High-temperature test method for wafer chip

Abstract

The invention relates to the technical field of high-temperature testing, in particular to a high-temperature testing method of a wafer chip, which comprises the following steps of: based on a high-temperature testing platform of the wafer chip, acquiring and arranging temperature and current changes of each testing unit, constructing a thermal steady-state parameter sequence, screening time sequence data, analyzing a trend and segmenting; temperature, current and voltage characteristic parameters are extracted, a stable distribution dot matrix is generated through space mapping, and thermal stability space information is recognized and distinguished. According to the invention, by synchronously acquiring multi-type response data of a test unit in a high-temperature steady-state stage, serialized segmentation and classification are carried out on temperature, current and voltage change tracks, accurate mapping of dynamic characteristics and spatial distribution is realized, thermal response characteristics of each region are converted into three-dimensional space coordinate expression, and the stability of the thermal response characteristics is improved. Local stability differences are separated and highlighted, a complex thermal behavior is converted into a partition mapping structure from a continuous track, and the defects of spatial resolution and trend expression are overcome.

Description

Technical Field

[0001] This invention relates to the field of high-temperature testing technology, and more particularly to a high-temperature testing method for wafer chips. Background Technology

[0002] The field of high-temperature testing technology involves performance testing, stability assessment, and fault prediction of semiconductor devices, circuits, packaging structures, and systems under high-temperature environments, aiming to ensure the reliability and consistency of chip products under extreme operating conditions. Traditional high-temperature testing methods for wafer chips refer to the means of verifying the electrical performance and assessing the reliability of unpackaged semiconductor wafers under high-temperature conditions. This primarily involves using a probe card to contact the wafer chip's pads on a high-temperature heating stage, inputting excitation signals, and monitoring the output response.

[0003] Existing technologies rely solely on high-temperature platforms and probe measurements to monitor the static response at the overall temperature point. During the testing process, the dynamic response changes generated by each unit in the steady-state phase cannot be continuously captured. The measurement data lacks serialization and aggregation, and the differences in thermal response between regions cannot be effectively distinguished. Subtle current drift and voltage change trends are covered up, and spatial information cannot be intuitively reflected in the overall results. Local unstable regions are often averaged out, resulting in insufficient spatial resolution, incomplete trend expression, and untimely anomaly identification in chip stability analysis under high-temperature environments. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a high-temperature testing method for wafer chips.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a high-temperature testing method for wafer chips, comprising the following steps: S1: Based on the high temperature test platform for wafer chips, analyze the output signal of the hot stage, determine the correspondence between the temperature and number of the test unit, compare the current changes detected by the electrical test probe, combine the steady-state duration, archive the test results in sequence, and obtain the thermal steady-state parameter sequence. S2: Based on the thermal steady-state parameter sequence, filter the temperature and current time series of each test unit, determine the data change trend, analyze data fluctuations and inflection points, identify the segmentation point by the amplitude change exceeding the preset threshold, number the segments in sequence, and obtain the segmented trajectory set. S3: Based on the segmented trajectory set, calculate the temperature span of each segment, accumulate the current change of each segment, analyze the voltage offset trend, extract the segment features, and combine the segment running time to obtain the steady-state characteristic parameter set. S4: Based on the steady-state characteristic parameter set, determine the vertical coordinates of the lattice corresponding to the temperature span, combine the horizontal coordinates corresponding to the current change and voltage characteristics, calculate the intensity parameters based on the segment running time weighted, and arrange them in space to obtain stable distribution lattice data; S5: Based on the stable distribution lattice data, analyze the spatial proximity relationship of each lattice, compare the intensity parameter intervals, identify continuous regions and regions where the parameter change rate exceeds the set value, adjust the lattice structure, mark the breakpoints, and obtain the thermal stability spatial information.

[0006] The present invention is improved in that the thermal steady-state parameter sequence includes unit temperature response, unit current characteristics and unit duration; the segmented trajectory set includes temperature segment information, current segment information and trajectory interval index; the steady-state characteristic parameter group includes segment temperature span, segment current accumulation, segment voltage characteristics and segment duration; the stable distribution lattice data includes lattice vertical coordinates, lattice horizontal coordinates and lattice intensity parameters; and the thermal stability spatial information includes spatial distribution morphology, regional stability differentiation and breakpoint marking information.

[0007] The present invention is improved in that the step of obtaining the thermal steady-state parameter sequence is specifically as follows: S111: Based on a high-temperature testing platform for wafer chips, analyze temperature setting parameters and heating power parameters, identify the correspondence between the number of each test unit and the thermal control zone, determine the distribution characteristics of parameters in each zone, and obtain the number-mapped parameter set; S112: Based on the numbering mapping parameter set, compare the current change sequence of each electrical test probe in the test unit, identify the current fluctuation range of adjacent data segments between test points, aggregate the current change data and numbering information in the same test unit, and obtain the unit current response sequence. S113: Based on the unit current response sequence, integrate the temperature monitoring data, current monitoring data and acquisition time information synchronously collected by each test unit during the thermal control stabilization stage, sort them according to the test unit number, aggregate them into structured monitoring results, and obtain the thermal steady-state parameter sequence.

[0008] The present invention is improved in that the step of obtaining the segmented trajectory set is specifically as follows: S211: Based on the thermal steady-state parameter sequence, compare the changes in temperature data and current data of each test unit under the time sequence arrangement, determine the trend direction of temperature and current data over time, identify the data interval where the derivative of the rate of change exceeds the preset threshold, and obtain the temperature and current trend feature set. S212: Based on the temperature and current trend feature set, determine the trend distribution area of ​​temperature and current in each test unit, compare the change amplitude between continuous trends in data segments, locate the trend turning point in the data sequence, and obtain the turning interval index group. S213: Based on the turning interval index group, determine the fluctuation amplitude characteristics of the continuously monitored data segments in each test unit, compare the start and end point structures of each data segment, and number the segments according to the test unit detection order to obtain a set of segmented trajectories.

[0009] The present invention is improved in that the steps for obtaining the steady-state characteristic parameter set are as follows: S311: Based on the segmented trajectory set, calculate the difference between the highest and lowest temperatures in each segment, and determine the temperature fluctuation of each segment by comparing the temperature change range, thereby obtaining the temperature change range of the segment. S312: Based on the temperature change range of the section, compare the continuous current detection points in each segment, calculate the change amplitude between adjacent currents, accumulate the change amplitudes, analyze the current change trend of each segment, and obtain the cumulative data of current change in the section. S313: Based on the cumulative data of current change in the section, calculate the voltage offset rate between the voltage detection point and the starting voltage in each section, analyze the voltage change of the sampling point, determine the average voltage offset of each section, analyze the temperature change range and the cumulative amount of current change in the section, and combine the section running time to obtain the steady-state characteristic parameter set.

