual scanning scheme to generate an in-plane steered electrothermal acoustic beam, and the power ramp matrix corresponding to the scanning angle index and frequency index is obtained. During beam scanning, a scanning angle index and a rendering pipeline event timer are synchronously recorded, a second error count matrix and a radio frequency scatter spectrum matrix are collected, and the second error count matrix and the radio frequency scatter spectrum matrix are time-aligned according to the same angle index and frequency index; performing differential calibration processing on the power jump matrix, the second error count matrix, and the radio frequency scattering spectrum matrix to deduct background signals in non-target focus areas, thereby obtaining a calibrated multi-domain coupling matrix; Convolution correlation processing is performed according to the calibrated multi-domain coupling matrix to extract a coordinate set where the synchronous peak value of the power-error-scattering three domains exceeds a first threshold, and the area where the air gap defect exists is output through spatial clustering analysis.

[0043] The following is a detailed description of the steps involved in the above embodiment: Phase delay scheduling parameters refer to a set of parameters that control the operation of each unit in a stream processor cluster according to a specific time difference sequence, which is used to produce a directional interference effect. In specific implementations, the stream processor cluster in the target focal area is configured as a phased transmit array—an array of processing units that can collaboratively generate directional electrothermal acoustic waves. During configuration, each stream processor unit is assigned an independent clock delay value through the GPU firmware interface. The delay value is calculated based on the physical layout coordinates: delay value (x, y) = (x·cosθ+y·sinθ)·d / v, where θ is the target direction angle, d is the unit spacing, and v is the acoustic wave propagation velocity in the silicon wafer. The uniform angle and uniform frequency dual scanning scheme is a method that performs a two-dimensional parameter scan using uniform angle steps and uniform frequency steps. In specific implementations, the angle direction ranges from 0° to 180° in 5° steps (a total of 37 angle points), and the frequency direction ranges from 8 GHz to 12 GHz in 100 MHz steps (a total of 41 frequency points). An in-plane steered electrothermal acoustic beam is a thermal-mechanical-electrical coupled wave that propagates in a set direction within the chip plane. During the electrothermal acoustic beamforming process, the stream processor units in the phased transmit array alternately execute high-load instructions according to a set phase delay sequence, generating periodic temperature fluctuations and mechanical stresses. These fluctuations constructively interfere in a specific direction, forming a directional beam. The power jump matrix is ​​a data matrix that records the amplitude of sudden power increases at different locations at various scanning angles and frequencies. For example, at an angle of 45° and a frequency of 10.5 GHz, if the power at a certain location suddenly increases from a baseline value of 115 mW to 135 mW, the power jump value is 20 mW. This angle-frequency dual-parameter scanning method can utilize the directional and frequency correlation interaction characteristics of the electrothermal acoustic beam and the air gap. When the beam direction is parallel to the long axis of the air gap and the frequency is close to the natural resonant frequency of the air gap, a significant power jump will occur in the air gap area, greatly improving the detection's directional recognition capability and frequency resolution.

[0044] The scan angle index is the serial number identifying the angle parameter during the dual scan process, and the render pipeline event timer is a timing device that records the occurrence time of each event in the GPU graphics rendering pipeline. During implementation, the GPU event tagging mechanism records the current angle index and frequency index during each angle-frequency configuration, and these index values ​​are associated with the timestamp of the render pipeline event timer. The second error count matrix is ​​a data matrix that records the number of errors occurring in each partition during the electrothermal acoustic beam scan. This data is collected by the GPU's built-in error detection and correction circuitry. The RF scattering spectrum matrix is ​​a data matrix that records the RF signal scattering characteristics of the chip surface at different angles and frequencies. This data is collected by a micro RF detector array placed above the GPU package. The RF detector array consists of 16×16 micro antenna elements operating at a frequency of 5-15 GHz, which monitor the electromagnetic scattering characteristics of the chip surface in real time. Time alignment is the process of matching and integrating data from different sources corresponding to the same angle and frequency parameters based on timestamps. In specific implementation, a data table is established with angle index i and frequency index j as primary keys. Data with the same index (i, j) in the second error count matrix E(i, j, x, y) and the RF scatter spectrum matrix S(i, j, x, y) are organized into the same data entry. This synchronized measurement and time alignment method ensures that multi-domain data is acquired under the same conditions, accurately capturing the correlation between different physical phenomena and enhancing the ability to identify air gap defect characteristics.

[0045] Differential calibration refers to a data processing method that improves signal quality by subtracting a reference background signal. In practice, an area away from the target focus area is selected as the reference background area, and the power, error count, and RF scattering data of this area are collected at the same angle and frequency configuration, which are denoted as P bg(i,j) 、E bg(i,j) and S bg(i,j) , where i is the angle index and j is the frequency index. Then perform a subtraction operation on the three original data matrices: P cal(i,j,x,y) =P(i,j,x,y)-P bg(i,j) , E cal(i,j,x,y) =E(i,j,x,y)-E bg(i,j) , S cal(i,j,x,y) =S(i,j,x,y)-S bg(i,j) , and obtain the three calibrated matrices. The multi-domain coupling matrix is ​​the result of integrating the calibrated power jump, error count and RF scattering matrices into a unified data structure, expressed as M(i,j,x,y)=[P cal(i,j,x,y) ,E cal(i,j,x,y) ,S cal(i,j,x,y)], recording calibration values ​​for power, error, and scatter at each angle, frequency, and spatial position. This matrix intuitively reflects the multi-domain response characteristics of different positions under various scanning conditions. This differential calibration process effectively eliminates the influence of systematic background interference, such as global temperature drift and power supply fluctuations, allowing the calibrated multi-domain coupling matrix to demonstrate the multi-physics coupling characteristics unique to air gap defects with a higher signal-to-noise ratio.

