Predicting The Yield Of Integrated Circuits By Tism With Neutral Network

The importance of integrated circuits has again attracted attention recently due to the trade friction between US and China,and between Japan and Korea recent year.In recent years,for example,ZTE has been unable to cope with US sanctions.Huawei has also been banned from importing US semiconductor products on the grounds of endangering national security since May 26,2020.As the integrated circuits processing performance and technology level of integrated circuits become increasingly polarized,the importance of IC output is becoming more prominent.The field of integrated circuits has gradually become a pawn in the game between great powers.One of the most important indicators of IC output is chip yield.The yield determines the manufacturing cost and process limits of the chip.In the past,many studies on the IC chip yield have remained on specific processes and specific types of integrated circuits.For example,some studies start with photolithography in the first step of IC processing,while others start with encapsulation in the last step of IC processing.This research adopts method called deep learning networks to explain the entire process of integrated circuit processing and further to calculate and predict the IC chip yield innovatively.We use Totally Interpretive Structural Modeling and two neural networks with strong explainability to combine the method we called TISM+DLNN.And we get good result by using the data we collected.More importantly,we propose a highly explainable method based on neural network and TISM for solving multi-factor complex problems.It provides ideas for dealing with such problems in the future.