[0010] The present invention is improved in that the steps for obtaining the stable distribution matrix data are specifically as follows: S411: Based on the steady-state characteristic parameter set, analyze the temperature difference span of each section, combine the maximum and minimum temperature data, calculate the temperature difference distribution of each section, determine the trend of temperature fluctuation between different sections, optimize the distribution of temperature data in the vertical direction of three-dimensional space, and obtain the vertical coordinate set of the dot matrix. S412: Based on the vertical coordinate set of the dot matrix, calculate the current accumulation and voltage characteristics of each segment, analyze the relationship between current change and voltage change, identify the correlation of current and voltage combinations, compare the distribution of each combination in the horizontal space, and obtain the horizontal coordinate sequence of the dot matrix. S413: Based on the aforementioned horizontal coordinate sequence of the dot matrix, determine the operational status of each segment, calculate the spatial intensity of each point, and use the following formula: ; By acquiring lattice intensity parameters and optimizing the spatial lattice arrangement, stable lattice data can be obtained. Indicates the spatial coordinate position The lattice intensity parameters at that location, This indicates the operational characteristic indicators of the corresponding segment c. This represents the normalized index of the corresponding voltage characteristic at spatial index j. This represents the normalized index of the corresponding current characteristic at spatial index j. This represents the operational characteristic index of the k-th segment, and n represents the total number of segments involved in the calculation.

[0011] The present invention is improved in that the step of obtaining the thermal stability spatial information is specifically as follows: S511: Based on the stable distribution lattice data, determine the spatial relationship of the lattice units in the three-dimensional coordinate system, compare the proximity of adjacent lattices in the vertical direction and the horizontal coordinate, identify the positional differences between the cluster area and the boundary area, and obtain the spatial adjacency structure group. S512: Based on the spatial adjacency structure group, compare the intensity parameter variation range of adjacent lattices, determine the variation trend of intensity parameters in the continuous distribution area, locate the lattice distribution where intensity variation is concentrated, and obtain the intensity variation characteristics. S513: Based on the intensity change characteristics, adjust the layout of the dot matrix structure, determine the coordinates of the breakpoints in each distribution area, and aggregate the dot matrix spatial coordinates, intensity parameters and breakpoint distribution information to obtain thermal stability spatial information.

[0012] The present invention is improved in that the test unit refers to the smallest functional area on the wafer chip that is divided into independent testing areas, the numbering correspondence refers to the mapping between the arrangement of all test units in physical space and their numbering identifiers, and the change trend refers to the direction and pattern of continuous change of temperature or current data on the test time axis.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by synchronously acquiring multi-type response data of the test unit during the high-temperature steady-state stage, the trajectories of temperature, current, and voltage changes are serialized, segmented, and classified to achieve accurate mapping between dynamic characteristics and spatial distribution. The thermal response characteristics of each region are transformed into three-dimensional spatial coordinates, separating and highlighting local stability differences. The complex thermal behavior is transformed from a continuous trajectory into a partitioned mapping structure, making up for the deficiencies in spatial resolution and trend expression, enabling timely location of regional anomalies and promoting detailed identification of stability distribution under high-temperature conditions. Attached Figure Description

[0014] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the thermal steady-state parameter sequence in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the segmented trajectory set in this invention; Figure 4 This is a flowchart illustrating the acquisition of the steady-state characteristic parameter set in this invention. Figure 5 This is a flowchart illustrating the process of acquiring stable distributed dot matrix data in this invention. Figure 6This is a flowchart illustrating the acquisition of spatial information on thermal stability in this invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] All user-related information involved in this invention (including but not limited to biometric information, identity verification information, behavioral data, device information, and other data that can be used for identity verification and personalized services) is collected and processed with the user's full knowledge and voluntary consent. The collection, storage, and use of all information strictly comply with applicable national and regional laws and regulations, and meet relevant data protection standards and policy requirements. The use of data is limited to purposes necessary for providing the technical services of this invention, and reasonable technical and management measures will be taken to ensure the security and confidentiality of users' personal information in terms of information protection and privacy.

[0018] Example 1:

[0019] Please see Figure 1 This invention provides a technical solution: a high-temperature testing method for wafer chips, comprising the following steps: S1: Based on the high temperature test platform for wafer chips, analyze the output parameters of the hot stage, determine the correspondence between the temperature data and number of each test unit, compare the current change process detected by the electrical test probes between each test point, and combine the steady-state running time to archive the test data in the order of the test units to obtain the thermal steady-state parameter sequence. S2: Based on the thermal steady-state parameter sequence, filter the temperature and current data in the corresponding time series of each test unit, determine the trend of data change over time, analyze the change amplitude and inflection point of each segment of data, identify the segmentation point through the fluctuation amplitude parameter, and mark the segment number according to the detection order to obtain the segment trajectory set; S3: Based on the segmented trajectory set, calculate the span of the temperature series in each segment, gradually accumulate the current change parameters of each segment, analyze the voltage offset change trend to form segment characteristic information, and combine the running time of each segment to obtain the steady-state characteristic parameter set. S4: Based on the steady-state characteristic parameter set, determine the vertical direction of the temperature span corresponding to the lattice in the spatial coordinate system, combine the current change data and voltage characteristic data of each segment to the corresponding horizontal coordinate, allocate the running time of each segment to the lattice intensity parameter, execute the spatial arrangement of the characteristic points, and obtain the stable distribution lattice data. S5: Based on stable distribution lattice data, analyze the spatial proximity relationship of each lattice, compare the variation range of intensity parameters of each lattice, identify regions with continuous or abrupt changes in the variation range, adjust the lattice distribution structure and mark the breakpoints to obtain spatial information on thermal stability.

[0020] The thermal steady-state parameter sequence includes unit temperature response, unit current characteristics, and unit duration; the segmented trajectory set includes temperature segment information, current segment information, and trajectory interval index; the steady-state characteristic parameter set includes segment temperature span, segment current accumulation, segment voltage characteristics, and segment duration; the stable distribution lattice data includes lattice vertical coordinates, lattice horizontal coordinates, and lattice intensity parameters; and the thermal stability spatial information includes spatial distribution morphology, regional stability differentiation, and breakpoint marking information.