[0046] According to the calibrated multi-domain coupling matrix, convolution correlation processing is performed to extract the coordinate set where the synchronous peak value of the power-error-scattering three domains exceeds the first threshold, and the specific implementation method of outputting the area where the air gap defect exists is as follows: Convolution correlation processing refers to a data processing method that calculates the correlation between multiple signals through convolution operation. In specific implementation, the three components of the calibrated multi-domain coupling matrix M(i,j,x,y) are normalized so that the numerical range of each component is unified to the [0,1] interval, and then the three-domain correlation strength is calculated: C(i,j,x,y)=P norm(i,j,x,y) ×E norm(i,j,x,y) ×S norm(i,j,x,y) , where P norm 、E norm 、S norm They are the normalized power, error and scattering components respectively. The three-domain synchronous peak refers to the phenomenon that the three physical quantities of power, error and scattering change significantly at the same time, which is manifested as a position with a larger C(i,j,x,y) value. The first threshold is set to 0.6, that is, when the three-domain correlation intensity C value exceeds 0.6, it is judged as a valid peak point, and its coordinates (x,y) and the corresponding angle index i and frequency index j are recorded to form a coordinate set. Spatial clustering analysis refers to a data processing method that groups similar data points into a group based on spatial position relationships. A hierarchical clustering algorithm is applied to the extracted coordinate set, and the cluster distance threshold is set to 2 partition units. Peak points with similar spatial positions are grouped into the same cluster. The density center and main response angle and frequency characteristics of each cluster are calculated, and finally the area with air gap defects is output, which is represented as a set of regional descriptors: {(x c ,y c ),θ main ,f main ,r}, where (x c ,y c ) is the coordinate of the center of the region, θ main is the main response angle, f main is the main response frequency, and r is the region radius. This multi-domain correlation analysis method can simultaneously consider the coupling relationship between different physical phenomena, greatly improving the specificity of detection. It also provides preliminary estimates of the direction and size of the air gap, laying the foundation for subsequent precise characterization.

[0047] Please continue reading Figure 1 , in the region where the air gap defect exists, inputting a spread spectrum signal to the phased transmit array and applying a random flip sequence to the storage bus, acquiring temperature strain data, scattering phase data, and error count timing data, and calculating the three-dimensional parameters of the air gap defect; In one embodiment of the present invention, in the region where the air gap defect exists, a spread spectrum signal is input to the phased transmit array and a random flip sequence is applied to the storage bus to obtain temperature strain data, scattering phase data, and error count timing data, and calculate the three-dimensional parameters of the air gap defect, including: Inputting a spread spectrum code with a bandwidth equal to twice Δf based on the center frequency range into the region where the air gap defect exists and recording the spread spectrum index, driving the phase transmitting array to generate a broadband electrothermal acoustic beam according to the spread spectrum index, and acquiring scattered phase time series data corresponding to the spread spectrum index, where Δf represents an adjustable offset from the center frequency; Apply a random flip sequence synchronized with the spread spectrum index to the storage bus when the spread spectrum beam is in effect, and record the flip event address mapping and error count timing data; Synchronously collecting the temperature strain time series data output by the temperature strain array on the area where the air gap defect exists, and performing three-domain alignment processing on the scattering phase time series data, the error count time series data, and the flip event address mapping according to the spread spectrum index and the sampling timestamp to generate a multi-domain alignment data set; The multi-domain aligned dataset is input into an inversion model based on multi-physics field coupling, and an objective function with scattering phase offset, temperature strain amplitude, error count peak and flip event spatial distribution error as constraints is iteratively solved to output three-dimensional parameter data of air gap defects.

[0048] The following is a detailed description of the steps involved in the above embodiment: A spreading code is a modulation sequence that spreads a single frequency signal over a wider bandwidth. This scheme uses a pseudo-random sequence with a length of 127. The center frequency range refers to the air gap main response frequency determined in the previous step, typically between 9 GHz and 11 GHz. Δf represents the adjustable offset from the center frequency, set to 500 MHz. A bandwidth equal to twice Δf means the signal covers a frequency range of ±500 MHz from the center frequency, for a total bandwidth of 1 GHz. In implementation, a spreading code is first generated based on the determined center frequency f0 of the air gap defect region. This code is a pseudo-random code consisting of 127 1s and -1s. The spreading index is the position identifier within the spreading sequence and ranges from 0 to 126. Taking a center frequency of 10 GHz as an example, the generated spread spectrum signal will cover a continuous frequency band from 9.5 GHz to 10.5 GHz. A broadband electrothermal acoustic beam is a directional acoustic energy beam with a spectral coverage of a certain width, generated by a phased transmitting array. In specific implementation, the spread spectrum code sequence is loaded into the phase transmit array controller in index order through the GPU firmware interface, and each spread spectrum index corresponds to a frequency point. The stream processor in the phase transmit array performs pulse operation of different frequencies according to the timing of the spread spectrum code, and the thermal vibrations generated form a directional superposition in space, forming a broadband electrothermal acoustic beam. The scattered phase timing data refers to the time series data that records the phase changes of the scattered signal generated after the electrothermal acoustic beam interacts with the air gap. The scattered signal is collected in real time by the RF detector array placed on the surface of the GPU chip, and the phase change corresponding to each spread spectrum index is recorded. The sampling frequency is set to 10MHz. The design using spread spectrum technology instead of single-frequency technology enables the detection system to simultaneously stimulate the response characteristics of the air gap at different frequencies.

