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

• URL: http://arxiv.org/abs/2110.10724v2
• Date: Fri, 22 Oct 2021 06:06:22 GMT
• Title: Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT
• Authors: Shivanshu Mishra, Bipin Gaikwad and Nidhi Chaturvedi
• Abstract summary: 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. 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) 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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