[0021] In S1, the operating output parameters refer to a series of temperature control-related operating parameters such as temperature setting, heating power, and platform surface condition output in real time during high-temperature testing; the test unit refers to the smallest functional area on the wafer chip that is divided into independent testing areas, and each unit can measure indicators such as temperature and current independently; the numbering correspondence refers to the mapping between the arrangement of all test units in physical space and their numbering identifiers, that is, a one-to-one correspondence between the number and the actual position; each test point refers to the specific testing position arranged on the wafer surface corresponding to each test unit; the electrical test probe refers to the physical test pins or miniature sensing devices used to contact the wafer surface and collect signals such as current or voltage; the steady-state running time refers to the continuous measurement period experienced by each test unit after the temperature control conditions have stabilized, which is used to ensure that the collected data reflects the response under steady-state conditions; and all detection data refer to all raw measurement data such as temperature and current collected according to the unit number.

[0022] In S2, the time series refers to the sequence of measurement data recorded in chronological order for each test unit or test point within a continuous test period; the trend of change refers to the direction and pattern of continuous change of temperature or current data on the test time axis, such as gradual increase, stabilization, or periodic fluctuation; the magnitude of change refers to the absolute magnitude of the change in the value of parameters such as temperature or current between adjacent or consecutive time points; the inflection point refers to the point where the trend of data change reverses or changes from increasing to decreasing (or vice versa); the fluctuation amplitude parameter refers to the parameter used to measure the severity of data fluctuation, usually used to determine whether there is a drastic change in a certain segment of data; the dividing point refers to the boundary point that divides the long sequence of data into multiple intervals based on the trend and amplitude of change; the segment number refers to assigning a unique number to each segment of data obtained after division for subsequent tracking and processing.

[0023] In S3, the span of the temperature series refers to the difference between the maximum and minimum temperatures within a certain segment, used to describe the temperature variation range of that segment; the current variation parameter refers to the characteristic quantity used to quantify the change of current with temperature within each segment, such as the total current change or the rate of change; stepwise accumulation refers to the sum of the current changes at multiple measurement points within each segment to obtain the overall scale of current change; voltage offset refers to the overall drift of the measured voltage within each segment relative to the initial or reference value; segment characteristic information refers to the set of multiple parameters such as span, current variation, and voltage offset, used to describe the typical response characteristics of each segment; the runtime of each segment refers to the total test time actually experienced within the segment interval.

[0024] In S4, the vertical direction of the dot matrix refers to the vertical coordinate (z-axis) of the dot matrix image, which maps the temperature span to the dot matrix in three-dimensional space; voltage characteristic data refers to parameters that represent voltage response characteristics, such as voltage drift and fluctuation within the segmented intervals; the horizontal coordinate refers to the horizontal position of the dot matrix in three-dimensional space, which is usually determined by the current change parameters and voltage characteristic data (x-axis, y-axis); the dot matrix intensity parameter refers to the data that reflects the steady-state maintenance time or other robustness indicators of the test segment, used to show the strength of stability represented by the point; the spatial arrangement of feature points refers to mapping each feature parameter of each segment to three-dimensional space to form a structured dot matrix distribution, which is convenient for subsequent visualization and analysis.

[0025] In S5, spatial proximity refers to the geometric relationship between spatial lattices, such as distance and relative orientation, in a three-dimensional coordinate system; variation range refers to the range of differences in characteristic data such as intensity parameters between adjacent lattices; continuous or abrupt region refers to the location region where the intensity parameters between spatial lattices change continuously or undergo obvious abrupt changes; lattice distribution structure refers to the overall arrangement and structural form of all lattices in space, reflecting the stability distribution pattern of different regions of the wafer.

[0026] Please see Figure 2 The specific steps for obtaining the thermal steady-state parameter sequence are as follows: S111: Based on a high-temperature testing platform for wafer chips, analyze temperature setting parameters and heating power parameters, identify the correspondence between the number of each test unit and the thermal control zone, determine the distribution characteristics of parameters in each zone, and obtain the number-mapped parameter set; The system reads the target temperature and heating power settings currently configured on the hot carrier stage and records these parameters in the platform control file as baseline conditions. Then, it activates the temperature control modules for each zone of the hot carrier stage, gradually increasing the temperature. Temperature sensors collect real-time data on the current temperature of each zone, detecting whether the actual temperature is approaching the set value. Next, it reads the physical layout diagram of all test units on the wafer chip. Each test unit is located at specific coordinates in space. The layout diagram establishes a one-to-one correspondence between its number and spatial location. For example, test unit numbered U_021 is located in platform zone 3. Based on the location of this test unit, the system reverse-engineers its heating zone to extract the heating zone information. The current actual temperature is compared with its corresponding set value. Then, the current output power setting of the heating zone is read and compared with the original control setting value. Based on the temperature control characteristic data of the actual area where all test units are located, it is determined whether the area has a relatively stable control state close to the target temperature. If the thermal control area corresponding to the test unit is consistent with the control mapping represented by its number, its mapping record is retained. If there is an error or deviation, the comparison item is removed. The number mapping processing between all test units and their thermal control areas is completed. For example, U_021 and U_022 both belong to thermal control area 1, while U_035 belongs to thermal control area 2, forming a set of number mapping parameters as the index basis for subsequent data processing.

[0027] S112: Based on the number mapping parameter set, compare the current change sequence of each electrical test probe in the test unit, identify the current fluctuation range of adjacent data segments between test points, aggregate the current change data and number information in the same test unit, and obtain the unit current response sequence. The system reads current monitoring data recorded by multiple electrical test probes within each test unit. The data is recorded in time series format, meaning the current value is sampled at fixed intervals, with a sampling frequency of ten times per second or higher. The collected current data is sorted chronologically to construct a time series. For each current time series, the difference between each pair of adjacent sampling points is calculated to determine if a sudden change in current value has occurred. If the difference between two points is significantly greater than a set value, the data segment is marked as a current fluctuation segment. Furthermore, multiple probes within the same test unit are compared horizontally. If multiple probes show similar current fluctuations... Similar current fluctuations occurred throughout the time frame. The probe data were categorized into the same current fluctuation range, and then the categorized current fluctuation ranges were bound to the test unit number. This allowed multiple current change records to be aggregated under each number. For example, test unit U_055 contained two fluctuation ranges: the first was multiple sharp current changes within the first minute after the test started, and the second was a continuous rise in current between the fifth and sixth minutes. In this way, each test unit formed its unique current response sequence, and information such as the test point location, the start and end time of the current fluctuation, and the current difference amplitude were retained in the sequence as basic data for subsequent analysis.

