Fault detection system for equipment end cache equipment in semiconductor manufacturing
By deploying multi-dimensional sensors and building a fault location model in the cache device, the problems of inaccurate fault identification and difficulty in fault location in the existing technology are solved, achieving efficient fault detection and location, and reducing production costs and time loss.
Patent Information
- Application Number
- CN202511039085.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-25
- Publication Date
- 2025-11-11
AI Technical Summary
Existing technologies cannot accurately identify complex fault types in cache devices, resulting in a high false alarm rate and difficulty in quickly locating the fault source, increasing production costs and time losses.
An array of vibration sensors, infrared thermal imagers, current transformers, and pressure sensors are deployed to construct a three-dimensional spatial fault location model and a time-series anomaly discrimination algorithm. The mechanical vibration, heat distribution, current fluctuations, and airflow pressure parameters of the equipment are collected in real time, and the fault location is fused by combining multi-dimensional fault characteristics.
It enables accurate identification and location of cache device faults, reduces false alarm rate, improves the accuracy and speed of fault location, and reduces production line downtime losses.
Smart Images
Figure CN120929936A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of fault detection technology, and more specifically, to a fault detection system for device-side cache devices in semiconductor manufacturing. Background Technology
[0002] In the semiconductor manufacturing process, equipment-side buffer devices are key components for material transfer and data storage, and their operating status directly affects the continuity of the production line and product yield.
[0003] Current common methods for detecting cache device faults have significant shortcomings: traditional single vibration sensor monitoring can only provide early warnings for obvious abnormalities in the mechanical structure of the equipment, and cannot accurately identify complex fault types such as data read / write errors in cache units or unbalanced airflow distribution; existing temperature threshold detection methods usually set fixed upper temperature values, without taking into account the dynamic thermal characteristics of the cache device under different operating conditions (such as high-load data access and low-load standby), resulting in a high false alarm rate; furthermore, existing detection schemes lack time-series analysis of the data flow inside the cache device, making it difficult to quickly locate the fault source when intermittent data congestion or transmission delays occur, often causing production line downtime for maintenance, increasing production costs and time losses.
[0004] Therefore, there is an urgent need for a fault detection system for equipment-side buffer devices in semiconductor manufacturing. By deploying array-type vibration sensors, infrared thermal imagers, current transformers, and pressure sensors in key parts of the buffer device, the system can collect mechanical vibration spectrum, thermal distribution images, current fluctuation curves, and airflow pressure parameters in real time during equipment operation. Furthermore, it can construct a three-dimensional spatial fault location model and a time-series anomaly discrimination algorithm to accurately identify and locate mechanical, thermal, electrical, and data transmission faults in the buffer device, effectively solving the aforementioned problems. Summary of the Invention
[0005] To overcome the aforementioned deficiencies of the prior art, the present invention provides a fault detection system for device-side cache devices in semiconductor manufacturing, which solves the problems mentioned in the background art through the following solutions.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a fault detection system for equipment-side buffer devices in semiconductor manufacturing, comprising:
[0007] Initialization module: Constructs a three-dimensional monitoring coordinate system and collects reference data from each sensor when the equipment is unloaded, providing a spatial positioning framework and data comparison benchmark for subsequent fault detection;
[0008] Multidimensional data real-time acquisition module: During normal equipment production and operation, it acquires data in real time to obtain real-time data input for fault detection;
[0009] Fault feature extraction module: includes time-frequency analysis and judgment of collected vibration signals, thermal fault analysis, electrical fault analysis, airflow pressure analysis, and buffer timing analysis;
[0010] Multi-dimensional fault feature fusion localization module: It fuses multi-dimensional fault indices into feature vectors to achieve fault type identification and precise three-dimensional spatial localization.
[0011] Preferably, the three-dimensional monitoring coordinate system is constructed with the geometric center of the buffer device as the origin, an X-axis established along the length of the device, a Y-axis established along the width, and a Z-axis established along the height. The reference data includes: an array-type vibration sensor collecting reference vibration spectra of various parts of the device in the 0-20kHz frequency band, with a sampling frequency of 102.4kHz, continuously collecting data for 300s, and recording the mean and standard deviation of the vibration amplitude in each frequency band; and an infrared thermal imager acquiring a reference thermal map of the surface temperature distribution of the device, with a resolution of 640×512 pixels, a sampling interval of 1s, and continuous data collection. For 10 minutes, the baseline threshold range of temperature for each area was determined; the current transformer recorded the current waveform when the equipment was unloaded, with a sampling frequency of 20kHz, and continuously collected for 500 power frequency cycles to calculate the baseline range of the effective current value; the pressure sensor measured the baseline pressure value of the airflow channel inside the buffer device, with a sampling frequency of 1kHz, and continuously collected for 2 minutes to obtain the baseline fluctuation range of pressure at each measuring point; 100 data read and write operations were simulated, and the data write time and read time for each operation were recorded to calculate the average access latency; at the same time, the total data throughput within 10 minutes was statistically analyzed to calculate the baseline average throughput.
[0012] Preferably, the real-time data acquisition method is as follows: an array-type vibration sensor continuously acquires vibration signals from various parts of the equipment at a sampling frequency of 102.4 kHz, focusing on monitoring the vibration amplitude changes in the 2-15 kHz frequency band; an infrared thermal imager acquires real-time thermal distribution images of the equipment surface at a frame rate of 25 Hz, focusing on temperature changes in the buffer unit chip area, i.e., Z = 1.2-1.5 m, X = 0.3-0.7 m, Y = 0.4-0.6 m; a current transformer acquires the equipment operating current at a sampling frequency of 20 kHz, simultaneously recording the distortion rate and harmonic components of the current waveform; a pressure sensor monitors pressure fluctuations in the internal airflow channel of the equipment at a sampling frequency of 1 kHz, i.e., the airflow channel Y = 0.1-0.2 m, paying particular attention to sudden pressure changes.
[0013] Preferably, the time-frequency analysis and judgment specifically involves: converting the time-domain vibration signal into a frequency-domain spectrum using a fast Fourier transform, calculating the amplitude spectral density of each frequency component within the 2-15kHz frequency band; and defining a vibration anomaly index. Among them, A f μ represents the vibration amplitude at the current frequency f.f and σ f respectively represent the mean and standard deviation of the vibration amplitude at this frequency recorded in the initialization module; and define the vibration anomaly index threshold VI based on the 3σ principle def , when VI def <VI, it is determined that there is a mechanical anomaly.
[0014] Preferably, for the thermal fault analysis: perform region segmentation on the infrared thermal imaging map, extract the temperature distribution of the cache unit chip region, and calculate the mean temperature T of this region mean and the temperature gradient Define the thermal fault index where, T ref represents the reference mean temperature of this region recorded in the initialization module, represents the reference value of the temperature gradient, and k represents the gradient influence coefficient; when TI > 1.2, it is determined that there is a risk of thermal fault.
[0015] Preferably, for the electrical fault analysis: perform harmonic analysis on the current signal, calculate the total distortion rate THD of the 3rd, 5th, and 7th harmonic components, and at the same time calculate the effective current value I rms , the reference effective value I ref and the deviation between the effective current value and the reference effective value Define the electrical fault index EI = 0.6·δ I + 0.4·THD, when EI > 0.3, it is determined that there is an electrical anomaly.
[0016] Preferably, for the air flow pressure analysis: perform wavelet denoising on the pressure sensor data, extract the pressure fluctuation signal of the air flow channel, and calculate the root mean square value RMS of the pressure fluctuation p , define the pressure anomaly index where, RMS p-ref represents the reference root mean square value of the pressure fluctuation recorded in the initialization module, when PI > 0.5, it is determined that there is a blockage or leakage fault in the air flow channel.
[0017] Preferably, for the cache timing analysis: collect the data access timing records of the cache device, including the data write time t w , the read time t r and the corresponding data volume B, calculate the access delay D = t r - t w , and count the data throughput T within the unit time p , define the timing anomaly index where, and represent the average delay and average throughput within the current 10s, and This indicates the average latency baseline value and average throughput baseline value recorded in the initialization module. When DI>0.8, it is determined that there is a data read / write abnormality in the cache unit.
[0018] Preferably, the feature vector The Euclidean distance algorithm is used to calculate the distance between the current fault feature vector and each mode in the preset fault mode library. Among them, F i,j The j-th feature value of the i-th preset fault mode is represented by the minimum distance d. min The corresponding fault mode is used as the current detection result, and the specific area where the fault occurred is determined based on the spatial location information of the sensor array.
[0019] The technical effects and advantages of this invention are as follows:
[0020] 1. This invention achieves more comprehensive monitoring of various types of faults by deploying array-type vibration sensors, infrared thermal imagers and other multi-dimensional sensors; the calculation of the vibration anomaly index combines the sum of squares of the deviations between the vibration amplitude of each frequency band and the reference value, which can accurately capture mechanical faults such as bearing wear and gear meshing problems. Compared with traditional single vibration sensors, the fault identification accuracy is improved.
[0021] 2. When calculating the thermal fault index, this invention considers both the average temperature and the gradient change, overcoming the shortcomings of traditional fixed threshold detection, and can detect potential overheating of cache unit chips in advance; the introduction of weighted calculation of harmonic distortion rate and current deviation rate in electrical fault analysis can effectively identify hidden electrical faults such as poor power supply filtering and short circuits between winding turns, reducing the false alarm rate.
[0022] 3. The present invention provides a method for locating cache unit faults based on pressure fluctuations and time-series data. By calculating the pressure anomaly index and the time-series anomaly index, it can accurately determine airflow channel blockage and data read / write delay faults. Combined with a fault feature fusion algorithm based on a three-dimensional coordinate system, it improves the accuracy of fault location and provides precise guidance for rapid repair. Compared with existing technologies, the fault location time is shorter, significantly reducing production line downtime losses. Attached Figure Description
[0023] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation
[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0025] As attached Figure 1 The system shown is a fault detection system for equipment-side cache devices in semiconductor manufacturing, including an initialization module, a multi-dimensional data real-time acquisition module, a fault feature extraction module, and a multi-dimensional fault feature fusion and localization module.
[0026] Initialization module: Constructs a three-dimensional monitoring coordinate system and collects reference data from each sensor when the equipment is unloaded, providing a spatial positioning framework and data comparison benchmark for subsequent fault detection;
[0027] In this embodiment, it is specifically noted that: the three-dimensional monitoring coordinate system is constructed with the geometric center of the buffer device as the origin, an X-axis is established along the length of the device, a Y-axis is established along the width, and a Z-axis is established along the height; the reference data includes: an array-type vibration sensor collecting reference vibration spectra of various parts of the device in the 0-20kHz frequency band, with a sampling frequency of 102.4kHz, continuous collection for 300s, and recording the mean and standard deviation of the vibration amplitude in each frequency band; and an infrared thermal imager acquiring a reference thermal map of the surface temperature distribution of the device, with a resolution of 640×512 pixels and a sampling interval of 1. For 10 minutes, the baseline threshold range of temperature in each area was determined. The current transformer recorded the current waveform when the equipment was unloaded, with a sampling frequency of 20kHz, and continuously collected data for 500 power frequency cycles to calculate the baseline range of the effective current value. The pressure sensor measured the baseline pressure value of the airflow channel inside the buffer device, with a sampling frequency of 1kHz, and continuously collected data for 2 minutes to obtain the baseline fluctuation range of the pressure at each measuring point. 100 data read and write operations were simulated, and the data write time and read time for each operation were recorded to calculate the average access latency. At the same time, the total data throughput within 10 minutes was statistically analyzed to calculate the baseline average throughput.
[0028] Multidimensional data real-time acquisition module: During normal equipment production and operation, it collects data in real time and obtains real-time data input for fault detection;
[0029] In this embodiment, the following details should be noted: the real-time data acquisition method is as follows: an array-type vibration sensor continuously acquires vibration signals from various parts of the device at a sampling frequency of 102.4 kHz, focusing on monitoring the vibration amplitude changes in the 2-15 kHz frequency band; an infrared thermal imager acquires real-time thermal distribution images of the device surface at a frame rate of 25 Hz, focusing on temperature changes in the buffer unit chip area, which is Z = 1.2-1.5 m, X = 0.3-0.7 m, and Y = 0.4-0.6 m; a current transformer acquires the device operating current at a sampling frequency of 20 kHz, simultaneously recording the distortion rate and harmonic components of the current waveform; and a pressure sensor monitors pressure fluctuations in the internal airflow channel of the device at a sampling frequency of 1 kHz, which is Y = 0.1-0.2 m, paying particular attention to sudden pressure changes.
[0030] Fault feature extraction module: It includes time-frequency analysis and judgment of the collected vibration signals, thermal fault analysis, electrical fault analysis, air flow pressure analysis, and cache timing analysis;
[0031] In this embodiment, specifically, it should be noted that: the time-frequency analysis and judgment is specifically: using the fast Fourier transform to convert the time-domain vibration signal into a frequency-domain spectrum, and calculating the amplitude spectral density of each frequency component in the 2 - 15 kHz frequency band; defining the vibration anomaly index where, A f represents the vibration amplitude at the current frequency f, μ f and σ f respectively represent the mean and standard deviation of the vibration amplitude at this frequency recorded in the initialization module; and defining the vibration anomaly index threshold VI def , when VI def < VI, it is determined that there is a mechanical anomaly; the thermal fault analysis: performing region segmentation on the infrared thermal imaging map, extracting the temperature distribution of the cache unit chip region, and calculating the mean temperature T mean of this region and the temperature gradient defining the thermal fault index where, T ref represents the mean temperature reference value of this region recorded in the initialization module, represents the temperature gradient reference value, and k represents the gradient influence coefficient; when TI > 1.2, it is determined that there is a thermal fault risk; the electrical fault analysis: performing harmonic analysis on the current signal, calculating the total distortion rate THD of the 3rd, 5th, and 7th harmonic components, and at the same time calculating the effective current value I rms , the reference effective value I ref and the deviation between the effective current value and the reference effective value defining the electrical fault index EI = 0.6·δ I + 0.4·THD, when EI > 0.3, it is determined that there is an electrical anomaly; the air flow pressure analysis: performing wavelet denoising on the pressure sensor data, extracting the pressure fluctuation signal of the air flow channel, and calculating the root mean square value RMS p of the pressure fluctuation, defining the pressure anomaly index where, RMS p-ref represents the reference root mean square value of the pressure fluctuation recorded in the initialization module, when PI > 0.5, it is determined that there is a blockage or leakage fault in the air flow channel; the cache timing analysis: collecting the data access timing records of the cache device, including the data write time t w , the read time t r and the corresponding data volume B, calculating the access delay D = t r - t w , and counting the data throughput T per unit timep Define the time series anomaly index in, and This represents the average latency and average throughput over the current 10 seconds. and This indicates the average latency baseline value and average throughput baseline value recorded in the initialization module. When DI>0.8, it is determined that there is a data read / write abnormality in the cache unit.
[0032] Multi-dimensional fault feature fusion localization module: It fuses multi-dimensional fault indices into feature vectors to achieve fault type identification and precise three-dimensional spatial localization.
[0033] In this embodiment, it should be specifically noted that: the feature vector The Euclidean distance algorithm is used to calculate the distance between the current fault feature vector and each mode in the preset fault mode library. Among them, F i,j The j-th feature value of the i-th preset fault mode is represented by the minimum distance d. min The corresponding fault mode is used as the current detection result, and the specific area where the fault occurred is determined based on the spatial location information of the sensor array.
[0034] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.
[0035] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A fault detection system for equipment-side buffer devices in semiconductor manufacturing, characterized in that, include: Initialization module: Constructs a three-dimensional monitoring coordinate system and collects reference data from each sensor when the equipment is unloaded, providing a spatial positioning framework and data comparison benchmark for subsequent fault detection; Multidimensional data real-time acquisition module: During normal equipment production and operation, it collects data in real time and obtains real-time data input for fault detection; Fault feature extraction module: includes time-frequency analysis and judgment of the collected vibration signals, thermal fault analysis, electrical fault analysis, airflow pressure analysis, and buffer timing analysis; Multi-dimensional fault feature fusion and localization module: It fuses multi-dimensional fault indices into feature vectors to achieve fault type identification and precise three-dimensional spatial localization.