[0049] The memory bus is the data transmission channel connecting the GPU's internal computing units and memory units. A random flip sequence is a sequence of operations that randomly changes data bit values ​​(from 0 to 1 or 1 to 0) according to a specific probability distribution. In implementation, a 127-bit random sequence with the same length as the spreading code is first generated, with each bit value being 0 or 1, indicating whether a flip operation is to be performed at the corresponding moment. This random sequence is loaded into the GPU memory controller and synchronized with the spreading index. When the spreading index is i, if the i-th bit of the random sequence is 1, a voltage perturbation is applied to the memory bus, causing the data bits in the specific address region to flip. The flip event address map is a data structure that records the correspondence between the physical and logical memory addresses where data bit flip operations occur. For example, at spreading index 45, if the random sequence value is 1, a flip operation is performed in the physical address region 0x8A000000 to 0x8A000FFF, and the corresponding GPU internal structure coordinates are recorded. Error count time series data is a data sequence that records the number of errors occurring in each region over time. The GPU's built-in error detection and correction circuitry collects error counter values ​​for each partition every 100 nanoseconds, following the spread spectrum index sequence. This generates a time-indexed sequence of error data. This random flipping design, combined with electrothermal acoustic beam excitation, accurately measures the impact of air gap defects on data integrity and their spatial distribution. When the air gap is resonant, even small voltage disturbances are amplified, making nearby data bits more susceptible to errors and highlighting the spatial and temporal signature of the air gap defect.

[0050] The temperature-strain array refers to a sensor network distributed across the surface of the GPU chip for measuring local temperature changes and mechanical deformation. Temperature-strain time-series data refers to a data sequence that records temperature and strain changes over time. In specific implementation, an 8×8 array of micro-temperature sensors and strain sensors is deployed in the air gap defect area, with a temperature sensor resolution of 0.01°C and a strain sensor resolution of 0.5 nanometers. During spread-spectrum beam excitation, temperature and strain data are synchronously collected at a sampling frequency of 5MHz, recording the temperature change rate (°C / ms) and strain amplitude (nanometers) corresponding to each spread-spectrum index. Three-domain alignment processing is a data processing method that matches and integrates time-series data from different sources according to a common time base, using coupling characteristics to more comprehensively represent the physical characteristics of the defect. In specific implementation, the scattering phase time series data P(i,t), error count time series data E(i,t), temperature strain time series data T(i,t), and flip event address mapping A(i,t) are time-synchronized and integrated using the spread spectrum index i and sampling timestamp t as primary keys, resulting in a multidimensional dataset D(i,t) = {P(i,t), E(i,t), T(i,t), A(i,t)}. A multi-domain aligned dataset is a comprehensive data set obtained through alignment that contains synchronized information on multiple physical quantities. For example, for spread spectrum index i = 45 and timestamp t = 10.25ms, the aligned dataset includes the scattering phase value of 34°, error count increment of 16, temperature change rate of 0.42°C / ms, strain amplitude of 2.8nm, and the corresponding flip address mapping information. This multi-domain synchronous acquisition and data alignment method accurately captures the air gap defect in four domains: electromagnetic, thermal, mechanical, and functional, enhancing the reliability of subsequent parameter inversion.

[0051] The inversion model based on multi-physics coupling is a computational method that combines the interactions among electromagnetic, thermal, mechanical, and functional fields to infer the physical parameters of defects from measured data. This model, based on finite element analysis, considers the quantitative relationship between the air gap geometry and the responses of various physical fields. The objective function is a mathematical expression that reflects the degree of discrepancy between the model's predicted and measured values. The scattering phase offset refers to the difference between the scattered signal phase and the incident phase; the temperature strain amplitude refers to the mechanical deformation caused by temperature changes; the error count peak refers to the maximum number of errors; and the flip event spatial distribution error refers to the deviation between the spatial distribution of the flip-induced errors and the predicted distribution. In specific implementation, the objective function J=w1×J1+w2×J2+w3×J3+w4×J4 is first constructed based on the physical model of the air gap. J1 to J4 are the mean squared error terms of the four physical quantities, and w1 to w4 are weight coefficients, set to 0.3, 0.25, 0.25, and 0.2, respectively. The iterative solution process utilizes a gradient descent method, with the air gap height h, width w, and length l as optimization variables, with initial values ​​set to h0 = 80 nm, w0 = 2 μm, and l0 = 300 μm. In each iteration, the theoretical response is calculated based on the current parameter values, the error is compared with the measured data, and the parameter values ​​are then updated along the error gradient. The iterative step size is initially set to 10 nm, and when the error change is less than 1% over three consecutive iterations, the step size is halved. When the total error falls below a preset threshold (0.05) or the number of iterations reaches the upper limit (100), the current parameter values ​​are output as the three-dimensional parameter data of the air gap defect. This multi-physics coupled inversion method fully utilizes the mutual constraints and complementary information between different physical fields, resolving the parameter ambiguity problem in traditional single-physics inversion, achieving nanometer-level measurement accuracy for air gap geometric parameters, with height measurement accuracy of approximately ±5 nm and length and width measurement accuracy of approximately ±50 nm.

[0052] In one embodiment of the present invention, the multi-domain aligned dataset is input into an inversion model based on multi-physics field coupling, and an objective function with scattering phase offset, temperature strain amplitude, error count peak, and flip event spatial distribution error as constraints is iteratively solved to output three-dimensional parameter data of the air gap defect, including: A joint error function is constructed based on the scattering phase shift, temperature strain amplitude, error count peak and flip event spatial distribution; Set the air gap height, width, and length as the variables to be optimized, initialize the variable vector, and set the adaptive learning rate; Calculate the error function gradient in each iteration, update the variable vector according to the learning rate, and dynamically adjust the learning rate based on the convergence monitoring threshold; When each component of the error function is lower than the second threshold or the number of iterations reaches the upper limit, the current variable vector is output to obtain the three-dimensional parameter data of the air gap defect.