[0028] S113: Based on the unit current response sequence, integrate the temperature monitoring data, current monitoring data and acquisition time information synchronously collected by each test unit during the thermal control stabilization stage, sort them according to the test unit number, aggregate them into structured monitoring results, and obtain the thermal steady-state parameter sequence; After the temperature control gradually stabilizes, i.e., when the surface temperature of the hot track slows down and no longer changes significantly, the current period is determined to be a thermal steady-state acquisition period. The full-area synchronous data acquisition program is started, and the temperature and current data of each unit in the current state are synchronously read according to the numbering order of all test units. Each set of data is timestamped, recording the start time and end time range. The data segment is sorted and numbered according to time sequence. The thermal control zone number of each test unit, the current time series collected by its electrical test probe and its time range are read one by one, and then its temperature monitoring record is read accordingly. The information is aggregated by number to construct a structured dataset containing information such as number, temperature control zone, temperature data, current data and acquisition period. During the processing, if the acquisition period of a certain test unit is found to be inconsistent with that of other units, its time period boundary is automatically calibrated or incomplete data records are removed. All test units are sorted according to number, and the overall structured monitoring results are output as a complete thermal steady-state parameter sequence to support data trend analysis and thermal stability judgment in subsequent stages.

[0029] Please see Figure 3 The specific steps for obtaining the segmented trajectory set are as follows: S211: Based on the thermal steady-state parameter sequence, compare the changes in temperature and current data of each test unit under the time sequence arrangement, determine the trend direction of temperature and current data over time, identify the data interval of trend change, and obtain the temperature and current trend feature set. Temperature and current data for each test unit are extracted from structured data and arranged chronologically within the test period. A dual-sequence data pair is constructed for each test unit. By comparing the temperature and current increases / decreases between adjacent time points, the trend direction of temperature and current is marked for each time period. For example, if the temperature value increases sequentially across three consecutive sampling points, the temperature trend is marked as upward; if the current value also increases sequentially, the current trend is also marked as upward. Then, segments of trend direction change are searched within the trend-marked sequence of each test unit. If the temperature trend changes from continuous increase to decrease and the current trend also changes accordingly, this time point is considered to be at the trend abrupt change boundary. Further analysis is then performed on the difference before and after the trend change segment. The absolute difference is calculated. If the temperature changes by more than 5°C between two sampling points or the current fluctuates by more than 1 mA within 100 milliseconds, the interval is determined to be a trend change interval. For all the change intervals detected in each test unit, the start and end times are recorded in the order of their occurrence, and trend change labels are attached to them, such as rising to falling or falling to rising. Combined with the corresponding test unit number, a set of joint trend change features of temperature and current is formed. For example, in test unit U_056, the temperature drops from 250°C to 243°C and the current rises from 14.2 mA to 16.0 mA within the 40th to 45th second of the test, which is marked as a temperature decrease-current increase type change interval. After this process is repeated on all units, the temperature and current trend feature set is output.

[0030] S212: Based on the temperature and current trend feature set, determine the trend distribution area of ​​temperature and current in each test unit, compare the change amplitude between continuous trends in data segments, locate the trend turning point in the data sequence, and obtain the turning interval index group. The trend label data for each test unit is categorized and processed. First, the cumulative duration of temperature trends (rising, falling, or stable) for each test unit throughout the entire monitoring period is calculated. Simultaneously, the direction of current trend changes and their correspondence with the temperature trend are also calculated. Then, the numerical amplitude changes between consecutive trend segments are compared. A segment-by-segment calculation method is used to extract the temperature and current change amplitudes between adjacent trend segments. Thresholds are set for judgment; for example, a temperature change exceeding 5°C or a current change exceeding 1.5 mA is considered a trend reversal. The trend reversal point is located by recording the time index of the trend direction change point. If the trend direction alternates and reverses within multiple consecutive segments and the duration of the change exceeds 3 seconds, then… Alternating reversal segments are treated as composite trend turning points and archived separately. The turning point location and corresponding trend type of each test unit are recorded in the structured index file. During execution, a sliding detection window is used to re-analyze the trend data within each second and mark the starting point of the trend direction change. The significance of the turning point is further verified by combining the span difference of consecutive trend segments. A trend turning point interval index group is established with the test unit number as the main index. For example, in test unit U_045, the trend changes from temperature increase and current decrease to temperature decrease and current increase at the 22nd second of the test, and then reverses again to temperature increase and current stabilization at the 29th second. These two time points are marked as the trend turning points of the unit and recorded in the index group.

[0031] S213: Based on the turning interval index group, determine the fluctuation amplitude characteristics of the continuously monitored data segments in each test unit, compare the start and end point structures of each data segment, and number the segments according to the test unit detection order to obtain the segmented trajectory set; The continuously monitored data segments identified in each test unit are divided, with segments exhibiting consistent trends grouped as a single data segment. Within each segment, the start and end points of temperature and current values ​​are extracted and compared. The amplitude of temperature and current changes is calculated and categorized. For example, if the current increases from 13.0 mA to 14.8 mA, it is classified as a strong rise; if the temperature change during this period is only 0.5°C, it is classified as a weak fluctuation. Simultaneously, the duration of each segment is recorded. If the duration is less than 2 seconds and the amplitude change is less than a set baseline value, the segment is marked as invalid. After elimination, the remaining valid segments are numbered sequentially along the timeline. Segment naming is done using a combination of test unit number and sequence number. For example, U_078_Seg_001 represents the first valid data segment of test unit U_078. This process is repeated to traverse all test units, processing each segment's turning index segment and generating its temperature and current fluctuation characteristic type. By integrating parameters such as segment number, start and end time, change amplitude, and trend label, a complete segment description record is generated. The segmentation information of all test units is summarized into a segment trajectory set for subsequent spatial structure transformation and dot matrix projection processing.

