Jiaxin Chen, Ting Xu, Xinyi Zhang, Bo Li, Lei Wang, Jianhui Bu
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引用次数: 0
Abstract
It's hard to accurately consider various operating factors for the traditional single event transient (SET) SPICE modeling. This paper proposes a novel method based on a neural network. The proposed method can unify the intricate data correlations among drain voltage, linear energy transfer (LET), temperature, strike position, time, and drain transient current in a single model with high accuracy. Technology computer aided design (TCAD) simulation is used to get the original SET data for training. The genetic algorithm (GA) optimized back propagation (BP) neural network established herein has a root mean square error (RMSE) of less than 2.0042%. This optimized neural network is converted to the SET current SPICE model through the Verilog-A language, and its practicality has been verified through circuit simulation of a two-input NAND gate.
期刊介绍:
Prediction through modelling forms the basis of engineering design. The computational power at the fingertips of the professional engineer is increasing enormously and techniques for computer simulation are changing rapidly. Engineers need models which relate to their design area and which are adaptable to new design concepts. They also need efficient and friendly ways of presenting, viewing and transmitting the data associated with their models.
The International Journal of Numerical Modelling: Electronic Networks, Devices and Fields provides a communication vehicle for numerical modelling methods and data preparation methods associated with electrical and electronic circuits and fields. It concentrates on numerical modelling rather than abstract numerical mathematics.
Contributions on numerical modelling will cover the entire subject of electrical and electronic engineering. They will range from electrical distribution networks to integrated circuits on VLSI design, and from static electric and magnetic fields through microwaves to optical design. They will also include the use of electrical networks as a modelling medium.