Overhead shuttle vehicle self-fault detection system for semiconductor manufacturing

Abstract

The invention discloses an elevated shuttle vehicle self-fault detection system for semiconductor manufacturing, and particularly relates to the field of elevated shuttle vehicle self-fault detection, which comprises a data acquisition module, a mechanical vibration anomaly detection module, a positioning deviation detection module, a motion stability detection module and a wafer carrier butt joint fault detection module, the data acquisition module is used for acquiring target data of operation of the elevated shuttle vehicle in real time through multiple types of sensors; the mechanical vibration abnormity detection module is used for carrying out statistical characteristic analysis on the vibration signals based on the target data and identifying early-stage abrasion or loosening faults of the mechanical parts; according to the invention, by collecting the multi-dimensional operation data in real time, high-precision data reflecting the real operation state of the equipment is obtained, and the advantages of identifying early fault hidden dangers of mechanical vibration abnormity and positioning deviation in advance, advancing fault discovery time from later maintenance to a real-time monitoring stage, and reducing wafer transmission delay risk are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of self-fault detection of overhead shuttles, and more particularly to a self-fault detection system of overhead shuttles used in semiconductor manufacturing. Background Art

[0002] In the semiconductor manufacturing process, the overhead shuttle, as the core equipment for wafer transportation, is responsible for transporting wafers with high precision and high speed in a clean room environment.

[0003] However, overhead shuttles may face various failure risks during long-term operation, such as mechanical wear, sensor failure, and trajectory deviation. Traditional fault detection relies on regular manual inspections and post-event repairs, which fail to detect the equipment's operating status in real time. This can easily lead to wafer transmission delays, equipment downtime, and even contaminated and scrapped wafers, seriously impacting the continuity and yield of semiconductor production lines.

[0004] Therefore, there is an urgent need for a self-fault detection system for overhead shuttles used in semiconductor manufacturing. By deploying multi-dimensional sensors on key components of the overhead shuttle, real-time collection of operating parameters and construction of a fault feature space can be achieved. By combining dynamic constraints with statistical analysis algorithms, real-time detection and early warning of typical faults such as abnormal mechanical vibration, positioning deviation, and deterioration of motion smoothness can be achieved, thus enabling real-time monitoring of the operating status of the overhead shuttle and accurate identification of faults. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a self-fault detection system for an overhead shuttle for semiconductor manufacturing, which solves the problems of strong lag and fuzzy fault location of the traditional detection method proposed in the above background technology through the following scheme.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a self-fault detection system for an overhead shuttle for semiconductor manufacturing, comprising a laser ranging sensor, a three-axis acceleration sensor, an encoder, a visual recognition terminal, and a control unit, specifically comprising:

[0007] Data acquisition module: acquires target data of elevated shuttle operation in real time through multiple types of sensors;

[0008] Mechanical vibration anomaly detection module: performs statistical feature analysis on the vibration signal based on the target data to identify early wear or looseness failures of mechanical parts;

[0009] Positioning deviation detection module: This module compares the theoretical trajectory with the actual trajectory to determine faults and detect abnormal displacement control accuracy or mechanical jamming of the drive system.

[0010] Motion stability detection module: It determines motion stability through jerk analysis and identifies abnormal control stability of the drive system;

[0011] Wafer carrier docking fault detection module: Uses visual positioning technology to detect the docking accuracy between the overhead shuttle and wafer storage or processing equipment to avoid wafer damage or contamination due to docking deviation.

[0012] Preferably, the target data includes: the vehicle lateral offset D output by the laser ranging sensor at a frequency of 200 Hz x (t); Synchronously output the vibration acceleration signal a through the three-axis acceleration sensor x (t), a y (t) and a z (t), and the vibration acceleration signal is low-pass filtered at 50Hz to remove environmental noise interference; the motor speed ω(τ) and the cumulative number of pulses N(t) are fed back in real time through the encoder; each time the visual recognition terminal completes the wafer carrier docking, it collects the positioning pin hole image and calculates the pin hole center coordinates in the image coordinate system, and converts it into the world coordinate system coordinates (x c ,y c ).

[0013] Preferably, the statistical feature analysis includes normal vibration feature modeling, real-time vibration monitoring, and fault determination; the normal vibration feature modeling is: when the overhead shuttle is running at a constant speed without load, 100 sets of normal vibration data are collected, and the root mean square value of the acceleration of each axis is calculated as a reference value, which is specifically expressed as follows: in, as well as Respectively represent the root mean square reference value of the vibration acceleration of the x, y and z axes, T represents the sampling time window, as well as Respectively represent the x-axis, y-axis and z-axis vibration acceleration signals under normal conditions; the real-time vibration monitoring is specifically expressed as: Among them, RMS x (t), RMS y (t) and RMS z (t) represents the real-time calculated root mean square value of the x-axis, y-axis and z-axis vibration acceleration, t w Indicates the sliding window time; the fault judgment: when the root mean square value of vibration of any axis exceeds 1.5 times the reference value, it is determined that there is a mechanical vibration abnormality and the first warning is issued.

