1. Statistical Learning of IC Models for System-Level ESD Simulation.
- Author
-
Xiong, Jie, Chen, Zaichen, Raginsky, Maxim, and Rosenbaum, Elyse
- Subjects
SIMULATION Program with Integrated Circuit Emphasis ,STATISTICAL learning ,ELECTROSTATIC discharges ,RECURRENT neural networks ,FLUX pinning ,INTEGRATING circuits - Abstract
To enable accurate system-level electrostatic discharge (ESD) simulation, this article applies statistical learning to obtain I/O port models of the victim integrated circuits (ICs). A quasi-static I–V model derived using kernel regression can capture the circuit board dependency of the behavior observed at the I/O pin, regardless if there is snapback. The non-parametric kernel model can be reduced to a system-specific parametric model, which has smaller requirements for computing time and memory. In some cases, transient system-level ESD simulation may require the IC model to replicate the dynamic behavior of the nonlinear circuit. A recurrent neural network is demonstrated to be a suitable model in such cases. This article provides a detailed RNN training flow for IC pin modeling, and presents a Verilog-A implementation of the RNN for use with Simulation Program with Integrated Circuit Emphasis (SPICE)-type simulators. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF