Crown block track detection device and method for semiconductor industry

Abstract

The invention relates to a crown block track detection device and method for the semiconductor industry, and the device comprises a crown block main body, and a detection module, a power supply and interaction all-in-one machine and a communication module which are connected to the crown block main body. The detection module comprises an industrial camera assembly, a nine-axis angle gauge, a laser displacement sensor, an infrared temperature sensor and a correlation optical fiber sensor; the power supply and interaction all-in-one machine is used for supplying power to the detection module and collecting and processing data output by the detection module; the communication module is used for transmitting the processed data to a far-end server, and the far-end server is used for monitoring the operation condition of the crown block main body and the data fed back by each element of the detection module and transmitting an analysis result to a maintenance center. According to the invention, the strict requirements of the semiconductor industry on high stability and high precision of wafer transmission equipment can be accurately met, and the technical problems existing in an existing traditional manual detection mode are solved.

Description

Technical Field

[0001] This application relates to the field of semiconductor equipment technology, and more specifically, to a crane track detection device and method for the semiconductor industry. Background Technology

[0002] In the semiconductor industry, overhead cranes transport wafers back and forth along tracks. After a period of operation, various problems inevitably arise on the crane tracks, including but not limited to: wear on the support rail surface; increased gap between the guide rail and the support rail; severe track stepping; severe wear on the traveling wheels; foreign objects intruding and adhering; wireless power supply harnesses detaching from the support frame or wearing out; and broken wireless power supply brackets. These problems can significantly reduce the lifespan of related components due to wear and tear, and may even pose significant safety hazards.

[0003] Common solutions to such problems require maintenance personnel to periodically inspect all components, including the tracks, using tools such as rulers, calipers, height gauges, feeler gauges, and various homemade tools. Visual inspection is the primary method of assessment. However, these systems typically involve long transport distances, with the total length of the tracks or wireless coils often reaching kilometers or even tens of kilometers. Furthermore, the tracks are generally more than 3 meters above the ground, requiring a significant amount of manpower. As the working hours increase, numerous misjudgments and omissions are easily introduced, further increasing maintenance costs. Summary of the Invention

[0004] The purpose of this application is to provide a crane track inspection device and method for the semiconductor industry, which can accurately match the stringent requirements of the semiconductor industry for high stability and high precision of wafer transfer equipment, and solve the technical problems of low efficiency, strong subjectivity, high rate of missed detection and false detection, prominent safety risks of high-altitude operation, and inability to achieve real-time continuous monitoring in the existing traditional manual inspection mode for semiconductor crane track inspection.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a crane track detection device for the semiconductor industry, comprising a crane body, and a detection module, a power supply and interaction integrated machine, and a communication module connected to the crane body; The detection module includes: Industrial camera components are used for visual inspection of track surfaces, guide rails, support rails, and power supply coils. A nine-axis angle gauge is used to detect the crane's operating attitude and track deviation; Laser displacement sensor, used to measure the relative position of guide rail and support rail; Infrared temperature sensor used to monitor the temperature of the track and power supply coil; Through-beam fiber optic sensor, used to detect the displacement of the power supply coil; The integrated power supply and interaction unit is used to supply power to the detection module and to collect and process the data output by the detection module; The communication module is used to transmit the processed data to a remote server. The remote server is used to monitor the operation status of the crane body and the data fed back by each component of the detection module, and transmit the analysis results to the maintenance center. The detection module draws power from the external interface of the crane.

[0006] In an optional implementation, the industrial camera assembly includes: Guide rail inspection camera, used to inspect the surface condition of guide rails; Support rail inspection camera, used to detect the surface condition of the support rail and the distance to the rail edge; A power supply testing camera is used to detect the surface condition of the power supply coil and supporting profile.

[0007] In an optional embodiment, the laser displacement sensor includes: Guide rail displacement sensor, used to measure the positional offset of the guide rail; Support rail displacement sensor, used to measure the positional offset of the support rail.

[0008] In an optional embodiment, the infrared temperature sensor includes: Support rail temperature sensor, used to detect the temperature of the wheel-rail contact surface; A coil temperature sensor is used to detect the temperature of the power supply coil.

[0009] In an optional embodiment, the through-beam fiber optic sensor is mounted above the power supply coil, and the excitation optical axis of the through-beam fiber optic sensor is not lower than the top surface of the power supply coil.

[0010] In an optional embodiment, an epitaxial sensor module is also included, which includes at least one of a temperature and humidity sensor, a particle size sensor, a gas sensor, and a noise sensor.

[0011] In an optional implementation, the detection module is installed on the front drive wheel assembly and / or the rear drive wheel assembly; All components of the detection module are integrated and installed on the same crane body, or different components of the detection module are installed in combination on different crane bodies.

[0012] Secondly, the present invention provides a crane track detection method for the semiconductor industry, which is performed according to any one of the foregoing embodiments using a crane track detection device for the semiconductor industry, and includes the following steps: A detection module is installed on the main body of the crane, and power is supplied to the detection module through the external interface of the main body of the crane. During the operation of the overhead crane, multi-dimensional data of the track and its accessories are collected in real time. The collected data is transmitted to a remote server; Remote servers perform anomaly detection, fault warning, or health status assessment based on data.

[0013] In optional embodiments, the detection includes at least one of the following: track sag detection, guide rail and support rail spacing detection, drive wheel wear or deformation detection, power supply coil detachment or wear detection, support rail surface wear detection, and guide rail surface wear or breakage detection.

[0014] In an optional implementation, the following steps are also included: Establish a device health database based on historical data; By comparing real-time data with historical data, trend prediction and fault warning are performed.

