Neural Approach for Modeling and Optimizing Si-MOSFET Manufacturing

• Main Authors: Hyun-Chul Choi, Hyeok Yun, Jun-Sik Yoon, Rock-Hyun Baek
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Transistor; semiconductor manufacturing; holistic parameter optimization; machine learning; neural networks; gradient descent method
• Online Access: https://ieeexplore.ieee.org/document/9178720/

An optimal design of semiconductor device and its process uniformity are critical factors affecting desired figure-of-merits as well as reducing fabrication cost of fixing possible malfunctioning in semiconductor manufacturing. Two main tasks in optimal device design for semiconductor manufacturing, i.e., parameter optimization and modeling, have been typically used either to characterize the devices by understanding how each parameter affects the device performances or to calibrate the parameters for SPICE circuit simulation. However, there still remains limitations in describing the relationship between all manufacturing parameters and figure-of-merits using several simple equations human experts can utilize. Even with the best model currently available, the optimal design of semiconductor device heavily relies on experiences of human experts and deals with time-consuming ad-hoc trials and non-holistic approaches. In this paper, we propose a new approach for data-based accurate electrical modeling of transistor, which is the most fundamental unit device of semiconductor, and fast optimization of its manufacturing parameters. Instead of the previous analytic approaches, finding finite equations derived from semiconductor physics, we utilize machine learning technique and neural networks to find appropriate modeling functions from data pairs of parameters and figure-of-merits. And for given desired figure-of-merits, we find optimal manufacturing parameters in holistic manner by using the learned functions of neural networks and fast gradient-based optimization method. Experimental results show that our neural-network-based-model directly estimate figure-of-merits with competitive accuracy and that our holistic optimization technique accurately and rapidly adapts the manufacturing parameters to meet desired figure-of-merits.