A semiconductor device machine hand life prediction method

By dynamically collecting motion parameters of the robotic arm joints and utilizing multidimensional feature vectors and long short-term memory network models, the problem of accurate life prediction for semiconductor equipment robotic arms was solved, achieving precise life prediction, reducing equipment failure risks, and improving the reliability and efficiency of equipment operation.

CN122197657APending Publication Date: 2026-06-12NINGBO RUNHUA QUANXIN MICROELECTRONICS EQUIP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NINGBO RUNHUA QUANXIN MICROELECTRONICS EQUIP CO LTD
Filing Date
2026-05-18
Publication Date
2026-06-12

AI Technical Summary

⚠Technical Problem

Existing methods for predicting the lifespan of semiconductor equipment robotic arms cannot accurately predict their actual lifespan, leading to unplanned equipment downtime and economic losses. Furthermore, traditional methods cannot meet the requirements of the semiconductor manufacturing industry for equipment operating efficiency and reliability.

⚗Method used

By dynamically collecting motion parameters of the robotic arm joints and extracting multidimensional feature vectors, using Mahalanobis distance to map health indicators, and combining state-space models and long short-term memory network models, the remaining service life of the robotic arm is predicted. By integrating physical baseline lifespan and environmental data, accurate lifespan prediction is achieved.

🎯Benefits of technology

It enables accurate prediction of the lifespan of robotic arms, reduces economic losses caused by equipment failure, improves the reliability and efficiency of equipment operation, adapts to dynamic correlation under complex working conditions, and enhances the reliability and accuracy of prediction results.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a semiconductor equipment manipulator life prediction method, motion parameters of a manipulator joint are dynamically collected within a preset time; a multi-dimensional feature vector is extracted based on the motion parameters, a Mahalanobis distance is obtained according to the multi-dimensional feature vector and a reference state, and the Mahalanobis distance is mapped into a health degree index; based on a state space model including a state equation and an observation equation, a current time degradation rate parameter and a current wear state are obtained according to the health degree index, a number of cycles when a predicted wear amount reaches a predetermined failure threshold is obtained according to the degradation rate parameter and the current wear state, and a physical reference life is obtained according to the number of cycles and a time length corresponding to the cycles; a time sequence of the health degree index, the physical reference life and wet environment temperature and humidity data are input into a long short-term memory network model to obtain a predicted health degree trajectory; and based on a time point when the predicted health degree trajectory reaches a preset safety threshold, a remaining service life of the manipulator joint is determined.
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