Calibration judgment method and system for ATE equipment

Abstract

The invention belongs to the technical field of ATE calibration, and particularly relates to a calibration judgment method and system for ATE equipment, so as to solve the problems of excessive calibration, misjudgment calibration and decision lag of the equipment. The calibration judgment method comprises the following steps: when a device triggers a calibration event, collecting a current state parameter and an environment parameter of the device, if the state parameter and the environment parameter both meet a preset calibration permission condition, executing calibration and storing to form a credible calibration data point, otherwise, generating an abnormal alarm and not storing current calibration data; based on a plurality of credible calibration data points stored historically, the deviation trend of actual measurement values of the credible calibration data points relative to theoretical standard values is analyzed, the deviation trend is compared with a preset drift allowable threshold value, and whether the equipment needs to be recalibrated or not is judged; and accumulating the effective operation duration or the actual measurement times of the equipment after the previous calibration, and forcibly triggering the calibration event and executing the calibration when the effective operation duration or the actual measurement times exceeds a preset forced calibration threshold value.

Description

Technical Field

[0001] This invention belongs to the field of automatic test equipment (ATE) calibration technology, and specifically relates to a calibration determination method and system for ATE equipment. Background Technology

[0002] In the use of automated test equipment (ATE), calibration is a key step in ensuring the measurement accuracy of the equipment and maintaining the reliability of test results. Its core objective is to control the deviation between the actual measured value and the theoretical standard value within the allowable range by adjusting the equipment parameters. Therefore, the rationality of the calibration mechanism directly affects the overall efficiency and test quality of the ATE equipment.

[0003] Current ATE equipment calibration triggers largely rely on a single, passive mechanism, initiating calibration only when the device's measurement data deviation exceeds the hardware specification threshold. This mechanism cannot effectively distinguish the root cause of the deviation; it cannot identify systemic deviations caused by the failure of the calibration data itself, nor can it rule out anomalies caused by non-calibration factors such as poor probe contact, signal interference, or environmental fluctuations. This easily leads to miscalibration or missed calibration. On the one hand, when the device experiences temporary measurement anomalies due to non-calibration factors, it may be mistakenly judged as requiring calibration, resulting in unnecessary calibration operations. On the other hand, if the calibration data has implicitly degraded but has not reached the hardware specification threshold, the critical calibration opportunity may be missed, leading to distorted subsequent test data.

[0004] Meanwhile, the existing mechanism also suffers from significant overcalibration issues. For example, after equipment has been idle or moved, a full-channel calibration is forcibly initiated regardless of whether the equipment's actual condition is stable. Such calibration operations often take several hours to several days, severely consuming equipment uptime and reducing overall equipment efficiency (OEE). Furthermore, when measurement data shows slight fluctuations, staff often rely on experience to determine when to initiate calibration. Due to a lack of objective data support, the misjudgment rate is high, further exacerbating the efficiency losses and resource waste caused by overcalibration.

[0005] Furthermore, existing calibration decisions lack the ability to effectively utilize historical data. They cannot predict calibration data degradation trends by analyzing patterns in historical calibration data changes, leading to a long-term reliance on manual experience. This is not only inefficient but also ill-suited to adapting to individual differences in equipment and usage scenarios. For example, some equipment with high calibration data stability still requires adherence to uniform calibration cycles or triggering conditions, while some less stable equipment may pose testing risks due to untimely calibration. Overall, calibration decisions lack flexibility and scientific rigor, failing to establish an adaptive calibration management system. Summary of the Invention

[0006] The purpose of this invention is to provide a calibration determination method and system for ATE equipment to solve the problems of over-calibration, miscalibration, and delayed decision-making.

[0007] To achieve the above objectives, in a first aspect of the present invention, a calibration determination method for ATE equipment is provided, comprising the following steps: When the device triggers a calibration event, it collects the current status parameters and environmental parameters of the device. If the status parameters and environmental parameters meet the preset calibration allowable conditions, the calibration is performed and stored to form a reliable calibration data point; otherwise, an abnormal alarm is generated and the calibration data is not stored. Based on multiple historically stored trusted calibration data points, the deviation trend of the actual measured values ​​of the trusted calibration data points relative to the theoretical standard values ​​is analyzed, and the deviation trend is compared with a preset drift allowable threshold to determine whether the device needs to be recalibrated. The system accumulates the effective running time or actual number of measurements since the last calibration. When this exceeds the preset mandatory calibration threshold, a calibration event is forcibly triggered and calibration is performed.