[0032] Please see Figure 4 The specific steps for obtaining the steady-state characteristic parameter set are as follows: S311: Based on the segmented trajectory set, calculate the difference between the highest and lowest temperatures in each segment, and determine the temperature fluctuation of each segment by comparing the temperature change range, thereby obtaining the temperature change interval of the segment. Each test unit's segmented data is numbered and located. The temperature monitoring data sequence recorded in each segment is read, and all temperature sampling points within each segment are traversed to extract the highest and lowest temperature values, which are then marked as the upper and lower temperature limits for that segment. The temperature change range of that segment is obtained by directly subtracting the two values, serving as the basis for evaluating temperature fluctuations. Subsequently, all test segments are processed sequentially, and their temperature fluctuation values ​​are recorded. A temperature difference between 0 and 2℃ is defined as a weak fluctuation segment, a difference between 2℃ and 5℃ is defined as a moderate fluctuation segment, and a difference exceeding 5℃ is recorded as a strong fluctuation segment. Each segment is classified and labeled according to its temperature fluctuation level. Further horizontal comparisons are performed on multiple segments of each test unit to check for any significant fluctuations. For example, in test unit U_066, the temperature in segment 3 rises from 252.2℃ to 260.8℃, with a difference of 8.6℃ between the highest and lowest values. This segment is then marked as a strong fluctuation segment. If the temperature fluctuates only within the range of 1℃ to 3℃, it can be determined as an abnormal segment through comparison. During this process, the temperature change range of each segment is archived, and an independent temperature change range information item is generated for each segment. The information item includes the test unit number, segment number, highest temperature, lowest temperature and their difference, and temperature fluctuation level. This information is then written into the segment temperature change database to complete the construction of the segment temperature change range.

[0033] S312: Based on the temperature change range of the section, compare the continuous current detection points in each segment, calculate the change amplitude between adjacent currents, and accumulate all the change amplitudes to analyze the current change trend of each segment and obtain the cumulative data of current change in the section. The current detection data for each segment is read, and each adjacent sampling point within the data is processed item by item. The current difference between the current point and the next point is extracted, and the magnitude of the absolute difference is determined. All difference values ​​are recorded as a difference sequence, and then all values ​​in the sequence are accumulated to obtain the cumulative value of the overall current change within the segment. A point is considered stable if the difference between adjacent currents is less than 0.2mA, a point of normal change is considered if it is between 0.2mA and 1.0mA, and a point of drastic fluctuation is considered if it is greater than 1.0mA. By statistically analyzing the frequency of drastic fluctuations within each segment, the stability characteristics of the current in that segment are determined. For example, in a certain test segment U_088_D2, a total of... There are 60 current detection points, with an average difference of 0.85mA between adjacent points. Among them, 12 differences exceeded 1.0mA, and the proportion of these 20% was recorded as a high fluctuation segment. The sum of all current changes within this segment was recorded as the cumulative current change value for that segment. For example, if the cumulative total was 45.2mA, this value was written into the corresponding segment database field and associated with its temperature change interval number to ensure that temperature and current characteristics could be tracked together in subsequent processing. In addition, the above operation was performed on all segments under all test units one by one, and the cumulative current change value and its fluctuation level judgment result were output for each segment, thus completing the generation of cumulative current change data for the segment.

[0034] S313: Based on the cumulative data of current changes in the sections, calculate the deviation between the voltage detection point and the starting voltage within each section, analyze the voltage changes at the sampling points, and use the following formula: ; The average voltage deviation of each segment is determined, the temperature variation range and cumulative current variation of the segment are analyzed, and the steady-state characteristic parameter set is obtained by combining the segment's operating time. This represents the average voltage offset of the i-th segment, that is, the average absolute deviation of each voltage sampling point in this segment from the starting voltage. This represents the total number of voltage sampling points within the i-th segment. This represents the voltage detection data at the t-th sampling point within the i-th segment. This represents the starting voltage detection data for the i-th segment, i.e., the voltage reference value at the first sampling point of this segment. : Represents the absolute voltage deviation between the t-th sampling point within the i-th segment and the starting voltage of that segment; The average voltage deviation refers to the comparison of the voltage value of each sampling point within a certain segment with the starting voltage of that segment, the calculation of the absolute difference between the two, the summation of all differences, and finally the averaging using the number of sampling points. The result reflects the average change of the voltage detection data within the current segment relative to the starting time of the segment, that is, the degree of voltage fluctuation or deviation during that segment. The larger the value, the more drastic the overall voltage change within the segment; the smaller the value, the more stable the voltage and the smaller the change. This result can be used to characterize the voltage stability of each test segment in the high-temperature testing of wafer chips, providing a direct quantitative indicator for judging thermal steady-state characteristics and analyzing changes in electrical performance. The voltage detection sequence data collected in each segment is retrieved, and the voltage at each sampling point is calculated one by one. With segmented starting voltage The difference is calculated, and the absolute value of the difference is taken to obtain the degree of offset of each sampling point from the reference benchmark. This is achieved by summing the offset terms over the entire segment and dividing by the number of sampling points. The average offset value representing the overall voltage offset trend of this segment is obtained. This quantity reflects the stability of the segmented voltage response over time. Taking segment A1 as an example, assuming its voltage sampling sequence is the original value: ; Starting voltage The absolute values ​​of the voltage offset are then as follows: ; Unify the voltage range Under the premise of minimax normalization, the corresponding normalized value is processed as follows: Substitute the terms into the formula for the calculation, and the process is as follows: ; Within the same segment, the difference between the corresponding normalized sequences is Then the normalized average offset is: ; Set average voltage offset The preset stability evaluation range is as follows: like If the voltage change in that segment is in the low offset region, it indicates that the voltage response is relatively stable. like If the voltage change in this segment is in the middle offset range, it indicates that there is a certain amplitude fluctuation in the voltage response; like If the voltage change in that segment is in the high offset region, it indicates that the voltage response fluctuates significantly.

[0035] Calculated average offset Substitute the values ​​into the interval for judgment; if they satisfy... Therefore, it belongs to the medium offset segment. The result shows that the voltage response of segment A1 has a certain degree of stability offset relative to the initial state during this test cycle. The degree of offset is consistent with the temperature change range and the cumulative current change in the same level segment. Therefore, it can be classified as a medium stability state unit. Its value will be directly used as the voltage response characteristic parameter in the steady-state characteristic parameter group, and integrated with the temperature change range and the cumulative current change data of this segment as the input parameter for constructing the three-dimensional stability lattice in the subsequent steps. It is used to map the positional relationship of the lattice represented by this segment in the vertical and horizontal coordinates and its lattice strength level.

