CMOS-PCM Based Artificial Thermosensory Neuron for Bio-Inspired Sensing

We propose a hybrid CMOS-PCM (Phase-Change Memory) based artificial thermosensory neuron with integrated synaptic module for neuromorphic sensing applications. We exploit the volatile Ovonic-Threshold-Switching (OTS) behaviour of a PCM cell for emulating the spiking neuronal activity. Thermally driven OTS voltage reduction trend of a PCM cell helps to modulate firing frequency of the proposed sensory neuron with increasing temperature. The proposed neuron circuit is capable of sensing a temperature range from 273K to 353 K. Furthermore, we propose a Spiking Neural Network (SNN) of size 784×196 × 10 for MNIST, Fashion-MNIST data based image classification, where gray-scale pixel values are considered as temperature profiles. The proposed SNN was trained with Back-Propagation (BP) algorithm for 100 epochs. We achieved maximum 99.21 % (/95.5%) train (/test) accuracy on MNIST dataset, whereas on Fashion-MNIST dataset, maximum train (/test) accuracy was 92.18% (/83.24%). Through network simulations, we also investigated the impact of resistance drift behavior of PCM synaptic devices on the proposed SNN application.