[0014] Preferably, the theoretical trajectory is: according to the preset running path, based on the relationship between motor speed and displacement, the theoretical displacement is calculated Wherein, ω(τ) represents the motor speed, and p represents the motor screw pitch; the actual trajectory: calculate the actual displacement S(t) = N(t) × 0.01; the comparison is used to calculate the deviation between the actual displacement and the theoretical displacement ΔS(t) = S(t) - S th (t), and at the same time, the lateral offset cumulative error is calculated by laser ranging data Where n represents the number of sampling points, D x (t i ) and D x (t i-1 ) represents t i time and t i-1 The lateral spacing measurement value at the moment; the fault judgment: when |ΔS(t)|>0.5mm and ΔD x When (t)>0.3mm, it is determined that there is a positioning deviation fault and a second-level warning signal is issued.

[0015] Preferably, the jerk analysis: performing differential processing on the acceleration signal to calculate the jerk Among them, j x (t), j y (t) and j z (t) represents the acceleration of the x-axis, y-axis, and z-axis respectively, and Δt represents the sampling time interval. The average value of the absolute value of the acceleration is calculated as the stability indicator: Among them, SM represents the motion smoothness index, m represents the number of acceleration sampling points, and j x (t k ),j y (t k ) and j z (t k ) represent the kth acceleration sampling value of the x, y and z axes respectively; the motion smoothness determination: when the smoothness index SM exceeds 2 times the mean value under normal operating conditions, the motion smoothness is determined to be deteriorated and a third warning signal is issued.

[0016] Preferably, the visual positioning technology includes visual positioning error calculation, pin hole attitude angle deviation calculation and fault judgment; the visual positioning error calculation: define the theoretical docking position coordinates as (x0, y0), calculate the Euclidean distance between the actual positioning coordinates and the theoretical coordinates The pin hole posture angle deviation calculation: the pin hole axis direction is extracted by image edge detection, and the angle deviation Δθ=θ between the actual axis and the theoretical axis is calculated. r -θ t , where θ r represents the actual angle of the pin hole axis obtained by image fitting, θ t Indicates the theoretical axis angle; the fault judgment: when Ed When the angle is greater than 50 μm or Δθ is greater than 0.5°, a fourth warning signal is issued.

[0017] The technical effects and advantages of the present invention are as follows:

[0018] 1. The present invention deploys laser ranging sensors, three-axis acceleration sensors, encoders, and visual recognition modules in key components of the overhead shuttle to collect multi-dimensional operating data such as vehicle lateral offset, vibration acceleration, displacement trajectory, and wafer carrier docking coordinates in real time. It also removes noise interference through pre-processing algorithms such as low-pass filtering and sliding average, thereby obtaining high-precision data reflecting the true operating status of the equipment. This solves the problems of poor real-time perception of equipment status and delayed fault detection in the background technology, and achieves the goal of early identification of mechanical vibration anomalies and positioning deviations. This advances the time of fault discovery from post-maintenance to real-time monitoring, thereby reducing the risk of wafer transmission delays.

[0019] 2. By constructing a vibration RMS benchmark model, an algorithm for deviation between theoretical and actual trajectories, and an acceleration stability index, and combining it with multi-level threshold decision logic to fuse and analyze multi-source data, this invention achieves the ability to accurately identify typical faults such as mechanical vibration anomalies, positioning deviations, and motion stability degradation. This solves the problems of fuzzy fault location and difficulty in quantifying fault severity in prior art, achieving a four-level early warning classification, providing clear fault level guidance to operation and maintenance personnel, shortening troubleshooting time, and reducing unnecessary downtime losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] A self-fault detection system for an overhead shuttle vehicle used in semiconductor manufacturing includes a laser ranging sensor, a three-axis acceleration sensor, an encoder, a visual recognition terminal, and a control unit.

[0023] The laser distance measuring sensor is installed on both sides of the guide rail (to detect lateral deviation), and samples at a high frequency of 200Hz to obtain the lateral distance between the vehicle body and the guide rail. The lateral deviation reflects the wear of the guide rail or the abnormality of the vehicle body guide wheel.