[0036] Please see Figure 5 The specific steps for obtaining stable distribution matrix data are as follows: S411: Based on the steady-state characteristic parameter set, analyze the temperature difference span of each section, combine the maximum and minimum temperature data, calculate the temperature difference distribution of each section, determine the trend of temperature fluctuation between different sections, optimize the distribution of temperature data in the vertical direction of three-dimensional space, and obtain the vertical coordinate set of the lattice. The temperature data set for each segment is read, and the highest and lowest temperature values ​​in the set are extracted segment by segment and marked as the maximum and minimum temperature points of the segment. The temperature difference between the two points is then used as the temperature difference span of the segment. A temperature difference span list is then created to index the temperature changes of all segments. All values ​​in the temperature difference span list are sorted according to the test unit number, and three fluctuation levels are distinguished according to the span size: if the temperature difference span of a segment is less than 3℃, it is classified as a low fluctuation segment; if the temperature difference span is between 3℃ and 7℃, it is classified as a medium fluctuation segment; and if the temperature difference span is greater than 7℃, it is classified as a high fluctuation segment. The fluctuation level distribution of multiple segments under the same test unit is further statistically analyzed, and the direction of temperature difference span change between adjacent segments is read. The fluctuation level of the previous segment is compared with that of the current segment. If the temperature difference span between two consecutive segments is greater than 3℃, the fluctuation level is determined. If the temperature increase or decrease exceeds 2℃, the trend between segments is marked as a sudden increase or decrease; otherwise, it is recorded as a gradual change. Based on this trend, it is determined whether the segment belongs to an abnormal temperature fluctuation zone. For example, the temperature difference of segment 1 in test unit U_062 is 2.1℃, and segment 2 is 7.9℃, with a span difference of 5.8℃ between the two segments. This is marked as a segment with a sudden temperature trend change. At the same time, the temperature difference span of each segment is used as the mapping basis for the vertical coordinate. The temperature difference values ​​are normalized, and the maximum span is set as the reference high value and the minimum span as the reference low value. The temperature fluctuation levels of all segments are mapped to the vertical axis of three-dimensional space for subsequent point space construction. The generated point space vertical coordinate set includes test unit number, segment number, maximum temperature, minimum temperature, temperature difference span, fluctuation level, and normalized coordinate position.

[0037] S412: Based on the vertical coordinate set of the dot matrix, calculate the current accumulation and voltage characteristics of each segment, analyze the relationship between current change and voltage change, identify the correlation of current and voltage combinations, compare the distribution of each combination in the horizontal space, and obtain the dot matrix horizontal coordinate sequence. The current sampling data sequence recorded in each segment of each test unit is read sequentially and numerically summarized. The cumulative value of all current changes within each segment is extracted as the current accumulation. Simultaneously, voltage data is read from the same segment, and all voltage sampling points within that segment are compared horizontally to extract the voltage change range of that segment, including the voltage start value, end value, and maximum and minimum values. The total voltage drift value within that segment is calculated as a voltage characteristic index. Then, the current accumulation and voltage drift of that segment are matched one-to-one to construct current-voltage pairs. All pairs are then cross-compared across the entire test unit to analyze the correlation between current increment and voltage drift between pairs, determining whether there is a positive correlation, negative correlation, or no obvious correlation trend. For example, when the current accumulation in a certain segment is 35mA, the voltage drift... One segment has a voltage of 0.85V, while another segment has 22mA and 0.40V. Comparing multiple combinations reveals similar incremental trends. These combinations are marked as positively correlated distributions. Based on the correlation, different combinations are then classified. After classifying the combination relationships, a horizontal coordinate mapping model is constructed for all combinations based on two dimensions: current accumulation and voltage drift. Combinations with larger current accumulation are mapped to the higher region of the X-axis, and combinations with larger voltage drift are mapped to the higher region of the Y-axis. Horizontal coordinate points for each segment are generated in the two-dimensional coordinate plane. Each horizontal coordinate point records the current value, voltage value, and normalized spatial position corresponding to that segment. They are also numbered sequentially. For example, the second segment of test unit U_078 corresponds to points X=0.82 and Y=0.73. The spatial mapping points of all segments are summarized to form a dot matrix horizontal coordinate sequence.

[0038] S413: Based on the horizontal coordinate sequence of the dot matrix, determine the operational status of each segment, calculate the spatial intensity of each point, and use the following formula: ; By acquiring lattice intensity parameters and optimizing the spatial lattice arrangement, stable lattice data can be obtained. Indicates the spatial coordinate position The lattice intensity parameters at that location, This indicates the operational characteristic indicators of the corresponding segment c. This represents the normalized index of the corresponding voltage characteristic at spatial index j. This represents the normalized index of the corresponding current characteristic at spatial index j. This represents the operational characteristic index of the k-th segment. This represents the difference between the current segment c and the k-th segment's operational characteristic index, where n represents the total number of segments involved in the calculation. The lattice intensity parameter is a quantitative index of the spatial distribution energy or performance level obtained by integrating the operating characteristics, voltage characteristics, and current characteristics of the sections and combining the overall performance differences between all sections in the three-dimensional coordinate system of stable distributed lattice data. This parameter reflects the stability and differences of the operating state of the test area at the coordinate points corresponding to the temperature span, current characteristics, and voltage characteristics, and has the function of directly measuring and comparing the performance of each test point in the spatial lattice distribution. The operating time, voltage fluctuation amplitude, and total accumulated current of the current segment are collected as raw data inputs. For the operating time, the raw data range is set to [118 seconds, 141 seconds]. The current segment's operating time is 132 seconds. Linear normalization is used to calculate the corresponding operating indicators. The other two comparison segments are 118 seconds and 141 seconds, respectively, after normalization. The voltage fluctuation data comes from the voltage sampling range. The maximum voltage in the current segment is 3.36V, the minimum voltage is 2.98V, the fluctuation is 0.38V, and the normalized range is [0.31V, 0.47V]. The corresponding normalized voltage index is... The total current change is calculated by summing the sampled current point sequences and multiplying by the sampling interval. The current sequence for the current segment is [1.24, 1.29, 1.27, 1.3] A, with a total of 5.1 A·s. The normalized interval is [4.2, 5.6] A·s, and the normalized current index is... Substitute the normalized parameters above into the following formula for the first step of the calculation to determine the principal term: ; Calculate the average difference term (summing the two segments): ; Integrated calculation results: ; The rules for dividing strength parameters are as follows: when When this occurs, it indicates that the operating state at that location is characterized by low intensity and instability. when When this occurs, it indicates that the position is in a moderately stable range, and the overall operating characteristics are balanced. when When classified as a high-intensity region, it indicates that the point exhibits a concentrated trend and low volatility in terms of operating characteristics, current and voltage dimensions, and possesses structural stability.

