A Review on Machine Learning Approaches for Predicting the Effect of Device Parameters on Performance of Nanoscale MOSFETs

This review investigates the possibility of using Machine Learning as a replacement for numerical TCAD device simulation. As the chip design is getting complex to incorporate more and more functionality in the devices, many chipmakers started exploring advanced techniques of machine learning to get rid of some big challenges faced by IC industry. In Machine learning, advanced algorithms are utilized to identify patterns in data and to predict about the required information. Machine Learning finds its application in semiconductor fabrication as well as parameter extraction in device modeling. It is also used in prediction of device reliability and its analysis. This work proposes to utilize machine learning method to establish mapping between the performance parameters and structural parameters of the nanoscale MOSFETs. Methods using Machine Learning are fast, highly efficient and computing resource saving over traditional methods.