Inverse modeling of semiconductor manufacturing processes by neural networks

This paper presents the most important aspects of semiconductor processes modeling and a process/device optimization technique based on neural network models. Efficiency of adaptive neural networks in complex process modeling and inverse modeling is demonstrated through modeling of ion implantation process. Also, efficiency of Technology Computer-Aided Design (TCAD) system containing direct and inverse neural network models is demonstrated through optimization of complex manufacturing process flow of low voltage power VDMOSFET. Results of optimization obtained by using direct and inverse neural network models are compared with those obtained by using world-known process simulator MUSIC 2 and modified version of device simulator MINIMOS 6, and an excellent agreement is achieved. It is shown that incorporation of neural network models in TCAD systems significantly improves optimization technique, thus leading to significant reduction of computational time.