[0039] The current result of 0.8149 satisfies the requirement. Therefore, it is identified as a high-stability point and used as input for the stable distribution of point data in terms of intensity dimension. Furthermore, the intensity gradient in the vertical direction in the spatial point matrix is ​​constructed by the relative intensity difference between this value and other points, providing the necessary intensity basis for subsequent spatial structure division and local breakpoint identification.

[0040] Please see Figure 6 The specific steps for obtaining thermal stability spatial information are as follows: S511: Based on stable distributed lattice data, determine the spatial relationship of lattice units in the three-dimensional coordinate system, compare the proximity of adjacent lattices in the vertical direction and horizontal coordinates, identify the positional differences of clustered areas and boundary areas, and obtain spatial adjacency structure groups. First, load all generated lattice data records, including the 3D coordinate information and corresponding test number of each lattice element. Then, extract the horizontal, vertical, and longitudinal coordinates of each point to construct a spatial coordinate grid. Calculate the Euclidean spatial distance between each pair of lattice elements. For each lattice element, select points whose spatial distance is less than a threshold of 10 units as its neighboring elements. Next, decompose the distances of neighboring points in the vertical and horizontal directions. The decomposition method is to calculate the difference in the z-axis between points in the vertical direction and the combined displacement difference between the x-axis and y-axis in the horizontal direction. Determine whether a neighboring point constitutes actual adjacency based on whether the vertical difference is less than 2 and the horizontal difference is less than 4. If both conditions are met, mark it as a strong spatial adjacency point. If only one of the conditions is met, the point is considered a weak adjacency; otherwise, the adjacency relationship is excluded. Then, each set of adjacent points is aggregated, and clusters with more than 5 consecutive strong adjacent points are identified as clustered regions. Conversely, if points in a certain region cannot form effective adjacencies or only form weak adjacencies, the region is marked as a boundary region. For example, in the spatial distribution, test unit U_103 has strong adjacency relationships with 8 surrounding points and is classified as a clustered region. Test unit U_087 has only one weak adjacency relationship with one point and is therefore classified as a boundary region. After completing the above judgment, the adjacency attributes of all points are recorded, generating a structured data file containing test number, adjacent point number, adjacency strength, and region identifier, which is then summarized to obtain a spatial adjacency structure group.

[0041] S512: Based on the spatial adjacency structure group, compare the intensity parameter variation range of adjacent lattices, determine the variation trend of intensity parameters in the continuous distribution area, locate the lattice distribution where intensity variation is concentrated, and obtain the intensity variation characteristics. Further, the intensity parameters corresponding to each lattice element are extracted. Based on the adjacent point pairs recorded in the adjacency table, the intensity difference between adjacent points is calculated one by one, and the ratio of its absolute value to the intensity value of the initial point is recorded as the change rate. The change level is divided according to the magnitude of the change rate: a change rate between 0 and 5% is classified as a stable distribution, 5% to 15% as a slow change, and more than 15% as a drastic change. Then, the frequency of the change level of adjacent points of each lattice is counted. If a lattice has more than three adjacent points that are drastic changes, the lattice is marked as the mutation center point. Then, starting from the mutation center point, all points with associated mutations are searched within a radius of 5 units to form a mutation. The system uses a set of regional identifiers. In addition, it samples some lattice units within stable regions and calculates the standard deviation of their intensity changes. If the standard deviation is less than the set stability benchmark value of 0.8, the entire region is registered as a continuous distribution region. Finally, by combining the abrupt change region identifier set and the continuous region identifier set, a global intensity change trend map is drawn, and the abrupt high-density lattice regions are numbered and registered. For example, the region numbered CHG_02 has 23 abrupt change markers with an average change rate of 24.3%. It is classified into the drastic change group and written into the intensity change feature file. The output includes the complete intensity change feature of each lattice unit number, adjacent intensity difference, change rate, change level, and region affiliation.

[0042] S513: Based on the intensity change characteristics, adjust the layout of the lattice structure, determine the coordinates of the breakpoints in each distribution area, and aggregate the spatial coordinates of the lattice, intensity parameters and breakpoint distribution information to obtain the spatial information of thermal stability. The overall lattice is reordered according to the mutation region identifiers. Regions with high mutation density are prioritized for numbering and aggregating into individual blocks. A central density function is then constructed based on the actual coordinates and intensity values ​​of the aggregation points. The extreme points of the function are used as local reorganization centers, and the lattice arrangement structure within the region is redistributed. If there are obvious holes or breaks in the original lattice, neighboring point interpolation is used to complete or shift and compress the points, with the reorganization center as a reference. Subsequently, the edge coordinate difference is calculated for each reorganized region to identify the breakpoint coordinates at the spatial boundary. Breakpoint judgment is based on the following conditions: the point has no effective adjacency relationship and its nearest neighbor exceeds the breakpoint threshold (set to 5 for vertical and 7 for horizontal) in both the vertical and horizontal directions. The lattice elements of the component are classified as breakpoint markers. For example, the distance between adjacent points of lattice element U_192 in the x-direction is 10.3 and in the z-direction is 5.6, both exceeding the breakpoint judgment criteria. Their coordinates are recorded and marked as breakpoints. Then, the intensity parameters of the breakpoints are classified. If the parameter is significantly higher than twice the average value of the surrounding distribution, it is judged as a sudden rise breakpoint. If it is significantly lower than 50% of the average value, it is a decay breakpoint. Then, the spatial coordinate information, intensity parameters and breakpoint identifiers of each lattice element are collected into records, generating a table of data with the fields [lattice number, x coordinate, y coordinate, z coordinate, intensity value, breakpoint type]. The spatial reconstruction of the entire lattice distribution area is completed to obtain the spatial information of thermal stability.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A high-temperature testing method for wafer chips, characterized in that, Includes the following steps: S1: Based on the high temperature test platform for wafer chips, analyze the output signal of the hot stage, determine the correspondence between the temperature and number of the test unit, compare the current changes detected by the electrical test probe, combine the steady-state duration, archive the test results in sequence, and obtain the thermal steady-state parameter sequence. S2: Based on the thermal steady-state parameter sequence, filter the temperature and current time series of each test unit, determine the data change trend, analyze data fluctuations and inflection points, identify the segmentation point by the amplitude change exceeding the preset threshold, number the segments in sequence, and obtain the segmented trajectory set. S3: Based on the segmented trajectory set, calculate the temperature span of each segment, accumulate the current change of each segment, analyze the voltage offset trend, extract the segment features, and combine the segment running time to obtain the steady-state characteristic parameter set. S4: Based on the steady-state characteristic parameter set, determine the vertical coordinates of the lattice corresponding to the temperature span, combine the horizontal coordinates corresponding to the current change and voltage characteristics, calculate the intensity parameters based on the segment running time weighted, and arrange them in space to obtain stable distribution lattice data; S5: Based on the stable distribution lattice data, analyze the spatial proximity relationship of each lattice, compare the intensity parameter intervals, identify continuous regions and regions where the parameter change rate exceeds the set value, adjust the lattice structure, mark the breakpoints, and obtain the thermal stability spatial information.

