Symbolic Regression Based on Kolmogorov–Arnold Networks for Gray-Box Simulation Program with Integrated Circuit Emphasis Model of Generic Transistors

In this paper, a novel approach to symbolic regression using Kolmogorov–Arnold Networks (KAN) for developing gray-box Simulation Program with Integrated Circuit Emphasis models of generic transistors is proposed. Unlike traditional black-box models, such as artificial neural networks, (ANN), the dev...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) Vol. 14; no. 6; p. 1161
Main Authors: Huang, Yiming, Li, Bin, Wu, Zhaohui, Liu, Wenchao
Format: Journal Article
Language:English
Published: Basel MDPI AG 16.03.2025
Subjects:
Accuracy
Artificial neural networks
Current voltage characteristics
Datasets
Design optimization
Efficiency
Formulas (mathematics)
Integrated circuits
Modelling
Neural networks
Product development
Semiconductor chips
Simulation
Simulation methods
Transistors
ISSN:2079-9292, 2079-9292
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a novel approach to symbolic regression using Kolmogorov–Arnold Networks (KAN) for developing gray-box Simulation Program with Integrated Circuit Emphasis models of generic transistors is proposed. Unlike traditional black-box models, such as artificial neural networks, (ANN), the developed KAN-based model enhances interpretability by generating explicit mathematical expressions while maintaining high accuracy in device modeling. By combining the computational efficiency of neural network approaches with the transparency of formula-based modeling, the SPICE model generation is significantly accelerated, thereby improving the efficiency of the design technology co-optimization (DTCO) process. The experimental results demonstrate that the expressions derived from the KAN model accurately represent the current–voltage (I–V) characteristics of the BSIM–CMG compact model and provide nearly symmetric results. To further validate the effectiveness and versatility of the approach, we embedded the trained I–V KAN model into a 12 nm FinFET SPICE model and performed 11-stage ring oscillator (RO) simulations. The results indicate that the KAN-based SPICE model achieves accuracy comparable to the original 12 nm FinFET SPICE model, demonstrating its potential to streamline device modeling for advanced technology nodes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14061161