Method and device for early warning of semiconductor packaging process based on sequential generative adversarial network

Abstract

This application proposes a semiconductor packaging process early warning method and device based on timing generation confrontation network, which relates to the field of data processing technology, wherein the method includes: obtaining monitoring data, and preprocessing the monitoring data to obtain training data; Obtain a normal sample set, calculate the density function of the normal sample set, and estimate the abnormal threshold according to the density function; obtain the trained generator network, use the trained generator network to generate a predetermined number of abnormal samples; combine the predetermined number of abnormal samples and The training data is mixed and input to neural network training, so that during the semiconductor packaging process, key process parameters are input into the trained neural network to obtain abnormal prediction values, and early warning processing is performed based on abnormal prediction values ​​and abnormal threshold values. Therefore, by generating abnormal samples and mixing them with training data for training, and determining the abnormal threshold value, early warning of production abnormalities can be realized, and product yield and production line efficiency can be improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a semiconductor packaging process early warning method and device based on sequential generative adversarial networks. Background technique [0002] Semiconductor packaging is a very important part of the semiconductor manufacturing chain, with various related equipment and complex processes. In the production process, occasional production abnormalities will cause product quality defects, reduce product yield, and cause economic losses. Reliable abnormal early warning, that is, to predict the occurrence of abnormalities in advance, it can provide sufficient response time for production line operators, provide meaningful information for decision-making of production line maintenance, reduce losses in the production process, and improve product yield, which is very important industrial application value. Due to the many steps in the production process, the ...

AI Technical Summary

Problems solved by technology

However, most of the existing data-driven anomaly prediction methods are limited to the prediction of the remaining service life of the equipment, lack of abnormal early warning research in the semiconductor manufacturing process, and even less research on the packaging process

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