[0008] Furthermore, in the calibration determination method for ATE equipment, the conditions for triggering the calibration event include calibration triggering conditions after the equipment is idle or moved, and the method for generating the calibration triggering conditions includes: Monitor motion sensor data and power-on status signals of the equipment; When the device is detected to switch from the powered-on state to the continuously powered-off state, and the duration of the continuous power-off exceeds the first preset threshold T_idle, a flag is generated and marked as idle and awaiting calibration. When the motion sensor data is detected to exceed the preset vibration intensity threshold, and the device subsequently enters the power-on state, a calibration flag is generated and marked as "needs calibration due to handling". If either the idle calibration flag or the movement calibration flag is present, the calibration event will be triggered over other calibration trigger conditions the next time the device is powered on.

[0009] Furthermore, in the calibration determination method for ATE equipment, neither the first preset threshold T_idle nor the vibration intensity threshold is a fixed value, but is dynamically adjusted based on the stability of the equipment's historical calibration data.

[0010] Furthermore, in the calibration determination method for ATE equipment, the method for determining the stability of the historical calibration data includes: based on multiple historically stored reliable calibration data points, if the deviation trend of the actual measured values ​​of N consecutive reliable calibration data points relative to the theoretical standard values ​​does not exceed a preset stability determination threshold, and the deviation trend does not increase significantly or fluctuate, then the historical calibration data is determined to be highly stable; otherwise, the historical calibration data is determined to be unstable, where N is a preset positive integer.

[0011] Furthermore, in the calibration determination method for ATE equipment, the status parameters include the equipment hardware operating status, test probe connection status, and functional module plug-in / plug-out status; the environmental parameters include the temperature, humidity, vibration intensity, and air pressure parameters of the environment in which the equipment is located.

[0012] Furthermore, in the calibration determination method for ATE equipment, the deviation trend of the actual measured value of the analyzed reliable calibration data point from the theoretical standard value includes: A calibration data sequence C(t) and an environmental parameter sequence E(t) are constructed, with calibration time points as the dimension. Based on the environmental parameter sequence E(t), a dynamic weighting factor W(t) is assigned to each reliable calibration data point in the calibration data sequence C(t). According to the weighting factor W(t), a weighted moving average algorithm or an exponential smoothing algorithm is used to process the calibration data sequence C(t). Linear regression analysis or nonlinear regression analysis is then performed on the processed calibration data sequence C(t) to fit the deviation trend. Furthermore, in the calibration judgment method for ATE equipment, the weighting factor W(t) is calculated using the environmental parameter weighting function F(E). The environmental parameter weighting function F(E) is a multivariate function including temperature T, humidity H, and vibration intensity V, specifically expressed as: W(t)=F(T,H,V)=α*f(T)+β*f(H)+γ*f(V), where α, β, and γ are preset weighting coefficients and satisfy α+β+γ=1. f(T), f(H), and f(V) are univariate evaluation functions for temperature, humidity, and vibration intensity, respectively, and the function value of each univariate evaluation function decreases as the degree of deviation of the corresponding parameter from the standard value increases.

[0013] In a second aspect of the present invention, a calibration determination system for ATE equipment is also provided, including a calibration trigger response module, a trend analysis determination module, and a forced calibration trigger module; The calibration trigger response module is used to collect the current status parameters and environmental parameters of the device when the device triggers a calibration event, and determine whether they meet the preset calibration allowable conditions. If they do, the device is controlled to perform calibration and stored to form a reliable calibration data point. If they do not meet the conditions, an abnormal device alarm is generated and the calibration data is not stored. The trend analysis and determination module is used to analyze the deviation trend of the actual measured value of the trusted calibration data point relative to the theoretical standard value based on multiple historically stored trusted calibration data points, compare the deviation trend with a preset drift allowable threshold, and determine whether the device needs to be recalibrated. The forced calibration trigger module is used to accumulate the effective running time or actual measurement number of the device since the last calibration. When the effective running time or actual measurement number exceeds the preset forced calibration threshold, the device calibration event is forcibly triggered.

