Fault Detection For Semiconductor Manufacturing Based On Intelligent Algorithm

Semiconductor manufacturing is one of the most complex and technologically advanced manufacturing processes.This process is usually composed of hundreds of steps.A typical manufacturing step is to synthesize silicon wafers from silicon materials and fabricate integrated circuits on newly synthesized silicon wafers.The integrated circuit is placed in a package to produce a usable product.It is necessary to effectively predict and detect product failures and defects during semiconductor manufacturing.This not only prevents sudden equipment damage,but also helps increase productivity,reduce costs,and repair time.In recent years,all manufacturing equipment is equipped with equipment/production sensors.Although real-time production monitoring is possible,the amount of data generated is huge and it is difficult to rely on humans to achieve real-time detection of production failures.In this case,big data and machine learning techniques provide the possibility of automatic real-time intelligent detection of faults and defects.In recent years,machine learning has been widely used in the manufacturing industry.Intelligent manufacturing has gradually become a research hotspot.This thesis introduced the background of intelligent manufacturing and semiconductor production fault detection,introduced the research status of intelligent algorithms based on artificial intelligence,machine learning,big data technology in the field of intelligent manufacturing,and the main work of this paper and the organizational structure of the paper.The relevant concepts and foundations of intelligent algorithms that can be used for defect detection in semiconductor manufacturing are introduced and analyzed.A Bayesian network-based semiconductor production fault detection method is given.Bayesian network is used as a failure prediction model,and each parameter and failure prediction in the production process is used as the node of the Bayesian network.The relevant parameters of the Bayesian network are learned through statistical methods and training data accumulated in the production process.Fault prediction through Bayesian network inference.The defect detection of semiconductor production based on image edge detection is given.An image edge detection algorithm based on improved ant colony algorithm is given.The process and details of the algorithm are introduced.The edge of the semiconductor wafer is detected by an algorithm to achieve semiconductor defect detection.