2. The high-temperature testing method for wafer chips according to claim 1, characterized in that, The thermal steady-state parameter sequence includes unit temperature response, unit current characteristics, and unit duration; the segmented trajectory set includes temperature segmentation information, current segmentation information, and trajectory interval index; the steady-state characteristic parameter set includes segment temperature span, segment current accumulation, segment voltage characteristics, and segment duration; the stable distribution lattice data includes lattice vertical coordinates, lattice horizontal coordinates, and lattice intensity parameters; and the thermal stability spatial information includes spatial distribution morphology, regional stability differentiation, and breakpoint marking information.

3. The high-temperature testing method for wafer chips according to claim 1, characterized in that, The specific steps for obtaining the thermal steady-state parameter sequence are as follows: S111: Based on a high-temperature testing platform for wafer chips, analyze temperature setting parameters and heating power parameters, identify the correspondence between the number of each test unit and the thermal control zone, determine the distribution characteristics of parameters in each zone, and obtain the number-mapped parameter set; S112: Based on the numbering mapping parameter set, compare the current change sequence of each electrical test probe in the test unit, identify the current fluctuation range of adjacent data segments between test points, aggregate the current change data and numbering information in the same test unit, and obtain the unit current response sequence. S113: Based on the unit current response sequence, integrate the temperature monitoring data, current monitoring data and acquisition time information synchronously collected by each test unit during the thermal control stabilization stage, sort them according to the test unit number, aggregate them into structured monitoring results, and obtain the thermal steady-state parameter sequence.

4. The high-temperature testing method for wafer chips according to claim 1, characterized in that, The specific steps for obtaining the segmented trajectory set are as follows: S211: Based on the thermal steady-state parameter sequence, compare the changes in temperature data and current data of each test unit under the time sequence arrangement, determine the trend direction of temperature and current data over time, identify the data interval where the derivative of the rate of change exceeds the preset threshold, and obtain the temperature and current trend feature set. S212: Based on the temperature and current trend feature set, determine the trend distribution area of ​​temperature and current in each test unit, compare the change amplitude between continuous trends in data segments, locate the trend turning point in the data sequence, and obtain the turning interval index group. S213: Based on the turning interval index group, determine the fluctuation amplitude characteristics of the continuously monitored data segments in each test unit, compare the start and end point structures of each data segment, and number the segments according to the test unit detection order to obtain a set of segmented trajectories.

5. The high-temperature testing method for wafer chips according to claim 1, characterized in that, The specific steps for obtaining the steady-state characteristic parameter set are as follows: S311: Based on the segmented trajectory set, calculate the difference between the highest and lowest temperatures in each segment, and determine the temperature fluctuation of each segment by comparing the temperature change range, thereby obtaining the temperature change range of the segment. S312: Based on the temperature change range of the section, compare the continuous current detection points in each segment, calculate the change amplitude between adjacent currents, accumulate the change amplitudes, analyze the current change trend of each segment, and obtain the cumulative data of current change in the section. S313: Based on the cumulative data of current change in the section, calculate the voltage offset rate between the voltage detection point and the starting voltage in each section, analyze the voltage change of the sampling point, determine the average voltage offset of each section, analyze the temperature change range and the cumulative amount of current change in the section, and combine the section running time to obtain the steady-state characteristic parameter set.

6. The high-temperature testing method for wafer chips according to claim 1, characterized in that, The specific steps for obtaining the stable distribution matrix data are as follows: S411: Based on the steady-state characteristic parameter set, analyze the temperature difference span of each section, combine the maximum and minimum temperature data, calculate the temperature difference distribution of each section, determine the trend of temperature fluctuation between different sections, optimize the distribution of temperature data in the vertical direction of three-dimensional space, and obtain the vertical coordinate set of the dot matrix. S412: Based on the vertical coordinate set of the dot matrix, calculate the current accumulation and voltage characteristics of each segment, analyze the relationship between current change and voltage change, identify the correlation of current and voltage combinations, compare the distribution of each combination in the horizontal space, and obtain the horizontal coordinate sequence of the dot matrix. S413: Based on the aforementioned horizontal coordinate sequence of the dot matrix, determine the operational status of each segment, calculate the spatial intensity of each point, and use the following formula: ； By acquiring lattice intensity parameters and optimizing the spatial lattice arrangement, stable lattice data can be obtained. Indicates the spatial coordinate position The lattice intensity parameters at that location, This indicates the operational characteristic indicators of the corresponding segment c. This represents the normalized index of the corresponding voltage characteristic at spatial index j. This represents the normalized index of the corresponding current characteristic at spatial index j. This represents the operational characteristic index of the k-th segment, and n represents the total number of segments involved in the calculation.

7. The high-temperature testing method for wafer chips according to claim 1, characterized in that, The specific steps for obtaining the thermal stability spatial information are as follows: S511: Based on the stable distribution lattice data, determine the spatial relationship of the lattice units in the three-dimensional coordinate system, compare the proximity of adjacent lattices in the vertical direction and the horizontal coordinate, identify the positional differences between the cluster area and the boundary area, and obtain the spatial adjacency structure group. S512: Based on the spatial adjacency structure group, compare the intensity parameter variation range of adjacent lattices, determine the variation trend of intensity parameters in the continuous distribution area, locate the lattice distribution where intensity variation is concentrated, and obtain the intensity variation characteristics. S513: Based on the intensity change characteristics, adjust the layout of the dot matrix structure, determine the coordinates of the breakpoints in each distribution area, and aggregate the dot matrix spatial coordinates, intensity parameters and breakpoint distribution information to obtain thermal stability spatial information.

8. The high-temperature testing method for wafer chips according to claim 1, characterized in that, The test unit refers to the smallest functional area on the wafer chip that is divided into independent testing areas. The numbering correspondence refers to the mapping between the arrangement of all test units in physical space and their numbering identifiers. The change trend refers to the direction and pattern of continuous change of temperature or current data on the test time axis.

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