2. The fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The three-dimensional monitoring coordinate system is constructed with the geometric center of the buffer device as the origin, establishing an X-axis along the length of the device, a Y-axis along the width, and a Z-axis along the height. The reference data includes: an array of vibration sensors collecting reference vibration spectra of various parts of the device in the 0-20kHz frequency band, with a sampling frequency of 102.4kHz, continuously collecting data for 300 seconds, and recording the mean and standard deviation of the vibration amplitude in each frequency band; and an infrared thermal imager acquiring a reference thermal map of the device's surface temperature distribution, with a resolution of 640×512 pixels, a sampling interval of 1 second, and continuous data collection for 10 seconds. The baseline threshold range of temperature for each region is determined. The current transformer records the current waveform when the equipment is unloaded, with a sampling frequency of 20kHz, and continuously collects data for 500 power frequency cycles to calculate the baseline range of the effective current value. The pressure sensor measures the baseline pressure value of the airflow channel inside the buffer device, with a sampling frequency of 1kHz, and continuously collects data for 2 minutes to obtain the baseline fluctuation range of the pressure at each measuring point. 100 data read and write operations are simulated, and the data write time and read time for each operation are recorded to calculate the average access latency. At the same time, the total data throughput within 10 minutes is statistically analyzed to calculate the baseline average throughput.
3. A fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The real-time data acquisition method is as follows: an array-type vibration sensor continuously acquires vibration signals from various parts of the equipment at a sampling frequency of 102.4kHz, focusing on monitoring the vibration amplitude changes in the 2-15kHz frequency band; an infrared thermal imager acquires real-time thermal distribution images of the equipment surface at a frame rate of 25Hz, focusing on temperature changes in the buffer unit chip area, which is Z=1.2-1.5m, X=0.3-0.7m, Y=0.4-0.6m; a current transformer acquires the equipment operating current at a sampling frequency of 20kHz, simultaneously recording the distortion rate and harmonic components of the current waveform; and a pressure sensor monitors pressure fluctuations in the internal airflow channel of the equipment at a sampling frequency of 1kHz, which is Y=0.1-0.2m, paying particular attention to sudden pressure changes.
4. A fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The time-frequency analysis and judgment are specifically as follows: The time-domain vibration signal is converted into a frequency-domain spectrum by using the fast Fourier transform, and the amplitude spectral density of each frequency component in the frequency band of 2 - 15 kHz is calculated; Define the vibration anomaly index where A f represents the vibration amplitude at the current frequency f, μ f and σ f respectively represent the mean and standard deviation of the vibration amplitude at this frequency recorded in the initialization module; And define the vibration anomaly index threshold VI def , when VI def < VI, it is determined that there is a mechanical anomaly.
5. A fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The thermal fault analysis involves segmenting the infrared thermal image into regions, extracting the temperature distribution of the cache unit chip region, and calculating the average temperature T of that region. mean With temperature gradient Define thermal failure index Among them, T ref This represents the average temperature baseline value of the region recorded in the initialization module. TI represents the baseline value of the temperature gradient, and k represents the gradient influence coefficient; when TI>1.2, a risk of thermal failure is identified.
6. A fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The electrical fault analysis involves harmonic analysis of the current signal, calculating the total distortion rate (THD) of the 3rd, 5th, and 7th harmonic components, and simultaneously calculating the effective value of the current (I). rms Reference effective value I ref And the deviation between the effective value of the current and the effective value of the reference. The electrical fault index EI is defined as 0.6·δ I +0.4·THD, when EI>0.3, an electrical anomaly is determined.
7. A fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The airflow pressure analysis involves performing wavelet denoising on the pressure sensor data, extracting the pressure fluctuation signal from the airflow channel, and calculating the root mean square (RMS) value of the pressure fluctuation. p Define the abnormal pressure index Among them, RMS p-ref This indicates the root mean square value of the pressure fluctuation reference recorded in the initialization module. When PI > 0.5, it is determined that there is a blockage or leakage fault in the airflow channel.
8. A fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The cache timing analysis involves collecting data access timing records from the cache device, including data write time t. w Reading time t r Given the corresponding data volume B, calculate the access latency D = t. r -t w And calculate the data throughput T per unit time. p Define the time series anomaly index in, and This represents the average latency and average throughput over the current 10 seconds. and This indicates the average latency baseline value and average throughput baseline value recorded in the initialization module. When DI>0.8, it is determined that there is a data read / write abnormality in the cache unit.
9. A fault detection system for equipment-side buffer devices in semiconductor manufacturing according to claim 1, characterized in that: The feature vector The Euclidean distance algorithm is used to calculate the distance between the current fault feature vector and each mode in the preset fault mode library. Among them, F i,j The j-th feature value of the i-th preset fault mode is represented by the minimum distance d. min The corresponding fault mode is used as the current detection result, and the specific area where the fault occurred is determined based on the spatial location information of the sensor array.