Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT

TL;DR

This paper introduces a semi-supervised physics-guided deep learning framework that efficiently predicts I-V characteristics of GaN HEMTs, reducing data needs and ensuring physical consistency.

Contribution

The novel semi-supervised physics-guided neural network (SPGNN) reduces training data requirements by over 80% and incorporates physical laws into deep learning for device modeling.

Findings

SPGNN achieves less than 1% error on 32.4% of unseen data.

SPGNN requires 80% less data than traditional neural networks.

SPGNN maintains physical consistency in predictions.

Abstract

This letter proposes a novel deep learning framework (DLF) that addresses two major hurdles in the adoption of deep learning techniques for solving physics-based problems: 1) requirement of the large dataset for training the DL model, 2) consistency of the DL model with the physics of the phenomenon. The framework is generic in nature and can be applied to model a phenomenon from other fields of research too as long as its behaviour is known. To demonstrate the technique, a semi-supervised physics guided neural network (SPGNN) has been developed that predicts I-V characteristics of a gallium nitride-based high electron mobility transistor (GaN HEMT). A two-stage training method is proposed, where in the first stage, the DL model is trained via the unsupervised learning method using the I-V equations of a field-effect transistor as a loss function of the model that incorporates physical behaviors in the DL model and in the second stage, the DL model has been fine-tuned with a very small set of experimental data. The SPGNN significantly reduces the requirement of the training data by more than 80% for achieving similar or better performance than a traditional neural network (TNN) even for unseen conditions. The SPGNN predicts 32.4% of the unseen test data with less than 1% of error and only 0.4% of the unseen test data with more than 10% of error.