[0014] Furthermore, in the calibration determination system for ATE equipment, the trend analysis determination module further includes a data sequence construction unit, a weighted processing unit, and a regression analysis unit. The data sequence construction unit is used to construct a calibration data sequence C(t) and an environmental parameter sequence E(t) with calibration time points as the dimension; the weighted processing unit is used to process the calibration data sequence C(t) using a weighted moving average algorithm or an exponential smoothing algorithm, and dynamically determine the weight factor W(t) of each calibration data point based on the environmental parameter sequence E(t), wherein the greater the degree of deviation of the environmental parameter from the standard environmental conditions, the smaller the value of the corresponding weight factor W(t); the regression analysis unit is used to calculate the deviation trend based on the weighted calibration data sequence C(t) through linear regression analysis or nonlinear regression analysis.

[0015] Furthermore, the calibration and determination system for ATE equipment also includes a flexible triggering module, which comprises a monitoring unit, a flag generation unit, and a priority triggering unit. The monitoring unit is used to monitor the motion sensor data and power-on status signal of the device; the flag generation unit is used to generate and mark an idle calibration flag when the device switches from the power-on running state to the continuous power-off state and the continuous power-off time exceeds a first preset threshold T_idle; and to generate and mark a transport calibration flag when the motion sensor data exceeds a preset vibration intensity threshold and the device subsequently enters the power-on state; the priority triggering unit is used to trigger the calibration event with priority over other calibration triggering conditions when the device is powered on again if either the idle calibration flag or the transport calibration flag exists.

[0016] Compared with the prior art, the present invention has at least the following technical effects: This invention first determines the equipment status and environmental parameters before calibration to construct reliable calibration data points, then analyzes the deviation trend based on historical reliable data points to determine calibration requirements, and finally sets mandatory calibration based on runtime or number of measurements. This can solve the problem of miscalibration or missed calibration caused by single passive triggering in the existing ATE equipment calibration mechanism, and avoid overcalibration caused by mandatory full-channel calibration after idleness or relocation and empirical calibration. Attached Figure Description

[0017] Figure 1 This is a flowchart of a calibration determination method for ATE equipment according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the stable distribution of calibration data in one embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the deterioration trend of calibration parameters in one embodiment of the present invention. Detailed Implementation

[0018] The following is a more detailed description of a calibration determination method and system for ATE equipment according to the present invention, with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. It should be understood that those skilled in the art can modify the invention described herein while still achieving its advantageous effects. Therefore, the following description should be understood as being of general knowledge to those skilled in the art and is not intended to limit the invention.

[0019] For clarity, not all features of the actual embodiments are described. In the following description, well-known functions and structures are not detailed in detail, as they would obscure the invention with unnecessary detail. It should be understood that in the development of any actual embodiment, numerous implementation details must be made to achieve the developer's specific objectives, such as changes from one embodiment to another according to limitations related to the system or business. Furthermore, it should be understood that such development work may be complex and time-consuming, but is merely routine work for those skilled in the art.

[0020] Based on the teachings of this specification, those skilled in the art can form new technical solutions through cross-combination of different implementation methods without creating technical contradictions. Such variations should all be considered to fall within the protection scope of this invention.

[0021] The invention is described more specifically by way of example in the following paragraphs with reference to the accompanying drawings. The advantages and features of the invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0022] Example 1 This embodiment uses an ATE (Automatic Test Equipment) device for chip voltage testing as the application object. This device is mainly used to test the power supply voltage accuracy of mobile phone processor chips. Traditional ATE equipment calibration judgments often rely on fixed cycles or manual experience, which leads to both over-calibration and under-calibration problems. Over-calibration manifests as frequent calibrations despite the equipment being in a stable state, wasting time; under-calibration manifests as the equipment drifting but not being calibrated in time, thus affecting test accuracy.

[0023] In view of this, such as Figure 1 As shown, to address these limitations of existing technologies, this embodiment proposes a calibration determination method for ATE equipment, solving the problems of inefficiency and accuracy in traditional calibration. The method includes the following steps: S1: When the device triggers a calibration event, the current status parameters and environmental parameters of the device are collected. If the status parameters and environmental parameters meet the preset calibration allowable conditions, calibration is performed and stored to form a reliable calibration data point. Otherwise, an abnormal alarm is generated and the calibration data is not stored. S2: Based on multiple historically stored trusted calibration data points, analyze the deviation trend of the actual measured value of the trusted calibration data points relative to the theoretical standard value, compare the deviation trend with the preset drift allowable threshold, and determine whether the device needs to be recalibrated; S3: Accumulate the effective running time or actual measurement count of the device since the last calibration. When the preset forced calibration threshold is exceeded, a calibration event will be forcibly triggered and calibration will be performed.