[0024] The triaxial acceleration sensor is installed at the center of gravity of the vehicle body and collects triaxial vibration acceleration at a sampling frequency of 1000 Hz. Abnormal vibration is usually related to loose mechanical parts, bearing wear or transmission system failure.

[0025] The encoder is integrated into the shaft end of the drive motor and records the motor speed and pulse number in real time.

[0026] The visual recognition terminal: The linear array CCD camera collects the pin hole image each time the wafer carrier is docked, extracts the pin hole center coordinates through the image processing algorithm, and then converts them into world coordinate system coordinates through the calibration matrix for detecting docking positioning deviation.

[0027] The control unit realizes synchronous acquisition, preprocessing and fault algorithm calculation of multi-source data.

[0028] As attached Figure 1 A self-fault detection system for an overhead shuttle vehicle for semiconductor manufacturing is shown, comprising:

[0029] Data acquisition module: acquires target data of elevated shuttle operation in real time through multiple types of sensors;

[0030] In this embodiment, it should be specifically explained that: the target data includes: the vehicle lateral offset D output by the laser ranging sensor at a frequency of 200 Hz x (t); Synchronously output the vibration acceleration signal a through the three-axis acceleration sensor x (t), a y (t) and a z (t), and the vibration acceleration signal is low-pass filtered at 50Hz to remove environmental noise interference; the motor speed ω(τ) and the cumulative number of pulses N(t) are fed back in real time through the encoder; each time the visual recognition terminal completes the wafer carrier docking, it collects the positioning pin hole image and calculates the pin hole center coordinates in the image coordinate system, and converts it into the world coordinate system coordinates (x c ,y c ).

[0031] Mechanical vibration anomaly detection module: performs statistical feature analysis on the vibration signal based on the target data to identify early wear or looseness failures of mechanical parts;

[0032] In this embodiment, it should be specifically noted that the statistical feature analysis includes normal vibration feature modeling, real-time vibration monitoring, and fault determination. The normal vibration feature modeling is performed by collecting 100 sets of normal vibration data while the elevated shuttle is running at a constant speed without load, and calculating the root mean square value of the acceleration of each axis as a reference value, which is specifically expressed as: in, as well as Respectively represent the root mean square reference value of the vibration acceleration of the x, y and z axes, T represents the sampling time window, as well as They represent the vibration acceleration signals of the x, y, and z axes under normal conditions, respectively. The RMS value reflects the vibration energy level, and the baseline value represents the healthy vibration energy when the equipment is fault-free. The real-time vibration monitoring is specifically expressed as: Among them, RMS x (t), RMS y (t) and RMS z (t) represents the real-time calculated root mean square value of the x-axis, y-axis and z-axis vibration acceleration, t w The sliding window time can capture the dynamic changes of vibration energy to avoid misjudgment of data at a single moment. The fault judgment is as follows: when the root mean square value of vibration of any axis exceeds 1.5 times the reference value, it is determined that there is a mechanical vibration abnormality and a first warning is issued. 1.5 times the reference value corresponds to a 125% increase in vibration energy, which is usually caused by component wear leading to a decrease in contact stiffness or an increase in clearance, such as bearing ball wear, gear tooth surface damage, etc.

[0033] Positioning deviation detection module: This module compares the theoretical trajectory with the actual trajectory to determine faults and detect abnormal displacement control accuracy or mechanical jamming of the drive system.

[0034] In this embodiment, it is specifically necessary to explain that: the theoretical trajectory: according to the preset running path, based on the relationship between the motor speed and displacement, the theoretical displacement is calculated. Wherein, ω(τ) represents the motor speed, p represents the motor screw pitch, the motor speed is proportional to the screw rotation angle, and is converted into a linear displacement through the screw pitch to reflect the ideal running trajectory; the actual trajectory: calculate the actual displacement S(t) = N(t) × 0.01; the comparison is used to calculate the deviation between the actual displacement and the theoretical displacement ΔS(t) = S(t) - S th (t), reflects the cumulative error of the drive system. At the same time, the cumulative error of the lateral offset is calculated through the laser ranging data. Where n represents the number of sampling points, D x (t i ) and D x (t i-1 ) represents t i time and t i-1 The lateral spacing measurement value at the moment, the absolute value of the lateral offset at the adjacent moments is accumulated to reflect the serpentine swing degree of the vehicle body along the guide rail. The abnormal increase may be caused by guide rail deformation or guide wheel wear; the fault judgment: when |ΔS(t)|>0.5mm and ΔD xWhen (t)>0.3mm, it is determined that there is a positioning deviation fault and a second-level warning signal is issued. The semiconductor wafer transmission requires a positioning accuracy of <±1mm, and 0.5mm is the warning threshold; the lateral cumulative error>0.3mm indicates that the running trajectory deviates significantly from the straight line, which may cause a collision risk.

[0035] Motion stability detection module: It determines motion stability through jerk analysis and identifies abnormal control stability of the drive system;

[0036] In this embodiment, it is specifically necessary to explain that: the acceleration analysis: performing differential processing on the acceleration signal to calculate the acceleration Among them, j x (t), j y (t) and j z (t) represents the x-, y-, and z-axis jerk, respectively. Δt represents the sampling time interval. Jerk reflects the instantaneous change in acceleration and should be close to 0 during smooth operation. A larger value indicates a more severe motion shock (such as jerking during starting / braking, gear meshing shock). The average of the absolute jerk values ​​is then calculated as the stability indicator: Among them, SM represents the motion smoothness index, m represents the number of acceleration sampling points, and j x (t k ),j y (t k ) and j z (t k ) represent the kth acceleration sampling value of the x-axis, y-axis and z-axis respectively, and integrate the three-axis data to avoid single-axis interference and reflect the overall motion smoothness; the motion smoothness judgment: when the smoothness index SM exceeds 2 times the average value under normal operating conditions, the motion smoothness is judged to be deteriorated and a third warning signal is issued. The doubling of the acceleration usually corresponds to an abnormality in the control parameters of the drive system (such as failure of PID adjustment) or a sudden change in mechanical resistance (such as drying up of the guide rail lubricant), which may cause the wafer carrier to shake or even fall off.

[0037] Wafer carrier docking fault detection module: Uses visual positioning technology to detect the docking accuracy between the overhead shuttle and wafer storage or processing equipment to avoid wafer damage or contamination due to docking deviation.

[0038] In this embodiment, it is specifically necessary to explain that: the visual positioning technology includes visual positioning error calculation, pin hole posture angle deviation calculation and fault judgment; the visual positioning error calculation: define the theoretical docking position coordinates as (x0, y0), calculate the Euclidean distance between the actual positioning coordinates and the theoretical coordinates The pin hole posture angle deviation calculation: the pin hole axis direction is extracted by image edge detection, and the angle deviation Δθ=θ between the actual axis and the theoretical axis is calculated. r -θ t , where θ r represents the actual angle of the pin hole axis obtained by image fitting, θ t Indicates the theoretical axis angle; the fault judgment: when E d When the angle is greater than 50 μm or Δθ is greater than 0.5°, a fourth warning signal is issued.

[0039] The present invention deploys laser ranging sensors, three-axis acceleration sensors, encoders and visual recognition modules in the key components of the elevated shuttle vehicle to collect multi-dimensional operation data such as vehicle lateral offset, vibration acceleration, displacement trajectory and wafer carrier docking coordinates in real time, and removes noise interference through pre-processing algorithms such as low-pass filtering and sliding average, thereby obtaining high-precision data reflecting the true operating status of the equipment. This solves the problem in the background technology that "traditional manual inspections are difficult to perceive the equipment status in real time and fault detection is delayed", and achieves the goal of early identification of mechanical vibration anomalies (such as bearing wear, gear loosening), positioning deviations (such as motor step loss, guide rail deformation) and other early fault hazards, and prevents faults from occurring. The fault discovery time is advanced from post-maintenance to real-time monitoring stage, reducing the risk of wafer transmission delay risk; by constructing a vibration root mean square (RMS) benchmark model, a theoretical trajectory and actual trajectory deviation algorithm, and an acceleration smoothness index, combined with a multi-level threshold judgment logic to fuse and analyze multi-source data, the ability to accurately identify typical faults such as mechanical vibration anomalies, positioning deviation, and motion smoothness degradation is obtained, solving the problem of "fuzzy fault location and inability to quantify the severity of the fault" in the background technology, achieving a four-level early warning classification, providing operation and maintenance personnel with clear fault level guidance, shortening fault investigation time, and reducing unnecessary downtime losses.

[0040] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0041] Finally: 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 in the scope of protection of the present invention.

Claims

1. A self-fault detection system for an overhead shuttle vehicle used in semiconductor manufacturing, characterized in that: It includes a laser ranging sensor, a three-axis acceleration sensor, an encoder, a visual recognition terminal, and a control unit, specifically including: Data acquisition module: acquires target data of the elevated shuttle operation in real time through multiple types of sensors; Mechanical vibration anomaly detection module: performs statistical feature analysis on the vibration signal based on the target data to identify early wear or looseness failures of mechanical parts; Positioning deviation detection module: This module compares the theoretical trajectory with the actual trajectory to determine faults and detect abnormal displacement control accuracy or mechanical jamming of the drive system. Motion stability detection module: It determines motion stability through jerk analysis and identifies abnormal control stability of the drive system; Wafer carrier docking fault detection module: Uses visual positioning technology to detect the docking accuracy between the overhead shuttle and wafer storage or processing equipment to avoid wafer damage or contamination due to docking deviation.

