Demonstration of intrinsic STDP learning capability in all-2D multi-state MoS <sub>2</sub> memory and its application in modelling neuromorphic speech recognition
Tathagata Paul, Akshaya A Mukundan, Krishna Kanhaiya Tiwari, Arindam Ghosh, Chetan Singh Thakur
Abstract
Abstract The human brain can be characterized by its large number of adaptive synapses, connecting billions of neurons capable of both learning and perceiving the environment. Neuromorphic computing, based on brain-inspired principles, is a promising technology, to build low-power, distributed, fault-tolerant intelligent systems mainly for perception tasks. Here, we demonstrate the intrinsic capability of floating gate (FG) MoS 2 device (MoS 2 FG-FET) to model the spike time dependent plasticity (STDP) learning rule that is based on the transient response of the MoS 2 channel to spikes applied to the source and gate leads. We implemented the STDP learning protocol in a neuromorphic speech recognition system (NSRS), inspired by the human auditory pathway, for various auditory recognition tasks. Our proposed NSRS consists of a cochlea model, an unsupervised feature learning stage, and a simple linear classifier. The unsupervised learning stage uses the biologically plausible STDP learning in novel two-dimensional MoS 2 FG-FET memory which circumvents the requirement of any other learning circuitry. Demonstration of STDP modelling in two-dimensional (2D) MoS 2 is an important step towards incorporating 2D architectures for reduced device footprints in neuromorphic learning circuits.