학술논문
Enhancement and Expansion of the Neural Network-Based Compact Model Using a Binning Method
Document Type
Periodical
Source
IEEE Journal of the Electron Devices Society IEEE J. Electron Devices Soc. Electron Devices Society, IEEE Journal of the. 12:65-73 2024
Subject
Language
ISSN
2168-6734
Abstract
The artificial neural network (ANN)-based compact model has significant advantages over physics-based standard compact models such as BSIM-CMG because it can achieve higher accuracy over a wide range of geometric parameters. This makes it particularly suitable for design space exploration and optimization. However, the ANN-based compact model using only one set of model parameters (global-ANN) requires larger model sizes to achieve wider coverage and higher accuracy in order to capture the unpredictable nonlinearities of emerging devices. This results in reduced simulation speed and a trade-off between simulation accuracy, model coverage, and simulation speed makes it difficult to utilize ANN-based compact models in a variety of ways. To solve this problem, we propose the first ANN-based compact modeling flow using a binning method (binning-ANN) and we address the training requirements and data sparsity issues that may occur due to the binning method in ANNs. In addition, we develop a bin size optimization guideline for the binning-ANN. As a result, the binning-ANN not only has higher accuracy, but also much better expandability than existing methods.