[0015] The overhead crane track detection device and method for the semiconductor industry described in this application integrates the principles of visual imaging, laser ranging, multi-dimensional mechanical sensing, and infrared temperature detection, combined with an intelligent analysis model constructed by a back-end deep learning algorithm. Through training with historical detection data and optimization of the scenario-based model, it achieves accurate identification, quantitative analysis, and trend prediction of various anomalies such as track deformation, loose attachments, wear and aging, and surface defects.

[0016] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overhead crane track detection device used in the semiconductor industry according to this application; Figure 2 for Figure 1 A schematic diagram of the side cross-sectional structure; Figure 3 for Figure 1 A top-view structural diagram; Figure 4 This is a schematic diagram of the process logic for single-sided track descent detection; Figure 5 This is a schematic diagram of the process logic for one type of dual-track vertical descent detection; Figure 6This is a schematic diagram of the process logic for another type of dual-track vertical descent detection; Figure 7 A schematic diagram illustrating the process logic for detecting the distance between the guide rail and the support rail; Figure 8 This is a schematic diagram of the process logic for detecting irregular deformation or damage in wheel systems. Figure 9 This is a schematic diagram of the process logic for wheel train wear detection; Figure 10 A schematic diagram of the process logic for testing aluminum profiles for power supply coils; Figure 11 A schematic diagram of the process logic for detecting wear and dislocation of power supply coils; Figure 12 A schematic diagram of the process logic supporting rail surface inspection; Figure 13 This is a schematic diagram of the process logic for surface inspection of guide rails.

[0019] icon: 1-Nine-axis angle gauge; 2-Laser displacement sensor; 3-Infrared temperature sensor; 4-Through-beam fiber optic sensor; 5-Power supply and interactive integrated machine; 6-Temperature and humidity sensor; 7-Noise sensor; 8-Guide rail detection camera; 9-Support rail detection camera; 10-Power supply detection camera. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] In the description of this application, it should be noted that the terms "inner" and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing this application and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "setup" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0023] The overhead crane track inspection device and method in this application are mainly used to improve the maintenance and inspection conditions of wafer handling overhead cranes in the semiconductor industry. By replacing the cumbersome mode of relying on manual inspection, it not only improves the inspection efficiency, but also enables the inspection accuracy to reach the micron level by means of the multi-dimensional data acquisition capability of sensors and the intelligent decision-making advantage of deep learning, effectively avoiding the human error of manual inspection.

[0024] It supports 24 / 7 uninterrupted real-time monitoring, which can provide early warning of potential fault risks and avoid production accidents such as crane downtime and wafer damage caused by track failures, thus significantly reducing downtime losses and maintenance costs. In addition, the detection data can be traced and visualized throughout the entire process, providing data support for the entire life cycle management of the track, helping semiconductor manufacturing to upgrade to intelligence, unmanned operation and high efficiency, effectively ensuring the stability and safety of the wafer transfer process, and laying a solid foundation for the semiconductor factory's capacity improvement and yield guarantee.

[0025] See Figure 1 and combined Figures 2-3 The overhead crane track detection device for the semiconductor industry in this application includes an overhead crane body, and a detection module, a power supply and interaction unit 5 and a communication module connected to the overhead crane body.

[0026] The detection module includes: Industrial camera components are used for visual inspection of track surfaces, guide rails, support rails, and power supply coils. Nine-axis angle gauge 1, used to detect the crane's operating attitude and track deviation; Laser displacement sensor 2 is used to measure the relative position of the guide rail and the support rail; Infrared temperature sensor 3 is used to monitor the temperature of the track and power supply coil; Through-beam fiber optic sensor 4 is used to detect the displacement of the power supply coil; The power supply and interaction integrated machine 5 is used to supply power to the detection module and to collect and process the data output by the detection module; The communication module is used to transmit the processed data to a remote server. The remote server is used to monitor the operation status of the crane body and the data fed back by each component of the detection module, and transmit the analysis results to the maintenance center. The detection module draws power from the external interface of the crane.

[0027] Specifically, the overhead crane track detection device for the semiconductor industry in this application mainly includes: an industrial camera assembly, a nine-axis angle gauge 1, a laser displacement sensor 2, an infrared temperature sensor 3, and a through-beam fiber optic sensor 4.

[0028] The components are installed on the crane body and draw power from the crane body, but data transmission can be selected to be transferred via the crane or directly connected to the remote server through the communication module, depending on the site requirements.

[0029] The remote server simultaneously monitors the crane's operation and data feedback from various components on the mechanism. It automatically analyzes and fits the two data sources to accurately and efficiently detect various defects and provide real-time feedback to the maintenance center. This highly automated process, requiring no human intervention, effectively reduces the maintenance costs of the detection equipment.

[0030] The power supply and interaction integrated unit 5 serves as the core control unit of the entire testing device. It incorporates an industrial-grade power supply module, enabling stable power supply through the crane's standard external interface. This allows for unified power management of various sensors (temperature, displacement, vision, angle gauges, etc.), ensuring stable operation of each testing module. Simultaneously, the integrated unit integrates a multi-protocol compatible data acquisition interface, capable of receiving and integrating raw data from all sensors in real time. Built-in preprocessing algorithms perform data noise reduction, format standardization, and preliminary screening, ensuring the accuracy and effectiveness of data transmission.

[0031] The data transmission process supports dual-path redundancy: data can be directly transmitted using the existing communication modules of the overhead crane, or a highly reliable transmission channel can be established directly through an industrial-grade wireless AP. Both methods feature low latency and anti-interference characteristics, ensuring rapid data upload to the backend intelligent analysis platform. The backend system, based on deep learning algorithms and historical databases, performs fusion analysis, anomaly identification, and trend prediction on the integrated multi-dimensional data, ultimately generating an equipment health status assessment report and maintenance recommendations. This provides end-to-end data support for the precise operation and maintenance of the overhead crane track and its accessories, achieving integrated closed-loop management of detection, transmission, analysis, and decision-making.