[0053] The following is a detailed description of the steps involved in the above embodiment: The joint error function is a mathematical expression that integrates the differences between multiple physical quantities and is used to quantify the degree of deviation between theoretical predictions and actual measurements. In specific implementation, the four measurement data items—scattering phase offset, temperature strain amplitude, error count peak, and flip event spatial distribution—are first normalized to bring the values ​​of each physical quantity into the [0, 1] range. The scattering phase offset is the difference between the phase of the scattered signal generated by the interaction of the electrothermal acoustic wave beam with the air gap and the incident phase, expressed in degrees. The temperature strain amplitude is the mechanical deformation of the chip surface caused by temperature changes, expressed in nanometers. The error count peak is the maximum number of errors recorded in the test sequence, expressed in times. The flip event spatial distribution is the distribution of data bit flip events in the GPU spatial coordinate system. A weighted sum joint error function is then constructed, in which the mean squared error terms of the four physical quantities are combined with different weights. The weighting coefficients were chosen to account for the varying measurement accuracy and sensitivity of each physical quantity to air gap parameters: Scattering phase shift is most sensitive to air gap geometry and therefore receives the highest weight of 0.3; temperature strain and error counts are less sensitive to air gap characteristics and receive a moderate weight of 0.25; the spatial distribution of flip events is influenced by multiple factors and is relatively less sensitive, receiving a weight of 0.2. This weighted combination of multiple physical quantities leverages the complementarity of data from different physical fields, mitigating potential parameter estimation biases introduced by a single physical quantity.

[0054] The variables to be optimized are the parameters that need to be adjusted during the optimization process to minimize the error function. The air gap height refers to the dimension of the air gap perpendicular to the chip plane, measured in nanometers; the air gap width refers to the dimension of the air gap along its minor axis within the chip plane, measured in micrometers; and the air gap length refers to the dimension of the air gap along its major axis within the chip plane, measured in micrometers. The variable vector is a vector representation of these three parameters, consisting of three components: height, width, and length. During initialization, based on the physical properties of typical nanogaps and the preliminary estimates obtained in the previous steps, the initial components of the variable vector were set to 80 nanometers, 2 micrometers, and 300 micrometers, respectively. These initial values ​​are based on engineering experience with the characteristic dimensions of nanogaps: height is typically in the range of 50-100 nanometers, width is in the range of 1-5 micrometers, and length is in the range of 100-500 micrometers. The adaptive learning rate is a step size parameter that can be dynamically adjusted during the optimization iterations based on convergence. The initial learning rates were set to 10 nanometers (height), 0.1 micrometers (width), and 5 micrometers (length), representing the maximum adjustment range for each parameter in each iteration. The initial learning rate setting takes into account the differences in the order of magnitude of the parameters and their varying sensitivities to the error function, avoiding the convergence instability that can result from using a uniform learning rate. This approach of directly parameterizing the air gap geometry simplifies the problem complexity and makes the optimization process more efficient.

[0055] The error function gradient refers to the vector of partial derivatives of the error function with respect to each optimized variable, representing the direction and rate of change of the error function at the current point. The specific calculation uses the finite difference method, making small perturbations to each variable. The change in the error function is calculated to obtain an approximate gradient. For example, the partial derivative of the air gap height is calculated by increasing the current height by a small amount (e.g., 0.1% of the current value), keeping the width and length unchanged, calculating the change in the error function, and then dividing by this small amount to obtain the gradient component in the height direction. The variable vector is updated using the gradient descent method, which subtracts the product of the learning rate and the gradient from the current variable value to obtain the new variable value. Convergence monitoring thresholds are criteria used to determine whether the optimization process has converged, and include error change rate thresholds and gradient norm thresholds. The dynamic learning rate adjustment strategy is as follows: if the error change rate over three consecutive iterations is very small (less than 1%), the learning rate is halved; if the error shows an increasing trend over three consecutive iterations, the learning rate is reduced to 1 / 4 of the original value; if the gradient norm is very small (less than 0.001), the learning rate is increased by 50%. This adaptive learning rate adjustment mechanism allows for a faster approach to the optimal solution using a larger step size during the initial optimization phase, while automatically reducing the step size to improve accuracy once the optimal solution is near. This significantly enhances the stability and accuracy of the air gap parameter inversion.

[0056] The components of the error function refer to the four sub-error terms in the joint error function, corresponding to the errors in the scattering phase offset, temperature strain amplitude, peak error count, and spatial distribution of flip events. The second threshold is a standard value for determining whether the optimization process has achieved sufficient accuracy. Different thresholds are set for each of the four sub-error terms: 0.05 for the scattering phase offset error, 0.08 for the temperature strain amplitude and peak error count error, and 0.1 for the spatial distribution of flip events. The upper limit on the number of iterations is the maximum number of iterations in the optimization process, set to 100 to prevent the algorithm from falling into a local optimum or experiencing prolonged periods of non-convergence. In implementation, after each iteration, a check is performed to see whether all four sub-error terms are below the corresponding second threshold or whether the current number of iterations has reached the upper limit. If any of these conditions are met, the iteration is terminated, and the current variable vector is output as the three-dimensional parameter data for the air gap defect. For example, after 68 iterations, if the four sub-error terms are 0.03, 0.06, 0.04, and 0.05, respectively, the final air gap parameters are 78 nanometers in height, 2.3 microns in width, and 285 microns in length. The second threshold is set based on the measurement accuracy and noise level of each physical quantity. The scattering phase measurement has higher accuracy, so a lower threshold is set; temperature strain and error counts are more susceptible to environmental factors, so a slightly higher threshold is set; and the spatial distribution of flip events is most influenced by random factors, so the highest threshold is set. This design of multiple threshold criteria and an upper iteration limit dual termination condition ensures the required accuracy of the parameter inversion while avoiding the waste of computational resources caused by excessive iterations.

[0057] Please continue reading Figure 1 , adjust the clock frequency according to the three-dimensional parameters of the air gap defect and control the phase emission array to generate a cancellation beam, adjust the power consumption of the stream processor cluster, perform ray tracing tests and collect test data to determine whether the GPU is qualified.

