  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.