[0024] For step S1, when a calibration event is triggered by the ATE equipment due to idleness, handling, fluctuations in measurement data, or long-term use, the data acquisition phase begins. This phase requires the simultaneous acquisition of the equipment's current status parameters and environmental parameters. The selection of these two types of parameters is directly related to the reliability of the calibration data. Status parameters include the equipment's hardware operating status, test probe connection status, and functional module insertion / removal status. During acquisition, all of these statuses must be within normal range to avoid hardware failures, poor probe contact, or loose module connections affecting the accuracy of the calibration data. Environmental parameters include the temperature, humidity, vibration intensity, and air pressure of the environment in which the equipment is located. Industry calibration standards must be referenced during acquisition.

[0025] After parameter acquisition is complete, the system will determine whether the acquired status and environmental parameters meet the preset calibration allowable conditions. These preset conditions require that all acquired status and environmental parameters strictly fall within the corresponding standard ranges. If all parameters are deemed to meet the requirements, the system will initiate the formal calibration operation. During the calibration process, the system will record the calibration data for different test items in real time, and simultaneously associate and store the corresponding environmental parameters with the calibration data to form a reliable calibration data point.

[0026] If any parameter is found to be non-compliant with the standard, for example, if the environmental sensor detects that the current ambient temperature is 30℃ during a calibration, which exceeds the standard range of 25℃±3℃, the system will immediately generate a device abnormality alarm. The alarm will include local audio and visual prompts on the device and a record in the background system log. At the same time, the storage and subsequent use of the calibration data will be strictly prohibited to prevent invalid calibration data caused by abnormal environmental or device conditions from entering the historical database, thereby ensuring the accuracy and reliability of historical calibration data from the source.

[0027] In step S2, the step of analyzing the deviation trend of the actual measured values ​​of the reliable calibration data points relative to the theoretical standard values ​​includes: constructing a calibration data sequence C(t) and an environmental parameter sequence E(t) with calibration time points as the dimension; assigning a dynamic weight factor W(t) to each reliable calibration data point in the calibration data sequence C(t) based on the environmental parameter sequence E(t); processing the calibration data sequence C(t) using a weighted moving average algorithm or an exponential smoothing algorithm according to the weight factor W(t); and performing linear regression analysis or nonlinear regression analysis on the processed calibration data sequence C(t) to fit the deviation trend.

[0028] Specifically, the first step is to construct a data sequence. A sufficient number of reliable calibration data points are extracted from the historical database to construct a calibration data sequence C(t) and an environmental parameter sequence E(t). The calibration data sequence C(t) uses the time point t of each calibration as the horizontal axis, recording the error data between the actual measured values ​​of key parameters tested by the equipment at the corresponding time point and the theoretical standard values ​​(e.g., the sequence [+0.01V, +0.012V, +0.015V, ..., +0.025V]). The environmental parameter sequence E(t) records the environmental conditions of the equipment at each calibration time, including parameters such as temperature, humidity, and vibration intensity (e.g., the sequence [(25℃, 50%RH, 0.3G), (26℃, 49%RH, 0.4G), ...]).

[0029] Next, the weighting factor W(t) is calculated. Since the reliability of calibration data varies under different environmental conditions, dynamic weights W(t) need to be assigned to each data point in the calibration data sequence C(t) based on the environmental parameter sequence E(t). The weights are calculated using the environmental parameter weighting function F(E), which is a multivariate function including temperature T, humidity H, and vibration intensity V. The specific expression is W(t)=F(T,H,V)=α*f(T)+β*f(H)+γ*f(V), where α, β, and γ are preset weighting coefficients and satisfy α+β+γ=1, such as α=0.4, β=0.3, and γ=0.3. The specific values ​​can be adjusted according to the degree of influence of different environmental factors on the calibration accuracy of the equipment. f(T), f(H), and f(V) are univariate evaluation functions for temperature, humidity, and vibration intensity, respectively. The function values ​​decrease as the corresponding parameters deviate from the standard values. For example, when the standard temperature is 25℃, f(T) = 1. The function value decreases by 0.1 for every 1℃ deviation. If the ambient temperature for a calibration is 28℃, then f(T) = 0.7. Taking a calibration environment parameter (temperature 28℃, humidity 60%, vibration 0.6G) as an example, first calculate the evaluation function values ​​for each univariate (f(T) = 0.7, f(H) = 0.7, f(V) = 0.9), then substitute them into the weighting function to obtain W(t) = 0.4 × 0.7 + 0.3 × 0.7 + 0.3 × 0.9 = 0.76, thus completing the weight assignment for this calibration data point.