[0058] In one embodiment of the present invention, adjusting the clock frequency according to the three-dimensional parameters of the air gap defect and controlling the phased transmit array to generate a cancellation beam, adjusting the power consumption of the stream processor cluster, performing a ray tracing test and collecting test data, and determining whether the GPU is qualified include: Performing vectorization processing on the three-dimensional parameter data of the air gap defect, generating a local clock offset vector and a phase cancellation template, and recording a corresponding compensation index; Writing the local clock offset vector into the target clock domain according to the compensation index, driving the phase transmitting array to transmit the cancellation beam according to the phase cancellation template, and collecting the beam stability index; Setting a load ratio coefficient of a target stream processor cluster according to the beam stability indicator, obtaining a power consumption adjustment plan, and collecting a power consumption matrix after adjustment; Running a transparent refraction ray tracing frame sequence test under the power consumption adjustment scheme, collecting a third error count matrix, a retransmission number matrix, a scattering spectrum matrix, and an adjusted power consumption matrix, and generating a retest indicator set; The retest indicator set is compared with the functional abnormality benchmark set before compensation by threshold value, and qualified data is generated when all indicators are lower than a third threshold value, otherwise a defect report is generated.

[0059] The following is a detailed description of the steps involved in the above embodiment: Vectorization is the process of converting the three-dimensional parameter data (height, width, and length) of an air gap defect into a data structure that can be directly used for compensation operations. In specific implementation, the resonant frequency characteristics of the air gap are first calculated based on the three-dimensional parameter data. The air gap's response characteristics at different frequencies are then analyzed using the acoustic-electrical-thermal coupling equation. The local clock offset vector is a numerical vector used to fine-tune the clock frequency in a local area to avoid the air gap's resonant frequency. It contains two attributes: the offset direction and the offset amount. During the calculation, the air gap's main resonant frequency f0 is used as the center, a frequency avoidance range of ±200 MHz is determined, and the optimal offset Δf is calculated so that the operating frequency f0 ± Δf effectively avoids the air gap's resonant frequency. For example, if the main resonant frequency of a certain air gap is 10.2 GHz, the local clock offset vector is -150 MHz, adjusting the clock frequency in that area to 10.05 GHz. The phase cancellation template is a set of control parameters used to generate a beam with a phase opposite to that of the air gap's resonant wave. This technique mitigates the air gap's resonance effect through the principle of phase interference. During specific generation, the delay time τ(x,y) of each unit in the phased transmission array is calculated based on the spatial position and geometric characteristics of the air gap, so that the transmitted beam forms a cancellation wave with a phase opposite to the resonant wave in the air gap area. The compensation index refers to the identification information that records the spatial region and frequency parameters corresponding to the compensation operation, including the spatial coordinates (x,y), the frequency offset Δf, and the phase adjustment value φ. For example, the compensation index {(12,15),-150MHz,180°} indicates that a frequency offset of -150MHz and a phase adjustment of 180° are performed in the area with coordinates (12,15). This compensation scheme design based on the physical characteristics of the air gap can provide customized compensation strategies for specific defect characteristics, rather than simply global frequency degradation, which ensures system stability while maximizing performance.

[0060] The target clock domain refers to the local clock control area within the GPU where frequency adjustment is required, usually corresponding to a specific stream processor cluster. In specific implementation, the GPU's clock management unit writes the local clock offset vector to the target clock domain register specified by the corresponding compensation index to achieve precise adjustment of the clock frequency of the local area. For example, if the area of ​​3×3 partitions around the coordinate (12,15) specified by the compensation index requires a frequency offset of -150MHz, then the frequency adjustment value is written to the clock control registers of these 9 partitions. The cancellation beam refers to an electrothermal acoustic beam generated by a phase-transmitting array with an opposite phase to the air gap resonance wave, which offsets the resonance effect of the air gap through the principle of constructive interference. In specific implementation, according to the delay time parameters in the phase cancellation template, the stream processor units located around the air gap are driven to perform pulse operations according to a precisely controlled timing to form a directional cancellation beam. The beam stability index refers to a set of parameters for evaluating the quality of the cancellation beam, including properties such as beam phase stability, energy density uniformity, and directionality. The phase fluctuation σ of the cancellation beam is collected by an RF detector array arranged on the surface of the GPU chip. φ (phase standard deviation, in degrees), energy density distribution E(x,y) (spatial energy distribution, in mW / mm 2 ) and the directional deviation angle θ dev (the angle between the actual direction and the target direction, in degrees) and other data to calculate the comprehensive stability index S=w φ ·σ φ +w E ·σ E +w θ ·θ dev , where w φ 、w E 、w θ are weight coefficients, which are set to 0.4 (phase weight), 0.3 (energy weight) and 0.3 (direction weight), σ E The dimensionless ratio of the standard deviation to the mean value of the energy density. A smaller S value indicates higher beam quality. When S < 0.15, the beam is considered high-quality and suitable for long-term stability compensation. When 0.15 ≤ S < 0.3, the beam is considered medium-quality and suitable for short-term compensation. When S ≥ 0.3, the beam is considered low-quality and unsuitable for compensation. This dual compensation mechanism, combining clock adjustment with beam cancellation, effectively reduces the triggering conditions and amplification effects of air gap resonance, significantly improving the stability of the defect region.

