| Autor: |
Jinyoung Choi, Hyunjoon Jeong, Sangmin Woo, Hyungmin Cho, Yohan Kim, Jeong-Taek Kong, Soyoung Kim |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of the Electron Devices Society, Vol 12, Pp 65-73 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2168-6734 |
| DOI: |
10.1109/JEDS.2023.3346380 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