[0030] Finally, trend analysis and judgment are performed. First, a weighted moving average or exponential smoothing algorithm is used to process the calibration data sequence C(t). During processing, the weighting factor W(t) corresponding to each calibration data point is used as the basis, ensuring that data points with environmental conditions closer to the standard value and higher reliability have a larger proportion in the trend calculation. Then, linear or nonlinear regression analysis is performed on the weighted calibration data sequence C(t) to fit the deviation trend line of the actual measured value relative to the theoretical standard value. This deviation trend is then compared with a preset drift allowable threshold.

[0031] like Figure 2 As shown, the calibration data points are closely distributed around the calibration line y2=k2x+b2. The deviation trend line obtained by fitting does not exceed the preset drift allowable threshold. This indicates that the historical calibration data does not deviate from the theoretical line y1=k1x+b1, meaning that the equipment has not drifted. Therefore, the equipment is currently considered to be performing stably and does not require recalibration in the current period. It can be used normally for measurement without recalibration for a short period of time. Figure 3As shown, the calibration data points gradually deviate from the original calibration line y2=k2x+b2, showing a trend of deterioration in calibration parameters. The deviation trend obtained by fitting exceeds the threshold, which means that the historical calibration data has a trend of deviating from the theoretical line y1=k1x+b1 according to the calibration time. This indicates that the equipment is drifting during the period. Therefore, it is determined that the equipment has a significant drift and a recalibration operation needs to be performed in time to avoid the impact of error accumulation on the accuracy of subsequent tests.

[0032] It should be noted that the actual calibration line is not limited to the linear formula y=kx+b, but also covers other curve formulas or cases that require multi-segment calibration. However, the judgment logic is the same: the deviation trend line of the actual measured value relative to the theoretical standard value is obtained by fitting, and then the determination of whether calibration is required is based on the comparison result of the trend line and the drift allowable threshold.

[0033] Step S3 involves quantitatively monitoring equipment usage time and measurement frequency to avoid test accuracy risks caused by potential oversights in the trend analysis of step S2, thus ensuring the calibration effectiveness of the equipment during long-term operation. Specifically, a forced calibration threshold must first be preset. This threshold includes an effective runtime threshold and an actual measurement count threshold. For example, based on the ATE equipment's hardware characteristics, test scenario requirements, and industry calibration standards, the effective runtime threshold can be set to a cumulative power-on time ≥ 18 days since the last calibration, and the actual measurement count threshold can be set to a cumulative number of completed test tasks ≥ 1000 since the last calibration. During daily equipment operation, the system will accumulate the effective runtime or actual measurement count since the last calibration in real time in the background. If, even if the trend analysis in step S2 determines that recalibration is not currently required, but the accumulated effective runtime has reached or exceeded the preset effective runtime threshold since the last calibration, the system will ignore the trend determination result of step S2 and automatically trigger a calibration event. If the equipment has accumulated at least 1000 measurements before reaching the effective operating time threshold (e.g., only 10 days of operation), a mandatory calibration will be triggered. After the calibration event is triggered, the calibration operation will be performed according to the standard procedure in step S1. That is, first, it will check whether the equipment status parameters and environmental parameters meet the calibration allowable conditions. If they do, the calibration will be performed and new reliable calibration data points will be stored, and the historical calibration data will be updated. If they do not meet the conditions, an abnormal alarm will be generated and the calibration data will be discarded. Step S3 will then be used as a mandatory fallback to balance equipment usage efficiency and long-term testing accuracy, make up for the limitations of pure trend analysis, and form a complete calibration judgment closed loop.

[0034] It should be noted that the conditions for the ATE device to trigger the calibration event include calibration trigger conditions after the device is idle or moved. These calibration trigger conditions are based on real-time responses to changes in device status and have the highest priority. The generation method for these calibration trigger conditions is as follows: The system monitors the device's motion sensor data and power-on status signal in real-time or intermittently. When the system detects that the device has switched from a powered-on state to a continuously powered-off state, and the continuous power-off duration exceeds a first preset threshold T_idle, an idle-to-calibrate flag is automatically generated and marked. When the system detects that the motion sensor data exceeds a preset vibration intensity threshold, and the device subsequently re-enters the powered-on state, an moved-to-calibrate flag is generated and marked. If either of these two flags occurs, the next time the device is powered on, this trigger condition will take precedence over all other calibration trigger conditions, initiating the calibration event and avoiding measurement deviations caused by idle aging or vibration during transport.