[0061] The load scaling factor is a parameter that controls the workload intensity of a stream processor cluster and is used to adjust the power consumption level of defective areas. In specific implementation, the load scaling factor α is determined based on the beam stability index S: when S < 0.15, α = 0.9; when 0.15 ≤ S < 0.3, α = 0.7; and when S ≥ 0.3, α = 0.5. A lower load scaling factor indicates that stricter power consumption limits need to be imposed on the stream processor cluster. A power regulation scheme is a specific strategy for controlling the power consumption of a stream processor cluster by adjusting instruction scheduling, voltage control, and frequency settings. In specific implementation, the GPU task scheduler limits the maximum instruction issuance rate of the stream processor cluster in the target area to α times the standard issuance rate. The supply voltage is adjusted as needed to maintain the average power consumption of the area at α times the standard power consumption. For example, if the standard power consumption of a region is 120 watts and the load scaling factor α = 0.7, the power regulation scheme will control the power consumption of that region to no more than 84 watts. The adjusted power consumption matrix is ​​a data matrix that records the actual power consumption distribution of each partition after power regulation. The GPU's built-in power monitoring circuit continuously collects 30 seconds of power consumption data for each partition at 10-millisecond intervals, forming a two-dimensional space-time power consumption matrix P(x,y,t), where x and y are the partition coordinates and t is the time point. This beam stability-based power regulation strategy enables refined power management of defective areas. While ensuring system stability, it dynamically adjusts the strictness of power consumption limits based on the effectiveness of the offset, avoiding extensive performance restrictions and achieving a better balance between system stability and performance.

[0062] The transparent refraction ray tracing frame sequence test refers to a verification test that uses the same high-complexity rendering scene as the previous steps and is performed again after applying compensation measures. During specific implementation, a test sequence containing 30 consecutive frames is loaded, each frame contains 15-20 transparent objects with different refractive indices and 4-8 levels of cascaded light refraction paths, and the test duration is 60 seconds. The third error count matrix refers to a new data matrix that records the number of errors in each partition under compensation measures, and is collected through the error detection and correction circuit built into the GPU. The retransmission count matrix refers to a data matrix that records the number of times a request is re-initiated after a texture access failure in each partition, and is collected through the GPU texture cache monitor. The scattering spectrum matrix refers to a data matrix that records the radio frequency scattering characteristics of each partition at different frequencies, and is collected through an RF detector array. The adjusted power consumption matrix refers to a data matrix that records the actual power consumption distribution of each partition under compensation measures, and is collected through a power monitoring circuit. The retest indicator set refers to a comprehensive evaluation data set formed by extracting the key indicators from the above four matrices, including the maximum error rate ER max (Unit: times / second), average number of retransmissions RR avg (Unit: times / second), scattering peak intensity SP max(normalized value, dimensionless) and power consumption stability PS (ratio of the standard deviation to the mean power consumption, dimensionless). For example, the retest metric set obtained from a particular test was {5 times / second, 12 times / second, 0.08, 0.05}, indicating a maximum error rate of 5 times / second, an average number of retransmissions of 12 times / second, a scattering peak intensity of 0.08, and a power consumption stability of 0.05. This multi-dimensional retesting approach comprehensively evaluates the effectiveness of compensation measures, focusing not only on direct functional indicators such as error rate but also monitoring underlying physical properties such as scattering characteristics and power consumption stability, providing a more comprehensive assessment of system status.

[0063] The retest indicator set is compared with the pre-compensation functional abnormality benchmark set by threshold comparison. When all indicators are lower than the third threshold, qualified data is generated. Otherwise, a defect report is generated. The specific implementation method is as follows: the pre-compensation functional abnormality benchmark set refers to the system abnormal state data set collected before the compensation measures are implemented, which is used as a comparison benchmark. In specific implementation, the four indicators extracted from the previous steps, namely, the maximum error rate, average number of retransmissions, scattering peak intensity and power consumption stability, constitute the benchmark set {ER base ,RR base ,SP base ,PS base}, the meaning of each indicator is the same as the corresponding item in the retest indicator set. The third threshold refers to the standard value for judging whether the GPU has passed the verification test, which is set to {10 times / second, 20 times / second, 0.15, 0.1}, corresponding to the qualified thresholds of the four indicators respectively. Threshold comparison refers to the process of comparing each indicator in the retest indicator set with the third threshold. In specific implementation, check one by one whether the four indicators in the retest indicator set are all lower than the corresponding third threshold. At the same time, calculate the improvement ratio of the retest indicator to the benchmark indicator: R i =(I base -I test ) / I base , where I base Indicates the benchmark index value, I test Represents the retest index value, R iRepresents the improvement ratio of the corresponding metric (dimensionless). Acceptable data refers to acceptance data generated when all retested metrics are below the third threshold and the average improvement ratio exceeds 70%. It includes the GPU serial number, test time, various metric values, and improvement ratio. A defect report is a detailed problem description document generated when any metric exceeds the third threshold or the average improvement ratio falls below 70%. It includes the specific value of the failed metric, the difference from the threshold, the location of the air gap, and a recommended remediation solution. For example, if the retest metric set is {8 times / second, 15 times / second, 0.12, 0.06} and the benchmark set is {50 times / second, 80 times / second, 0.45, 0.25}, the improvement ratios for each metric are (50-8) / 50 = 84%, (80-15) / 80 = 81%, (0.45-0.12) / 0.45 = 73%, and (0.25-0.06) / 0.25 = 76%, respectively. The average improvement ratio is (84% + 81% + 73% + 76%) / 4 = 78.5%. All metrics are below the third threshold, and the average improvement ratio exceeds 70%, thus generating qualified data. This verification mechanism based on multi-metric threshold comparison considers both absolute metric levels and relative improvement, ensuring the reliability and effectiveness of the verification results and providing a scientific basis for GPU quality control.