2. The self-fault detection system for an overhead shuttle vehicle used in semiconductor manufacturing according to claim 1, characterized in that: The target data includes: the vehicle body lateral offset D output by the laser ranging sensor at a frequency of 200 Hz x (t); Synchronously output the vibration acceleration signal a through the three-axis acceleration sensor x (t), a y (t) and a z (t), and the vibration acceleration signal is low-pass filtered at 50Hz to remove environmental noise interference; the motor speed ω(τ) and the cumulative number of pulses N(t) are fed back in real time through the encoder; each time the visual recognition terminal completes the wafer carrier docking, it collects the positioning pin hole image and calculates the pin hole center coordinates in the image coordinate system, and converts it into the world coordinate system coordinates (x c ,y c ).

3. The self-fault detection system for an overhead shuttle vehicle used in semiconductor manufacturing according to claim 1, characterized in that: The statistical feature analysis includes normal vibration feature modeling, real-time vibration monitoring, and fault determination. The normal vibration feature modeling is as follows: when the elevated shuttle is running at a constant speed without load, 100 sets of normal vibration data are collected, and the root mean square value of the acceleration of each axis is calculated as the benchmark value, which is specifically expressed as follows: in, as well as Respectively represent the root mean square reference value of the vibration acceleration of the x, y and z axes, T represents the sampling time window, as well as Respectively represent the x-axis, y-axis and z-axis vibration acceleration signals under normal conditions; the real-time vibration monitoring is specifically expressed as: Among them, RMS x (t), RMS y (t) and RMS z (t) represents the real-time calculated root mean square value of the x-axis, y-axis and z-axis vibration acceleration, t w Indicates the sliding window time; the fault judgment: when the root mean square value of vibration of any axis exceeds 1.5 times the reference value, it is determined that there is a mechanical vibration abnormality and the first warning is issued.

4. The self-fault detection system for an overhead shuttle vehicle used in semiconductor manufacturing according to claim 1, characterized in that: The theoretical trajectory: According to the preset running path, the theoretical displacement is calculated based on the relationship between motor speed and displacement. Wherein, ω(τ) represents the motor speed, and p represents the motor screw pitch; the actual trajectory: calculate the actual displacement S(t) = N(t) × 0.01; the comparison is used to calculate the deviation between the actual displacement and the theoretical displacement ΔS(t) = S(t) - S th (t), and at the same time, the lateral offset cumulative error is calculated by laser ranging data Where n represents the number of sampling points, D x (t i ) and D x (t i-1 ) represents t i time and t i-1 The lateral spacing measurement value at the moment; the fault judgment: when |ΔS(t)|>0.5mm and ΔD x When (t)>0.3mm, it is determined that there is a positioning deviation fault and a second-level warning signal is issued.

5. The self-fault detection system for an overhead shuttle vehicle used in semiconductor manufacturing according to claim 1, characterized in that: The jerk analysis: performing differential processing on the acceleration signal to calculate the jerk Among them, j x (t), j y (t) and j z (t) represents the acceleration of the x-axis, y-axis, and z-axis respectively, and Δt represents the sampling time interval. The average value of the absolute value of the acceleration is calculated as the stability indicator: Among them, SM represents the motion smoothness index, m represents the number of acceleration sampling points, and j x (t k ),j y (t k ) and j z (t k ) represent the kth acceleration sampling value of the x, y and z axes respectively; the motion smoothness determination: when the smoothness index SM exceeds 2 times the mean value under normal operating conditions, the motion smoothness is determined to be deteriorated and a third warning signal is issued.

6. The self-fault detection system for an overhead shuttle vehicle used in semiconductor manufacturing according to claim 1, characterized in that: The visual positioning technology includes visual positioning error calculation, pin hole posture angle deviation calculation and fault judgment; the visual positioning error calculation: define the theoretical docking position coordinates as (x0, y0), calculate the Euclidean distance between the actual positioning coordinates and the theoretical coordinates The pin hole posture angle deviation calculation: the pin hole axis direction is extracted by image edge detection, and the angle deviation Δθ=θ between the actual axis and the theoretical axis is calculated. r -θ t , where θ r represents the actual angle of the pin hole axis obtained by image fitting, θ t Indicates the theoretical axis angle; the fault judgment: when E d When the angle is greater than 50 μm or Δθ is greater than 0.5°, a fourth warning signal is issued.

Images