[0035] It is worth noting that the first preset threshold T_idle and the vibration intensity threshold in the above triggering conditions are not fixed, but are dynamically adjusted according to the stability of the device's historical calibration data. Specifically, when judging the stability of historical calibration data, multiple trusted calibration data points stored in the past are used as the basis. If the deviation trend of the actual measured values ​​of N consecutive trusted calibration data points relative to the theoretical standard values ​​does not exceed the preset stability judgment threshold, and the overall deviation trend does not increase significantly or fluctuate drastically, then the stability of the device's historical calibration data is judged to be high; conversely, if the above conditions are not met, the stability is judged to be low. When the stability is high, the first preset threshold T_idle will be extended accordingly (e.g., extended from the default 15 days to 30 days), and / or the vibration intensity threshold required to trigger calibration will be increased accordingly (e.g., increased from 3G to 5G), reducing unnecessary calibration; when the stability is low, the threshold will remain at the default or be appropriately shortened or reduced, so that the device can be calibrated in a timely manner.

[0036] In addition to the high-priority triggering conditions mentioned above, there are also triggering conditions based on drift trends. These conditions rely on the analysis results of historical reliable calibration data. In step S2, the system analyzes the deviation trend of the actual measured value relative to the theoretical standard value based on multiple historically stored reliable calibration data points (e.g., calculating the slope of error change through linear regression). If this deviation trend exceeds a preset drift allowable threshold, it is determined that the device has significant drift, triggering a calibration event; if it does not exceed the threshold, it is determined that calibration is not currently required, avoiding blind operation.

[0037] Finally, there is the mandatory trigger condition, which serves as a fallback mechanism for calibration decisions and corresponds to the mandatory update requirement in step S3. The system continuously accumulates the effective operating time and actual measurement count of the device since the last calibration and presets mandatory calibration thresholds: cumulative operating time ≥ 18 days since the last calibration, or cumulative measurement count ≥ 1000 times. Regardless of the previous drift trend analysis results, as long as either threshold condition is met, a calibration event will be forcibly triggered. For example, if the device has not triggered any calibration for 18 consecutive days (and step S2 determines that calibration is not required), or has completed 1000 measurements within 10 days (not reaching the 18-day operating time threshold), the system will ignore other judgment results, initiate calibration, and update historical calibration data to prevent latent deviations caused by long-term use and ensure measurement accuracy.

[0038] In summary, this invention first determines the equipment status and environmental parameters to construct reliable calibration data points before calibration, then analyzes the deviation trend based on historical reliable data points to determine calibration requirements, and finally sets mandatory calibration based on runtime or number of measurements. This can solve the problem of miscalibration or missed calibration caused by single passive triggering in the existing ATE equipment calibration mechanism, and avoid overcalibration caused by mandatory full-channel calibration after idleness or relocation and empirical calibration.

[0039] Example 2 This embodiment proposes a calibration determination system for ATE equipment to address the problems of over- and under-calibration in traditional methods. The system includes a calibration trigger response module, a trend analysis determination module, and a forced calibration trigger module.

[0040] It should be noted that the system also includes a flexible triggering module, which monitors equipment status changes in real time. This module includes a monitoring unit, a flag generation unit, and a priority triggering unit, responsible for prioritizing high-priority calibration events. The monitoring unit collects two types of data in real time: one is equipment motion sensor data (such as monitoring vibration acceleration), and the other is equipment power-on status signals (distinguishing between three states: off-line operation, continuous shutdown, and standby). The flag generation unit generates calibration flags based on the monitoring data: when the equipment switches from on-line operation to continuous shutdown, and the shutdown duration exceeds the first preset threshold T_idle (initially set to 12 days), an idle calibration flag is automatically generated; when the vibration intensity exceeds the preset vibration threshold (initially set to 2.5G) during handling, and the equipment is subsequently restarted, a "handling calibration flag" is generated. The priority triggering unit ensures the flags are effective: if any of the above flags exist, the flexible triggering module will skip other triggering conditions and prioritize the calibration event the next time the equipment is powered on. For example, after the equipment is moved from workshop A to workshop B and turned on, the monitoring unit detects that the vibration during the transport reaches 3G (exceeding the 2.5G threshold). The flag generation unit marks that the equipment needs to be calibrated due to the transport, and the priority trigger unit immediately controls the equipment to start calibration to avoid test deviations caused by the transport.