[0064] The above describes the detection method of the GPU chip in the embodiment of the present invention. The following describes the detection device of the GPU chip in the embodiment of the present invention. Figure 2 An embodiment of a GPU chip detection device according to an embodiment of the present invention includes: The initial scanning module 101 is used to inject a low duty cycle pulse signal into the stream processor cluster and perform linear frequency sweep processing to obtain power data, temperature rise data and phase data, and generate a frequency space phase resonance diagram; A target positioning module 102 is configured to collect a first error count matrix and retransmission count data based on the frequency-space phase resonance map when the stream processor cluster performs ray tracing rendering, and determine a target focus area; a defect verification module 103 configured to configure the stream processor cluster in the target focus area into a phased transmit array, control the phased transmit array to generate a steerable electrothermal acoustic beam, collect power consumption data, a second error count matrix, and scattering spectrum data, and determine an area where an air gap defect exists; The parameter measurement module 104 is configured to input a spread spectrum signal to the phased transmission array and apply a random flip sequence to the storage bus in the region where the air gap defect exists, obtain temperature strain data, scattered phase data, and error count timing data, and calculate the three-dimensional parameters of the air gap defect; The quality judgment module 105 is used to adjust the clock frequency according to the three-dimensional parameters of the air gap defect and control the phased emission array to generate a cancellation beam, adjust the power consumption of the stream processor cluster, perform ray tracing tests and collect test data, and determine whether the GPU is qualified.

[0065] above Figure 2 The detection device for the GPU chip in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The detection device for the GPU chip in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0066] Figure 3 FIG2 is a schematic diagram of the structure of a GPU chip testing device provided by an embodiment of the present invention. The GPU chip testing device 200 may vary significantly depending on configuration or performance. It may include one or more processors 210 (e.g., one or more processors), memory 220, and one or more storage media 230 (e.g., one or more mass storage devices) storing application programs 233 or data 232. The memory 220 and storage medium 230 may be either transient or persistent storage. The program stored in the storage medium 230 may include one or more modules (not shown), each of which may include a series of instructions for operating on the GPU chip testing device 200. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, and the GPU chip testing device 200 may execute the series of instructions stored in the storage medium 230 to implement the steps of the aforementioned GPU chip testing method.

[0067] The GPU chip detection device 200 may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the GPU chip detection device shown does not constitute a limitation on the GPU chip detection device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0068] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, cause the computer to execute the steps of the GPU chip detection method.

[0069] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0071] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for detecting a GPU chip, characterized in that: include: Inject low-duty-cycle pulse signals into the stream processor cluster and perform linear frequency sweep processing to obtain power data, temperature rise data, and phase data, and generate a frequency-space phase resonance diagram; Based on the frequency-space phase resonance map, collecting a first error count matrix and retransmission count data when the stream processor cluster performs ray tracing rendering to determine a target focus area; configuring a stream processor cluster in the target focal area as a phased transmit array, controlling the phased transmit array to generate a steerable electrothermal acoustic beam, collecting power consumption data, a second error count matrix, and scattering spectrum data, and determining an area where an air gap defect exists; In the region where the air gap defect exists, a spread spectrum signal is input to the phased transmit array and a random flip sequence is applied to the storage bus to obtain temperature strain data, scattered phase data, and error count timing data, and calculate the three-dimensional parameters of the air gap defect; The clock frequency is adjusted according to the three-dimensional parameters of the air gap defect and the phased emission array is controlled to generate a cancellation beam, the power consumption of the stream processor cluster is adjusted, a ray tracing test is performed and test data is collected to determine whether the GPU is qualified.

2. The method for detecting a GPU chip according to claim 1, wherein: The method injects a low duty cycle pulse signal into the stream processor cluster and performs linear frequency sweep processing to obtain power data, temperature rise data, and phase data, and generates a frequency-space phase resonance diagram, including: Perform phase-shifted pulse injection processing on the partitioned stream processor cluster to obtain the partitioned power response matrix; According to the pulse injection timing, the clock frequency is linearly scanned and the regional temperature rise gradient matrix and phase delay matrix of each frequency point are collected; During the pulse injection interval, power data, temperature rise data, and phase data of the reference silent cluster are collected, and differential calibration processing is performed on the partition power response matrix, the regional temperature rise gradient matrix, and the phase delay matrix to generate a calibrated measurement matrix; The calibrated measurement matrix is ​​mapped according to the spatial index and the frequency index to generate a frequency-space phase resonance diagram.

3. The method for detecting a GPU chip according to claim 2, wherein: The performing of phase-shifted pulse injection processing on the partitioned stream processor cluster to obtain a partitioned power response matrix includes: Establish a pulse timing table for each partition stream processor cluster and set a fixed phase difference between adjacent partitions; Injecting low duty cycle pulse signals into the partitioned stream processor cluster in sequence according to the pulse timing table, and recording the injection index; Synchronously collect instantaneous power samples of the corresponding partition within each pulse injection window to generate a power sample group; The power sample groups are integrated according to the spatial index and the injection order to construct a partition power response matrix.

4. The method for detecting a GPU chip according to claim 1, wherein: The method of collecting a first error count matrix and retransmission count data based on the frequency-space phase resonance diagram when the stream processor cluster performs ray tracing rendering to determine a target focus area includes: Loading a periodic transparent-refractive ray tracing frame sequence to a stream processor cluster and synchronously calling frequency labels in the frequency-space phase resonance diagram, performing rotational scanning processing on a clock phase, and obtaining a first error count matrix and a retransmission count matrix that increase over time; Selecting reference non-textured frame data in a rendering sequence and performing time domain difference processing based on the first error count matrix and the retransmission number matrix to obtain a function domain difference anomaly matrix; Performing a three-dimensional convolution process on the functional domain differential anomaly matrix and the frequency-space phase resonance map according to spatial coordinates and time indexes to obtain a coupling peak matrix; A threshold cluster analysis is performed on the coupling intensity according to the coupling peak matrix, and a cluster center coordinate set is output as the target focus area.