[0032] The industrial camera component in this application includes: Front wheel guide rail inspection camera 8 and rear wheel guide rail inspection camera 8: The core is used to detect the surface condition of the guide rail, specifically for the precise detection of the end faces on both sides of the guide rail. The focus is on identifying potential safety hazards such as cracks, surface wear, and fractures that may occur during the long-term high-frequency operation of the overhead crane, providing real-time monitoring support for the operational stability of the guide system.

[0033] Front wheel support rail detection camera 9 and rear wheel support rail detection camera 9: The core is used to detect the surface condition of the support rail and the distance to the rail edge. Specifically, it focuses on detecting the surface condition of the contact surface between the support rail and the wheel system, and simultaneously takes into account the high-precision measurement of the distance to the rail edge. It accurately identifies problems such as deviation between the guide rail and the support rail and plane abnormalities, and avoids the risk of wheel system wear or running jam caused by track mismatch.

[0034] Front wheel power supply detection camera 10 and rear wheel power supply detection camera 10: The core is used to detect the surface condition of the power supply coil and the supporting profile. Specifically, it detects the surface condition of the power supply coil and the power supply supporting profile, focusing on defects such as wear, deformation, cracks, and corrosion, to ensure the integrity and stability of the power transmission link and avoid crane shutdown or abnormal operation caused by power supply component failure.

[0035] The nine-axis angle gauge 1 has the ability to acquire multi-dimensional data synchronously, and can provide real-time feedback of dynamic angle values ​​of three rotation axes and acceleration data of three Cartesian axes, realizing comprehensive monitoring of cross-axis deviations during crane operation.

[0036] By accurately analyzing the collected data, key indicators such as wheel wear, support rail parallelism and deformation or pitting, and track sag or fault can be scientifically assessed. This allows for timely identification of potential operational deviations caused by component wear or track abnormalities, providing data support for the health status diagnosis of the crane transmission system and ensuring the stability and safety of equipment operation.

[0037] Laser displacement sensor 2 includes a front wheel guide rail displacement sensor and a rear wheel guide rail displacement sensor. It detects the positional offset of the guide rail relative to the foundation mounting position in real time. This data is deeply fused and analyzed with the rail edge dimension data collected by the track inspection camera to accurately calculate the relative positional fluctuation value between the guide rail and the support rail edges. Closed-loop monitoring ensures that the relative gap between the two is always controlled within a preset safe range, effectively avoiding operational jamming and excessive component wear caused by excessively small gaps, as well as the risk of crane deviation or even overturning caused by excessively large gaps. This provides two-way protection for track fit accuracy.

[0038] The front and rear wheel support rail displacement sensors are used to monitor the positional offset of the support rails relative to their fixed mounting positions. Their mounting positions are offset from the drive wheels, effectively eliminating the influence of drive wheel operation interference on the reference data and ensuring the accuracy and reliability of the collected data. This data can be linked with data from other modules such as the nine-axis angle gauge 1 and the track inspection camera, and combined with historical operating databases for comprehensive analysis. This allows for the scientific assessment of wheel system wear, support rail deformation, and wear condition, providing precise data support for preventative equipment maintenance and extending the service life of the wheel-rail system.

[0039] Infrared temperature sensor 3 includes a support rail temperature sensor, used to detect the temperature of the wheel-rail contact surface, monitor the dynamic temperature changes of the contact surface between the wheel system and the support rail in real time, and accurately capture abnormal temperature fluctuations. Through continuous tracking and analysis of temperature data in the contact area, it can quickly identify abnormal temperatures caused by foreign matter peeling or intrusion adhesion, abnormal wheel system sliding friction, etc., and provide timely warnings of excessive wear or thermal damage risks caused by the accumulation of impurities on the contact surface, providing real-time temperature-based assurance for the safe operation of the wheel-rail transmission system.

[0040] The coil temperature sensor is used to monitor the temperature change trend of the power supply coil and its support frame in real time, accurately capturing temperature anomalies caused by structural abnormalities such as abnormal coil wear. By identifying potential faults such as coil overheating in advance, it can effectively avoid crane operation failures, providing key data support for the stability and durability of the conductive system, and ensuring the continuous reliability of the crane's power supply link.

[0041] The through-beam fiber sensor 4 is mounted above the power supply coil, and the excitation optical axis of the through-beam fiber sensor 4 is not lower than the top surface of the power supply coil.

[0042] Specifically, the through-beam fiber optic sensor 4 is installed above the power supply coil. Its trigger optical axis, after precise calibration, is slightly higher than the top surface of the coil, specifically designed to assist in the accurate monitoring of the power supply coil's displacement status. Through signal feedback regarding whether the optical axis is blocked or not, displacement deviations of the power supply coil caused by vibration, wear, or loose installation can be quickly identified, providing supplementary detection support for the installation stability of the power supply coil and further ensuring the reliable operation of the overhead crane power supply system.

[0043] The overhead crane track detection device in this application also includes an epitaxial sensor module, which includes at least one of a temperature and humidity sensor 6, a particle size sensor, a gas sensor, and a noise sensor 7.