[0041] The calibration trigger response module is used to collect the current status parameters and environmental parameters of the device when the device triggers a calibration event, and determine whether they all meet the preset calibration allowable conditions. If all parameters meet the conditions, the module controls the device to perform calibration and store the data to form a reliable calibration data point. If the conditions do not meet the conditions, the module immediately generates a device abnormality alarm and prohibits the storage of the calibration data to avoid erroneous data from affecting subsequent analysis.

[0042] The trend analysis and determination module is used to analyze the deviation trend of the actual measured values ​​of multiple historically stored reliable calibration data points relative to the theoretical standard values. It compares the deviation trend with a preset drift allowable threshold to determine whether the equipment needs recalibration. Specifically, this module not only determines whether the equipment is drifting but also performs refined analysis through sub-units, including a data sequence construction unit, a weighted processing unit, and a regression analysis unit. The data sequence construction unit is used to construct a calibration data sequence C(t) and an environmental parameter sequence E(t) with the calibration time point as the dimension. The weighted processing unit uses a weighted moving average algorithm or an exponential smoothing algorithm to process C(t). The core is to dynamically calculate the weight factor W(t) based on E(t), and the greater the deviation of the environmental parameters from the standard conditions, the lower the value of the corresponding weight factor W(t) for the calibration data point. For example, in a calibration environment with a temperature of 26℃ (deviation from the standard of 23℃) and humidity of 58% (deviation from the standard of 45%), W(t) is calculated to be 0.6 according to preset rules (W(t) = 1.0 under standard conditions), making the data deviating from the environment have a smaller impact on trend analysis. The regression analysis unit performs linear or nonlinear regression analysis on the weighted C(t) to calculate the error deviation trend. If the regression results show that the slope of the error change does not exceed the preset drift allowable threshold, it is determined that no recalibration is required; if the slope exceeds the threshold, a calibration event is triggered to avoid drift causing test deviation.

[0043] The forced calibration trigger module is used to accumulate the effective running time or actual measurement number of the device since the last calibration. When the effective running time or actual measurement number exceeds the preset forced calibration threshold, a calibration event is forcibly triggered and calibration is performed to avoid hidden deviations caused by long-term use of the device.

[0044] In summary, this system enables intelligent calibration decisions for ATE equipment, reducing unnecessary calibrations to improve equipment utilization and ensuring stable test accuracy, effectively solving the problems of over- and under-calibration in traditional methods.

[0045] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the present invention. Any equivalent substitutions or modifications made by those skilled in the art to the technical solutions and content disclosed in the present invention without departing from the scope of the present invention shall be deemed to have remained within the protection scope of the present invention.

Claims

1. A calibration determination method for ATE equipment, characterized in that, Includes the following steps: When the device triggers a calibration event, it collects the current status parameters and environmental parameters of the device. If the status parameters and environmental parameters meet the preset calibration allowable conditions, the calibration is performed and stored to form a reliable calibration data point; otherwise, an abnormal alarm is generated and the calibration data is not stored. Based on multiple historically stored trusted calibration data points, the deviation trend of the actual measured values ​​of the trusted calibration data points relative to the theoretical standard values ​​is analyzed, and the deviation trend is compared with a preset drift allowable threshold to determine whether the device needs to be recalibrated. The system accumulates the effective running time or actual number of measurements since the last calibration. When this exceeds the preset mandatory calibration threshold, a calibration event is forcibly triggered and calibration is performed.

2. The method as described in claim 1, characterized in that, The conditions that trigger the calibration event include calibration triggering conditions after the equipment is idle or moved, and the method for generating the calibration triggering conditions includes: Monitor motion sensor data and power-on status signals of the equipment; When the device is detected to switch from the powered-on state to the continuously powered-off state, and the duration of the continuous power-off exceeds the first preset threshold T_idle, a flag is generated and marked as idle and awaiting calibration. When the motion sensor data is detected to exceed the preset vibration intensity threshold, and the device subsequently enters the power-on state, a calibration flag is generated and marked as "needs calibration due to handling". If either the idle calibration flag or the movement calibration flag is present, the calibration event will be triggered over other calibration trigger conditions the next time the device is powered on.

3. The method as described in claim 2, characterized in that, The first preset threshold T_idle and the vibration intensity threshold are not fixed values, but are dynamically adjusted according to the stability of the device's historical calibration data.

