Fast and Expandable ANN-Based Compact Model and Parameter Extraction for Emerging Transistors

Jeong, HyunJoon; Woo, SangMin; Choi, Jinyoung; Cho, HyungMin; Kim, Yohan; Kong, Jeong-Taek; Joo, Sanghoon

doi:10.1109/jeds.2023.3246477

Cited by 10 publications

(1 citation statement)

References 29 publications

(36 reference statements)

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“…These new challenges increase the difficulty of modeling new emerging devices for three reasons: (a) the traditional standard FET models cannot well-capture the electrical characteristics of emerging devices, (b) developing the physics-based model equation requires a long time and expertise, and (c) for equation-based models, it is still challenging to fully automate the model parameter extraction process while achieving a very high fitting accuracy [ 5 ]. In the previous studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], Neural Networks (NN) show promising accuracy in emerging device modeling. However, NN-based device modeling suffers from two main issues: unphysical behaviors and needing NN expertise [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Physics-Informed Automatic Neural Network Generation Framework for Emerging Device Modeling

Guangxin

You

et al. 2023

Micromachines

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With the rapid development of semiconductor technology, traditional equation-based modeling faces challenges in accuracy and development time. To overcome these limitations, neural network (NN)-based modeling methods have been proposed. However, the NN-based compact model encounters two major issues. Firstly, it exhibits unphysical behaviors such as un-smoothness and non-monotonicity, which hinder its practical use. Secondly, finding an appropriate NN structure with high accuracy requires expertise and is time-consuming. In this paper, we propose an Automatic Physical-Informed Neural Network (AutoPINN) generation framework to solve these challenges. The framework consists of two parts: the Physics-Informed Neural Network (PINN) and the two-step Automatic Neural Network (AutoNN). The PINN is introduced to resolve unphysical issues by incorporating physical information. The AutoNN assists the PINN in automatically determining an optimal structure without human involvement. We evaluate the proposed AutoPINN framework on the gate-all-around transistor device. The results demonstrate that AutoPINN achieves an error of less than 0.05%. The generalization of our NN is promising, as validated by the test error and the loss landscape. The results demonstrate smoothness in high-order derivatives, and the monotonicity can be well-preserved. We believe that this work has the potential to accelerate the development and simulation process of emerging devices.

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