[0044] Specifically, the epitaxial expansion module adopts a modular and compatible design, supports flexible selection of multiple types of sensors, and can be functionally expanded according to the differentiated monitoring needs of semiconductor fab plants. Specific adaptation scenarios are as follows: 1. Temperature and humidity sensor 6 and / or particle size sensor: To meet the environmental control requirements of clean rooms in wafer fabs, these sensors monitor real-time fluctuations in ambient temperature and humidity, as well as the concentration of airborne particulate matter (such as ISO Class 1-9 cleanliness standards), providing data support for environmental stability during wafer manufacturing and preventing process defects caused by environmental factors. 2. Gas Sensors: Accurately detect leaks of special gases (such as corrosive gases, flammable and explosive gases, toxic and harmful gases, etc.) within the wafer fab, enabling real-time early warning of leak risks and ensuring the safety of personnel and equipment operation in the production area; 3. Noise Sensor 7: Based on acoustic feature analysis technology, it monitors the noise parameters of overhead cranes (OHT / AGV) in the semiconductor workshop in real time. By identifying abnormal noise, it provides early warning of potential problems such as mechanical wear and transmission failures, thereby reducing the impact of crane downtime on the production process.

[0045] The configuration of epitaxial sensor modules enables on-demand customization of monitoring functions, significantly improving the system's coverage and adaptability to risks in multiple scenarios of wafer fabs, and providing comprehensive technical support for the stability and security of the entire semiconductor production process.

[0046] The crane track inspection device in this application enables a comprehensive upgrade in the inspection of crane tracks and accessories, with core advantages including: 1. Full coverage inspection of high-altitude / hard-to-reach areas: The track is mostly located above the equipment or in high-altitude areas of more than 3 meters. Manual inspection is difficult to achieve full coverage and high efficiency due to space limitations. However, this application uses a crane to carry the inspection module, which can complete the entire process inspection by running around the track once, without the need to build an additional work platform, thus completely solving the limitations of high-altitude inspection.

[0047] 2. Precise identification of hidden defects: For hidden defects such as wear on track support surfaces and material peeling that are difficult to detect by manual visual inspection, precise location and quantitative assessment can be achieved through multi-sensor collaborative data acquisition and deep learning algorithm analysis, completely solving the problem of "invisibility and inaccuracy" in traditional manual inspection.

[0048] 3. Precise Quantitative Detection of Key Parameters: It efficiently solves detection scenarios where manual operation is inconvenient. It can accurately measure key parameters such as single / double-sided track sag and deviation between guide rail and support rail, and output quantitative data in real time, avoiding errors caused by manual estimation and providing a reliable basis for track operation accuracy calibration.

[0049] 4. Real-time monitoring of multi-dimensional component status: Quickly identify deformation, damage and other problems on the track step surface, and provide timely warnings of potential structural safety hazards; Real-time monitoring of the wear and displacement of the power supply coil helps avoid the risk of power outages. Predicting potential problems such as fatigue damage and deformation of the power supply coil support profile in advance provides data support for preventive maintenance; It accurately detects irregular deformation and abnormal wear of the drive wheels, avoiding inaccurate positioning or track damage caused by wheel system failure.

[0050] 5. Long-term data-driven risk prediction: Based on continuously collected full-dimensional detection data, an equipment health status database is established, and risk assessment reports are generated regularly to clearly show the aging trend of components such as tracks, wheel systems, and power supply systems, realizing the transformation from "post-event maintenance" to "pre-event early warning".

[0051] 6. Avoid additional risks associated with manual inspection: Completely eliminate secondary problems that may be caused by manual inspection, such as missing inspection tools, contact scratches on the surface of the track / equipment during operation, and calibration tape residue, etc., to maximize the protection of the original working condition of the crane and track and reduce additional losses during operation and maintenance.

[0052] The core objective is to completely solve the many pain points of traditional manual inspection of overhead crane tracks and accessories: the traditional mode not only has a long inspection cycle and requires a variety of special inspection tools, but also requires special shutdowns or regional shutdowns of related lines to carry out the work, resulting in long inspection times and seriously affecting production continuity.

[0053] Meanwhile, manual inspections rely on a large number of professional personnel, resulting in high labor costs. Furthermore, the test results are highly dependent on the experience and sense of responsibility of the operators, which makes them prone to missed or false detections due to their subjectivity.

[0054] In addition, some hidden defects (such as microcracks and micro-displacement deviations) are difficult to identify accurately by visual inspection alone, and it is impossible to predict and warn of potential failures in advance. Often, the problem is not discovered until the failure occurs, resulting in passive shutdown and production losses.

[0055] The automated inspection method adopted in this application enables the overhead crane to perform real-time inspections at multiple points and in multiple dimensions simultaneously during normal operation, without requiring shutdown or disruption to the original production schedule. Through the synergistic effect of sensors and intelligent algorithms, it avoids the subjectivity and limitations of manual inspection, accurately captures hidden defects, and provides early warnings of faults, fundamentally solving the pain points of efficiency, accuracy, and safety in traditional manual inspections.

[0056] At the same time, a health management database can be established based on long-term data, transforming passive maintenance into proactive prevention.

[0057] This application also provides a crane track detection method for the semiconductor industry, based on the above-described crane track detection device for the semiconductor industry, including the following steps: A detection module is installed on the main body of the crane, and power is supplied to the detection module through the external interface of the main body of the crane. During the operation of the overhead crane, multi-dimensional data of the track and its accessories are collected in real time. The collected data is transmitted to a remote server; Remote servers perform anomaly detection, fault warning, or health status assessment based on data.

[0058] Meanwhile, the inspection includes at least one of the following: track sag inspection, guide rail and support rail spacing inspection, drive wheel wear or deformation inspection, power supply coil detachment or wear inspection, support rail surface wear inspection, and guide rail surface wear or breakage inspection.