5. The method for detecting a GPU chip according to claim 1, wherein: The method further comprises configuring the stream processor cluster in the target focus area as a phased transmit array, controlling the phased transmit array to generate a steerable electrothermal acoustic beam, collecting power consumption data, a second error count matrix, and scattering spectrum data, and determining an area where an air gap defect exists, including: Phase delay scheduling parameters are set for the stream processor cluster in the target focal area. The phased transmit array is driven according to the uniform angle and uniform frequency dual scanning scheme to generate an in-plane steered electrothermal acoustic beam, and the power ramp matrix corresponding to the scanning angle index and frequency index is obtained. During beam scanning, a scanning angle index and a rendering pipeline event timer are synchronously recorded, a second error count matrix and a radio frequency scatter spectrum matrix are collected, and the second error count matrix and the radio frequency scatter spectrum matrix are time-aligned according to the same angle index and frequency index; performing differential calibration processing on the power jump matrix, the second error count matrix, and the radio frequency scattering spectrum matrix to deduct background signals in non-target focus areas, thereby obtaining a calibrated multi-domain coupling matrix; Convolution correlation processing is performed according to the calibrated multi-domain coupling matrix to extract a coordinate set where the synchronous peak value of the power-error-scattering three domains exceeds a first threshold, and the area where the air gap defect exists is output through spatial clustering analysis.

6. The method for detecting a GPU chip according to claim 1, wherein: In the region where the air gap defect exists, a spread spectrum signal is input to the phased transmission array and a random flip sequence is applied to the storage bus to obtain temperature strain data, scattering phase data, and error count timing data, and calculate the three-dimensional parameters of the air gap defect, including: Inputting a spread spectrum code with a bandwidth equal to twice Δf based on the center frequency range into the region where the air gap defect exists and recording the spread spectrum index, driving the phase transmitting array to generate a broadband electrothermal acoustic beam according to the spread spectrum index, and acquiring scattered phase time series data corresponding to the spread spectrum index, where Δf represents an adjustable offset from the center frequency; Apply a random flip sequence synchronized with the spread spectrum index to the storage bus when the spread spectrum beam is in effect, and record the flip event address mapping and error count timing data; Synchronously collecting the temperature strain time series data output by the temperature strain array on the area where the air gap defect exists, and performing three-domain alignment processing on the scattering phase time series data, the error count time series data, and the flip event address mapping according to the spread spectrum index and the sampling timestamp to generate a multi-domain alignment data set; The multi-domain aligned dataset is input into an inversion model based on multi-physics field coupling, and an objective function with scattering phase offset, temperature strain amplitude, error count peak and flip event spatial distribution error as constraints is iteratively solved to output three-dimensional parameter data of air gap defects.

7. The method for detecting a GPU chip according to claim 6, wherein: The multi-domain aligned data set is input into an inversion model based on multi-physics field coupling, and an objective function constrained by scattering phase offset, temperature strain amplitude, error count peak, and flip event spatial distribution error is iteratively solved to output three-dimensional parameter data of the air gap defect, including: A joint error function is constructed based on the scattering phase shift, temperature strain amplitude, error count peak and flip event spatial distribution; Set the air gap height, width, and length as the variables to be optimized, initialize the variable vector, and set the adaptive learning rate; Calculate the error function gradient in each iteration, update the variable vector according to the learning rate, and dynamically adjust the learning rate based on the convergence monitoring threshold; When each component of the error function is lower than the second threshold or the number of iterations reaches the upper limit, the current variable vector is output to obtain the three-dimensional parameter data of the air gap defect.

8. The method for detecting a GPU chip according to claim 1, wherein: The method includes adjusting the clock frequency according to the three-dimensional parameters of the air gap defect and controlling the phased emission array to generate a cancellation beam, adjusting the power consumption of the stream processor cluster, performing a ray tracing test and collecting test data, and determining whether the GPU is qualified, including: Performing vectorization processing on the three-dimensional parameter data of the air gap defect, generating a local clock offset vector and a phase cancellation template, and recording a corresponding compensation index; Writing the local clock offset vector into the target clock domain according to the compensation index, driving the phase transmitting array to transmit the cancellation beam according to the phase cancellation template, and collecting the beam stability index; Setting a load ratio coefficient of a target stream processor cluster according to the beam stability indicator, obtaining a power consumption adjustment plan, and collecting a power consumption matrix after adjustment; Running a transparent refraction ray tracing frame sequence test under the power consumption adjustment scheme, collecting a third error count matrix, a retransmission number matrix, a scattering spectrum matrix, and an adjusted power consumption matrix, and generating a retest indicator set; The retest indicator set is compared with the functional abnormality reference set before compensation by threshold value, and qualified data is generated when all indicators are lower than a third threshold, otherwise a defect report is generated.

9. A GPU chip detection device, characterized in that: The GPU chip detection device adopts the GPU chip detection method according to any one of claims 1 to 8, and the GPU chip detection device includes: The initial scanning module is used to inject a low-duty-cycle pulse signal into the stream processor cluster and perform linear frequency sweep processing to obtain power data, temperature rise data, and phase data, and generate a frequency-space phase resonance diagram; a target positioning module, configured to collect a first error count matrix and retransmission count data based on the frequency-space phase resonance map when the stream processor cluster performs ray tracing rendering, and determine a target focus area; a defect verification module, configured to configure the stream processor cluster in the target focus area into a phased transmit array, control the phased transmit array to generate a steerable electrothermal acoustic beam, collect power consumption data, a second error count matrix, and scattering spectrum data, and determine an area where an air gap defect exists; a parameter measurement module, configured to input a spread spectrum signal to the phased transmit array and apply a random flip sequence to a storage bus in the region where the air gap defect exists, obtain temperature strain data, scattered phase data, and error count timing data, and calculate three-dimensional parameters of the air gap defect; The quality judgment module is used to adjust the clock frequency according to the three-dimensional parameters of the air gap defect and control the phased emission array to generate a cancellation beam, adjust the power consumption of the stream processor cluster, perform ray tracing tests and collect test data, and determine whether the GPU is qualified.

10. A GPU chip detection device, characterized in that: The GPU chip detection device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the GPU chip detection device to perform the steps of the GPU chip detection method according to any one of claims 1 to 8.

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