As one of the most important members of the two dimensional chalcogenide family, molybdenum disulphide (MoS2) has played a fundamental role in the advancement of low dimensional electronic, optoelectronic and piezoelectric designs. Here, we demonstrate a new approach to solid state synaptic transistors using two dimensional MoS2 floating gate memories. By using an extended floating gate architecture which allows the device to be operated at near-ideal subthreshold swing of 77 mV/decade over four decades of drain current, we have realised a charge tunneling based synaptic memory with performance comparable to the state of the art in neuromorphic designs. The device successfully demonstrates various features of a biological synapse, including pulsed potentiation and relaxation of channel conductance, as well as spike time dependent plasticity (STDP). Our device returns excellent energy efficiency figures and provides a robust platform based on ultrathin two dimensional nanosheets for future neuromorphic applications.Understanding the complexities in the functioning of the human brain has been one of the foremost challenges in the field of neuroscience. Among the several proposed models, only a few can explain the operation of a human brain and that too for a very limited set of functionalities [1][2][3] . From an electronic point of view, the computational architecture of a brain is vastly different from that of a traditional von Neumann architecture based system [4,5] . This has led to the emergence of neuromorphic computation schemes [6][7][8][9][10] . Current computation follows an architecture where processing and storage of data is handled by separate entities whereas in neuromorphic computation, processing and storage of data is handled by a single element which acts as the electrical analogue of a synapse. Mimicing the functionality and density of synapses in the brain would lead to a massive reduction in energy consumption and immensely enhance computational capabilities like parallel processing. Given the high density of synapses required, traditional silicon based devices which are plagued by power dissipation and short channel effects are rendered unsuitable for scalable neuromorphic applications [11,12] . This makes ultrathin two dimensional materials a perfect candidate for the active element of a synaptic transistor given their immunity to short channel effects and excellent gate coupling at nanometer length scales [12,13] .Biologically, a synapse functions by changing its conductivity based on the sequence of synaptic pulses it receives. This is accomplished by varying the concentration of neurotransmitters or chemical stimulants which control the conductivity of the junction between two neurons [14] . An ideal synaptic transistor must possess the ‡ e-mail:tathagata@iisc.ac.in, arindam@iisc.ac.in twin qualities of being a non-volatile memory while inculcating a learning based mechanism to deduce its conductance from the history of applied inputs [15][16][17][18][19][20][21][22][23][24][25][26][27][28...
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) MoS2 device (MoS2 FG-FET) to model the spike time dependent plasticity (STDP) learning rule that is based on the transient response of the MoS2 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 MoS2 FG-FET memory which circumvents the requirement of any other learning circuitry. Demonstration of STDP modelling in two-dimensional (2D) MoS2 is an important step towards incorporating 2D architectures for reduced device footprints in neuromorphic learning circuits.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.