[0059] Track sag detection includes single-sided and double-sided track sag detection. Whether it's a single or double track, sag significantly impacts the safety of overhead crane operation and the reliability of wafer transport. Firstly, it leads to uneven stress on the contact surface between the wheel system and the track, accelerating abnormal wear of the wheel system. Secondly, tilting the crane further exacerbates track settlement, creating a vicious cycle of wear and settlement. More critically, tilting can cause wafers inside the crane to detach from the support structure, posing a high risk of wafer fragmentation. Sag also increases the impact load during crane operation, causing vibration parameters to exceed limits. If the vibration value exceeds the wafer's tolerance threshold, it directly affects wafer yield. Furthermore, track sag is often caused by loose or detached fasteners or deformed support rails. If these problems are not addressed promptly, they will not only continuously affect the equipment's operating status but may also lead to major safety accidents such as crane derailment and overturning, posing a serious threat to production and asset safety.

[0060] Combination Figure 4 In the single-sided track sag detection, the nine-axis coordinates of the wheel set are obtained, and empirical values ​​within the operating range are acquired and preset. It is then determined whether the Y-axis tilt angle of the crane's front wheel exceeds the preset value, and whether this exceeds the preset value only within the entire area or at a specific outlier. If it exceeds the preset value within the entire area, the Y-axis tilt angle of the crane's rear wheel is checked. If it exceeds the preset value at a specific outlier, the process switches to track foreign object or damage detection. If the Y-axis tilt angle of the crane's rear wheel does not exceed the preset value, the process switches to wheel system wear detection. If the Y-axis tilt angle of the crane's rear wheel exceeds the preset value, it is determined whether the sensors of both the front and rear wheel sets exceed the threshold or have a similar increase. If not, the process switches to wheel system wear detection. If so, a single-sided track sag is determined.

[0061] Combination Figure 5 In one type of dual-track sag detection, the nine-axis coordinates of the wheel set are obtained, and empirical values ​​within the operating range are acquired and preset. It is then determined whether the X-axis surge or Z-axis acceleration of the crane's front wheel exceeds the preset value. It is also determined whether the exceedance occurs throughout the region or at a specific point. If the exceedance occurs throughout the region, it is then determined whether the X-axis surge or Z-axis acceleration of the crane's rear wheel exceeds the preset value. If the exceedance occurs at a specific point, the process switches to track foreign object or damage detection. If the X-axis surge or Z-axis acceleration of the crane's rear wheel exceeds the preset value, it is then determined whether the sensors of the front and rear wheel sets both exceed the threshold or have a similar increase. If not, the process switches to wheel system wear detection. If the wear occurs, dual-track sag is determined.

[0062] Combination Figure 6In another type of double-sided track sag detection, the nine-axis coordinates of the wheel set are obtained, and empirical values ​​within the operating range are obtained and preset. It is then determined whether the value of the front wheel of the crane exceeds the preset value. If it does, it is determined whether it is at the track splice point. If it is at the track splice point, it is determined whether the value of the rear wheel of the crane exceeds the preset value. If it is not at the track splice point, it is switched to track foreign object or damage detection. If the value of the rear wheel of the crane exceeds the preset value, it is determined whether the sensors of the front and rear wheel sets both exceed the threshold and the direction of change is opposite. If not, it is switched to wheel system wear detection. If it is, double-sided track sag is determined.

[0063] In the detection of the spacing between guide rails and support rails, whether it is the straight section of the traveling track and the guide rail or the arc transition section, the spacing must always be strictly controlled within the preset error range. This is the core prerequisite for ensuring the stable operation of the overhead crane. If the spacing is too small, it will cause hard friction and jamming between the overhead crane and the track during operation, and may even cause the mechanism to jam. If the spacing is too large, it will destroy the operational stability of the overhead crane, causing the equipment's traveling posture to deviate, greatly increasing the safety risk of the overhead crane tilting or even overturning, directly threatening the safety of wafer transmission and production operations.

[0064] Combination Figure 7 The specific testing process includes: Acquire data from the wheel set guide rail displacement sensor, track edge data from the track camera, and empirical values ​​within the operating range, and assign preset values; determine whether the values ​​exceed the preset values. If they do, use high-frequency laser data acquisition to evaluate whether they still exceed the preset values; if they do, compare the highest, lowest, and average values ​​to see if they exceed the empirical values ​​and include them in the high-risk observation database. If they do, determine whether they exceed the limit threshold. If they exceed the limit threshold, determine whether the guide rail or support rail is deformed. If they do not exceed the limit threshold, include them in the high-risk observation database.

[0065] In the detection of irregular deformation or damage to the wheel system, irregular deformation or damage to the drive wheel will trigger multiple chain reactions, severely affecting the crane's operational accuracy and equipment lifespan: First, it will directly lead to deviations in the crane's travel positioning accuracy, compromising the precision of wafer transport; second, it will exacerbate crane vibration, and if the vibration value exceeds the safety threshold, it will not only affect equipment stability but may also damage sensitive wafers; more importantly, a damaged or deformed drive wheel will cause the local contact area to change from normal rolling friction to sliding friction, significantly increasing the wear rate of the track surface. If not intervened in time, a negative cumulative cycle of drive wheel damage, increased vibration, positioning deviation, and track wear will form, further deteriorating the equipment's operating condition and potentially leading to serious malfunctions such as crane jamming and irreversible track damage, posing a significant risk to production continuity and wafer yield.

[0066] Combination Figure 8 The specific testing process includes: 1) Obtain data from the wheel set support rail displacement sensor, and simultaneously obtain empirical values ​​within the operating range and assign preset values. Determine if the values ​​fluctuate from the standard values ​​over a long period. If fluctuations exist, determine if the values ​​fluctuate up and down over a long period. If they fluctuate up and down over a long period, determine if the drive wheel is irregularly deformed or damaged.

[0067] 2) At the same time, obtain the nine-axis coordinates of the wheel set, and obtain the empirical values ​​within the operating range and assign preset values; determine whether the values ​​of Y-axis tilt angle and Z-axis acceleration fluctuate with the standard values ​​for a long time. If there is fluctuation, determine whether the values ​​fluctuate up and down for a long time. If they fluctuate up and down for a long time, determine that the drive wheel is irregularly deformed or damaged.

[0068] 3) In addition, the long-term fluctuation of the coil-to-beam beam group indicates irregular deformation or damage to the drive wheel.

[0069] In the detection of severe wear in the wheel system, the drive wheels, as the core transmission components of the overhead crane, are subjected to high-frequency load and rolling friction conditions for extended periods. Wear is an unavoidable natural phenomenon, but the chain reaction of damage caused by accumulated wear must be carefully monitored. As the wear of the wheel set deepens, it will first directly lead to a shift in the crane's positioning accuracy, compromising the precise control requirements for wafer transport. Furthermore, uneven wear of the wheel set will cause the crane's center of gravity to tilt towards the side with greater wear, resulting in an imbalance in the wheel system's forces. This tilt will further exacerbate the uneven wear of the wheel set. Moreover, excessive wear on one side of the wheel system can lead to crane swerving or unilateral slippage. If not addressed promptly, this will not only significantly shorten the lifespan of the drive wheels but also cause synchronous damage to the track system, ultimately affecting the crane's operational stability and posing potential risks to production safety and wafer yield.

[0070] Combination Figure 9 The specific testing process includes: 1) Obtain the nine-axis coordinates of the wheel set, and at the same time obtain the empirical values ​​within the operating range and assign preset values. Determine whether the Y-axis tilt angle exceeds the empirical value for a long time. If it exceeds the empirical value for a long time, it is determined that the deviation shows a unidirectional increase over a long period of time. If it shows a unidirectional increase over a long period of time, and at the same time, the track detection camera does not detect obvious large-area wear, it is determined that the wheel system is severely worn.

[0071] 2) At the same time, acquire the displacement sensor data of the wheel set support rail, and acquire the empirical value within the operating range and assign it to the preset value. Determine whether the value fluctuates with the standard value for a long time. If there is a judgment deviation that shows an increasing state over a long period of time, and if it shows an increasing state over a long period of time, and at the same time, the track detection camera does not detect obvious large-area wear, it is determined that the wheel system is severely worn.

[0072] 3) In addition, the coil-to-beam beam group was triggered over a long period of time over a large area. At the same time, the track detection camera did not detect any obvious large-area wear, so it was determined that the wheel system was severely worn.

[0073] In the detection of power supply coil detachment or wear, the power supply coil support profile is fixedly connected to the main profile using an integrated structure or a nested tightening process, but its own structural strength is relatively limited. During the long-term operation of the overhead crane, this profile needs to continuously withstand the cumulative effects of vibration and environmental factors, posing a potential risk of breakage or detachment.

[0074] Crucially, this support profile is situated precisely within the embrace of the overhead crane's power take-up ring, and its structural stability is directly related to power supply safety and production operation. If the support fails due to fastener vibration loosening or profile fatigue fracture, the detached parts are highly likely to collide with or become stuck in the high-speed power take-up ring. This could not only instantly cause a power outage and sudden stop of the overhead crane, but also damage the power take-up system and the core components of the overhead crane itself. Furthermore, it could trigger a chain of risks, leading to wafer transmission interruptions or even serious production accidents such as wafer fragmentation, posing a dual major threat to equipment assets and production yield.

[0075] Combination Figure 10 The specific testing process includes: 1) Obtain the travel edge values ​​of the track camera, the profile edge values ​​and profile feature point values ​​of the power supply camera, and the empirical values ​​of both within the operating range, and assign preset values; determine whether the values ​​exceed the preset values, and further determine whether they all exceed the preset values ​​within the range. If they all exceed the preset values ​​within the range, determine whether they all exceed the preset values ​​in the same range multiple times. If they all exceed the preset values, determine whether they exceed the limited threshold. If they do not exceed the preset values, determine whether there are multiple triggers within the power supply line fiber optic sensor coil area. If there are multiple triggers, determine that the aluminum profile of the power supply coil is broken or severely loosened. If the limited threshold is exceeded, determine that the aluminum profile of the power supply coil is broken or severely loosened. If the limited threshold is not exceeded, determine that there is a risk of loose screws.

[0076] 2) At the same time, the power camera detects cracks on the surface of the aluminum profile or scratches on the edge. If so, it is determined that the aluminum profile of the power supply coil is broken or severely loosened.

[0077] In the detection of wear and dislocation of power supply coils, the power supply coils are usually embedded inside the power supply bracket. Under ideal working conditions, they can maintain structural integrity and operational stability. However, considering the actual operating scenarios of semiconductor cranes, they still face multiple potential damage risks: because the power supply bracket itself is an external load-bearing component of the crane, the coil is subjected to alternating stress tension, vibration and impact during operation, coupled with the continuous corrosion of trace amounts of corrosive chemical gases in the semiconductor workshop environment, as well as occasional bumps and sudden changes in operating conditions during transportation and installation. At the same time, over time, problems such as insulation layer cracks and wear may occur, and there are even cases where the coil detaches from the aluminum profile.

[0078] Crucially, the power supply coil carries extremely high operating current for extended periods, and its operational reliability is directly related to the safety of the overhead crane and the production order. If a fault occurs, it will not only directly lead to the interruption of the overhead crane's power supply and sudden stoppage of operation, causing wafer transmission to stagnate and production losses, but it may also cause arcing due to high current short circuits, which could lead to major safety accidents such as fires. At the same time, it may also pose multiple serious threats to the clean environment of the workshop, core production equipment, and personnel safety.

[0079] Combination Figure 11 The specific testing process includes: 1) Obtain the value from the edge of the profile to the edge of the coil in the power supply camera, obtain the empirical value of the operating range and assign it to the preset value. If the power supply camera detects that the position of the coil does not match the position of the edge of the supporting aluminum profile and exceeds the preset value, it determines that the power supply coil is dislodged.

[0080] 2) Determine if the data from the six-axis sensor and the laser sensor are normal, and whether the power supply coil photoelectric area is repeatedly triggered. If so, determine that the power supply coil is dislodged. If not, proceed to the high-risk monitoring item.

[0081] 3) The power supply camera uses deep learning to determine whether there is wear on the coil surface, the width of the wear and the geometry of the wear area, and whether there are cracks on the coil surface. If so, it determines whether the same area is reproduced multiple times. If so, it determines whether the limit threshold is exceeded. If it is not exceeded, it enters the high-risk monitoring item; if it exceeds the limit threshold, it is determined that the power supply coil is damaged.

[0082] 4) If the infrared sensor of the power supply coil exceeds the empirical value, and this occurs repeatedly in the same area, the power supply coil is determined to be damaged.

[0083] In the surface inspection of the support rails, the load rails, as the core base surface for the overhead crane's load-bearing and operation, bear the heavy load pressure of the crane's high-frequency reciprocating operation, as well as the rolling friction and impact loads between the wheels and rails. Inevitably, wear and material peeling will occur on their support surfaces. On the one hand, long-term friction leads to uniform wear on the rail surface, reducing its thickness and flatness. On the other hand, impact and metal fatigue can cause localized material peeling, forming pits, burrs, or flaking debris, damaging the integrity of the rail surface. Meanwhile, although semiconductor workshops are clean environments, there is still a risk of foreign object intrusion, such as surface peeling from components above, which can easily adhere to or embed into the rail support surface.

[0084] These problems can directly trigger multiple chain reactions: First, uneven force distribution when worn and peeling rail surfaces come into contact with wheels accelerates abnormal wear and irregular deformation of the wheel treads, and may even lead to wheel defects such as edge chipping and cracks; Second, pits, burrs, or embedded foreign objects on the rail surface will increase the resistance between the wheel and rail, causing the overhead crane to vibrate more, which not only affects the stability of wafer transport (may lead to excessive wafer vibration values); In addition, if large foreign objects are not removed in time, they may cause wheel jamming, causing the overhead crane to stop suddenly, and in severe cases, causing wafer support protection failure, wafer fragments, and other production accidents.

[0085] Combination Figure 12 The specific testing process includes: 1) Obtain the values ​​of the nine-axis sensor and displacement sensor of the wheel set, obtain the empirical value within the operating range and assign it to the preset value, and determine whether the coefficient value of the crane wheel exceeds the preset value. If it exceeds the preset value, determine whether it exceeds the limit in the entire section or at a specific point. If it exceeds the limit at a specific point, determine whether it exceeds the limit in a small area. If it exceeds the limit in all areas, determine whether it is reproduced in the same area. If it is reproduced in the same area, determine that the support surface is worn. If it does not exceed the limit in a small area, combine the limit of a specific point to determine whether it is reproduced. If it is reproduced, determine that the support surface is worn.

[0086] 2) Determine if there is obvious wear on the support surface detected by the camera in the same area. If so, determine if the same pattern is reproduced in the same area. If the same pattern is reproduced in the same area, determine that the support surface is worn. 3) If obvious pitting is detected by the camera on the support surface, determine whether it will be reproduced. If it is reproduced, the support surface is determined to be worn.

[0087] 4) If the infrared sensor detects an obvious temperature anomaly, determine whether it will be reproduced. If it is reproduced, determine that the support surface is worn.

[0088] In the surface inspection of guide rails, the guide rails are the core components for the crane's operation attitude control. Their core functions and operational safety are directly related to the overall operation and maintenance effect of the crane: working together with the top guide wheels and the lower limit wheels, they can accurately limit the crane's trajectory and orientation, ensuring the stability of straight-line operation; in key working conditions such as turning and diversion, they can accurately guide the crane into the target lane according to the crane's operation instructions, ensuring the accuracy of path switching.

[0089] The condition of the track surface directly determines the safety of the crane operation: if the guide track surface is worn, it will cause the crane to wobble left and right and become unstable during operation, which will aggravate the coupling wear between the wheel system and the track. If the track is misaligned or broken in the Z direction, it will directly exceed the safety threshold for crane operation, causing major safety accidents such as overturning or falling.

[0090] Combination Figure 13 The specific testing process includes: The guide rail camera relies on deep learning to: 1) determine whether there is surface wear in a certain area. If so, perform multiple checks to see if the wear area increases or if the color gamut deepens. If so, issue a wear warning.

[0091] 2) Determine if there is a significant height misalignment in the same area, exceeding the theoretical value. If so, determine if there are significant deviations in the same area after multiple tests. If so, determine if the guide rail is loose or misaligned.

[0092] 3) If the guide rail has obvious cracks or the cracks are deepening, issue a guide rail breakage warning.

[0093] The overhead crane track inspection device in this application allows the inspection module to be optionally installed on the front drive wheel set and / or the rear drive wheel set, depending on the inspection requirements; however, it is preferred that both the front and rear wheel sets be installed simultaneously. All components of the inspection module can be installed on a single crane body, or a combination of inspection components can be installed, meaning different components of the inspection module are installed in combination on different crane bodies. Preferably, all components can be installed on one of multiple cranes, as this provides the most comprehensive range of inspection points and optimizes costs.