4. The method as described in claim 3, characterized in that, The method for determining the stability of the historical calibration data includes: based on multiple historically stored trusted calibration data points, if the deviation trend of the actual measured value of N consecutive trusted calibration data points relative to the theoretical standard value does not exceed the preset stability judgment threshold, and the deviation trend does not increase or fluctuate significantly, then the historical calibration data is determined to be highly stable; otherwise, the historical calibration data is determined to be unstable, where N is a preset positive integer.

5. The method as described in claim 1, characterized in that, The status parameters include the device hardware operating status, test probe connection status, and functional module plug-in / plug-out status; the environmental parameters include the temperature, humidity, vibration intensity, and air pressure parameters of the environment in which the device is located.

6. The method as described in claim 1, characterized in that, The deviation trends of the actual measured values ​​of the analyzed reliable calibration data points from the theoretical standard values ​​include: A calibration data sequence C(t) and an environmental parameter sequence E(t) are constructed with calibration time points as the dimension. Based on the environmental parameter sequence E(t), a dynamic weight factor W(t) is assigned to each reliable calibration data point in the calibration data sequence C(t). According to the weight factor W(t), the calibration data sequence C(t) is processed using a weighted moving average algorithm or an exponential smoothing algorithm. Linear regression analysis or nonlinear regression analysis is then performed on the processed calibration data sequence C(t) to fit the deviation trend.

7. The method as described in claim 6, characterized in that, The weighting factor W(t) is calculated using the environmental parameter weighting function F(E), which is a multivariate function including temperature T, humidity H, and vibration intensity V. Specifically, W(t) = F(T,H,V) = α*f(T) + β*f(H) + γ*f(V), where α, β, and γ are preset weighting coefficients and satisfy α + β + γ = 1. f(T), f(H), and f(V) are univariate evaluation functions for temperature, humidity, and vibration intensity, respectively. The function value of each univariate evaluation function decreases as the corresponding parameter deviates from the standard value.

8. A calibration determination system for ATE equipment, characterized in that, It includes a calibration trigger response module, a trend analysis and judgment module, and a forced calibration trigger module; The calibration trigger response module is used to collect the current status parameters and environmental parameters of the device when the device triggers a calibration event, and determine whether they meet the preset calibration allowable conditions. If they do, the device is controlled to perform calibration and stored to form a reliable calibration data point. If they do not meet the conditions, an abnormal device alarm is generated and the calibration data is not stored. The trend analysis and determination module is used to analyze the deviation trend of the actual measured value of the trusted calibration data point relative to the theoretical standard value based on multiple historically stored trusted calibration data points, compare the deviation trend with a preset drift allowable threshold, and determine whether the device needs to be recalibrated. The forced calibration trigger module is used to accumulate the effective running time or actual measurement number of the device since the last calibration. When the effective running time or actual measurement number exceeds the preset forced calibration threshold, a calibration event is forcibly triggered and calibration is performed.

9. The system as described in claim 8, characterized in that, The trend analysis and determination module also includes a data sequence construction unit, a weighted processing unit, and a regression analysis unit; The data sequence construction unit is used to construct a calibration data sequence C(t) and an environmental parameter sequence E(t) with calibration time points as the dimension; the weighted processing unit is used to process the calibration data sequence C(t) using a weighted moving average algorithm or an exponential smoothing algorithm, and dynamically determine the weight factor W(t) of each calibration data point based on the environmental parameter sequence E(t), and the greater the degree of deviation of the environmental parameter from the standard environmental conditions, the smaller the value of the weight factor W(t) of the corresponding calibration data point; The regression analysis unit is used to calculate the deviation trend based on the weighted calibration data sequence C(t) through linear regression analysis or nonlinear regression analysis.

10. The system as described in claim 8, characterized in that, It also includes an elastic triggering module, which comprises a monitoring unit, a flag generation unit, and a priority triggering unit; The monitoring unit is used to monitor the motion sensor data and power-on status signal of the device; the flag generation unit is used to generate and mark an idle calibration flag when the device switches from the power-on running state to the continuous power-off state and the continuous power-off time exceeds a first preset threshold T_idle; and to generate and mark a transport calibration flag when the motion sensor data exceeds a preset vibration intensity threshold and the device subsequently enters the power-on state; the priority triggering unit is used to trigger the calibration event with priority over other calibration triggering conditions when the device is powered on again if either the idle calibration flag or the transport calibration flag exists.

Images