| dc.description.abstract | The need and demand for continuous high-speed, energy-efficient hardware advancement is undisputed. Traditional computing system with von Neumann architecture leads to high energy consumption and latency due to a huge amount of data transfer between the separated memory unit and the logic unit. In response to this discrepancy, extensive research has been conducted to develop brain-inspired electronic devices that can provide alternate computing platforms needed for implementing hardware neural networks. Artificial synapse, which emulates the dynamics of biological synapses, such as “update” and “memorize,” is one approach toward solid-state implementation of bio-inspired devices. Recently, two-dimensional (2D) van der Waals (vdW) materials have been actively explored for such artificial synapses. The distinctive electronic, optoelectronic, and mechanical properties of two-dimensional (2D) materials make these quite attractive for a wide variety of applications. This thesis explores the electronic and optoelectronic properties of 2D materials for mimicking the synaptic performance of the neuron. Materials of interest include MoS2, which is semiconducting, and α-In2Se3, a ferroelectric semiconducting material, investigated as active elements for synaptic applications. In the first part of the dissertation, we try to understand the working mechanism, i.e., charge trapping and de-trapping in synaptic devices using MoS2 as the channel material in a simple back-gated configuration. To this end, we have used a high-k dielectric (Ta2O5) as the gate oxide, which is expected to reduce the voltage swing and hence the power consumption, which is beneficial when used in neuromorphic networks. The hysteresis in the transfer characteristics
of the transistor arising out of the Ta2O5/MoS2 interface and interface trap charges within the oxide are exploited to demonstrate excitatory Post Synaptic current (EPSC) / Inhibitory Post Synaptic current (IPSC), Long Term Potentiation (LTP) / Long Term Depression (LTD), Spike Amplitude Dependent Plasticity (SADP), Spike Timing Dependent Plasticity (STDP) at a relatively lower energy budget. In the second part, we discuss the working mechanism of 2D ferroelectric semiconducting channel material (α-In2Se3) for synaptic applications. Ferroelectric materials have emerged as a promising candidate for enabling synaptic devices as they lead to fast operation, non-destructive readout, low-power, low variations, and high on/off ratios. The partial polarization switching behavior of the ferroelectric material can be exploited to emulate the biological synaptic functions by gradually modulating the channel conductance through an external electrical field. We also explored the continuous weight modulation through partial polarization of the channel displaying an excellent linear weight update trajectory with multiple stable conductance states. In the next part of the dissertation, we discuss artificial neural networks for pattern recognition using the conductance weights obtained from device-level emulation of synaptic dynamics. By updating the synaptic weights with conductance weight values on 18,000 digits, we achieved a successful recognition rate of 93% on the testing data. The introduction of 0.10 variance of noise pixels results in an accuracy of more than 70%, showing the strong fault-tolerant nature of the conductance states. These synaptic functionalities, learning rules, and device-to-subsystem-level simulation results based on α-In2Se3 could facilitate the development of more complex neuromorphic hardware systems based on FeS-FETs.
In the last part of the dissertation, we introduce a light-sensing function merged into the artificial synapses to realize an optoelectronic synapse. The optical input signal (λ = 527 nm) is used as a presynaptic signal with various frequencies and strengths to imitate the synaptic functionalities such as short-term memory (STM) and long-term memory (LTM), paired-pulse facilitation (PPF), spike rate-dependent plasticity (SRDP) spike duration-dependent plasticity (SDDP) and memory functions like learning, forgetting, and relearning | en_US |