[0094] This application presents an automated inspection device for overhead crane tracks and their core accessories in semiconductor manufacturing scenarios, precisely matching the stringent requirements of the semiconductor industry for high stability and high precision in wafer transport equipment. It addresses the pain points of traditional manual inspection methods in semiconductor overhead crane track inspection, including low efficiency, high subjectivity, high rates of missed and false detections, significant safety risks associated with high-altitude operations, and the inability to achieve real-time continuous monitoring.

[0095] By integrating the principles of visual imaging, laser ranging, multi-dimensional mechanical sensing, and infrared temperature detection, and combining them with backend deep learning algorithms to build an intelligent analysis model, as well as training with historical detection data and optimizing the model in a scenario-based manner, the system can accurately identify, quantify, and predict the trends of various anomalies such as track deformation, loose attachments, wear and aging, and surface defects.

[0096] This application completely replaces the cumbersome manual inspection method, not only improving inspection efficiency but also achieving micron-level inspection accuracy by leveraging the multi-dimensional data acquisition capabilities of sensors and the intelligent decision-making advantages of deep learning, effectively avoiding human error in manual inspection. It also supports 24 / 7 real-time monitoring, providing early warnings of potential fault risks and preventing production accidents such as crane downtime and wafer damage caused by track malfunctions, significantly reducing downtime losses and maintenance costs. Furthermore, the inspection data is fully traceable and visualized, providing data support for the entire lifecycle management of the track, helping semiconductor manufacturing upgrade towards intelligence, automation, and high efficiency, effectively ensuring the stability and security of the wafer transport process, and laying a solid foundation for increased capacity and yield in semiconductor factories.

[0097] The core components of the testing device in this application are all optimized for low power consumption, allowing for direct installation on top of the overhead crane. Power is conveniently drawn from the crane's external ports, eliminating the need for additional power lines. The supporting structure is constructed of high-strength aluminum alloy, ensuring industrial-grade strength requirements while keeping the total weight of the entire testing equipment below 4 kg. This not only avoids interference with the crane's existing operating mechanisms but also, due to its extremely low power consumption, does not add any extra power load to the crane system, ensuring the stability of the crane's original operating parameters. Furthermore, the solution requires no modification to the crane itself, the installation process is simple and convenient, the installation cost is controllable and economical, and it possesses strong engineering feasibility.

[0098] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0099] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A crane track detection device for the semiconductor industry, characterized in that, It includes the main body of the crane, as well as a detection module, a power supply and interaction unit and a communication module connected to the main body of the crane; The detection module includes: Industrial camera components are used for visual inspection of track surfaces, guide rails, support rails, and power supply coils. A nine-axis angle gauge is used to detect the crane's operating attitude and track deviation; Laser displacement sensor, used to measure the relative position of guide rail and support rail; Infrared temperature sensor used to monitor the temperature of the track and power supply coil; Through-beam fiber optic sensor, used to detect the displacement of the power supply coil; The integrated power supply and interaction unit is used to supply power to the detection module and to collect and process the data output by the detection module; The communication module is used to transmit the processed data to a remote server. The remote server is used to monitor the operation status of the crane body and the data fed back by each component of the detection module, and transmit the analysis results to the maintenance center. The detection module draws power from the external interface of the crane.

2. The overhead crane track detection device for the semiconductor industry according to claim 1, characterized in that, The industrial camera assembly includes: Guide rail inspection camera, used to inspect the surface condition of guide rails; Support rail inspection camera, used to detect the surface condition of the support rail and the distance to the rail edge; A power supply testing camera is used to detect the surface condition of the power supply coil and supporting profile.

3. The overhead crane track detection device for the semiconductor industry according to claim 1, characterized in that, The laser displacement sensor includes: Guide rail displacement sensor, used to measure the positional offset of the guide rail; Support rail displacement sensor, used to measure the positional offset of the support rail.

4. The overhead crane track detection device for the semiconductor industry according to claim 1, characterized in that, The infrared temperature sensor includes: Support rail temperature sensor, used to detect the temperature of the wheel-rail contact surface; A coil temperature sensor is used to detect the temperature of the power supply coil.

5. The overhead crane track detection device for the semiconductor industry according to claim 1, characterized in that, The through-beam fiber optic sensor is mounted above the power supply coil, and the excitation optical axis of the through-beam fiber optic sensor is not lower than the top surface of the power supply coil.

6. The overhead crane track detection device for the semiconductor industry according to claim 1, characterized in that, It also includes an epitaxial sensor module, which includes at least one of a temperature and humidity sensor, a particle size sensor, a gas sensor, and a noise sensor.

7. The overhead crane track detection device for the semiconductor industry according to claim 1, characterized in that, The detection module is installed on the front drive wheel assembly and / or the rear drive wheel assembly; All components of the detection module are integrated and installed on the same crane body, or different components of the detection module are installed in combination on different crane bodies.

8. A method for detecting overhead crane tracks in the semiconductor industry, performed using the overhead crane track detection device for the semiconductor industry as described in any one of claims 1-5, characterized in that... Includes the following steps: A detection module is installed on the main body of the crane, and power is supplied to the detection module through the external interface of the main body of the crane. During the operation of the overhead crane, multi-dimensional data of the track and its accessories are collected in real time. The collected data is transmitted to a remote server; Remote servers perform anomaly detection, fault warning, or health status assessment based on data.

9. The crane track detection method for the semiconductor industry according to claim 8, characterized in that, The inspection includes at least one of the following: track sag inspection, guide rail and support rail spacing inspection, drive wheel wear or deformation inspection, power supply coil detachment or wear inspection, support rail surface wear inspection, and guide rail surface wear or breakage inspection.

10. The crane track detection method for the semiconductor industry according to claim 8, characterized in that, It also includes the following steps: Establish a device health database based on historical data; By comparing real-time data with historical data, trend prediction and fault warning are performed.

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