DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.052
Publication date: 9th January 2023
Traditional computation based on von Neumann architecture is limited by the time and energy consumption due to data transfer between the storage and the processing units. The von Neumann architecture is also inefficient in solving unstructured, probabilistic, and real-time problems. To address these challenges, a new brain-inspired neuromorphic computational architecture is required. Due to the absence of resistance-capacitance (RC) delay, high bandwidth and low power consumption, optoelectronic artificial synaptic devices are desirable. However, stable, scalable, and complementary-metal-oxide-semiconductor (CMOS)-compatible synapses that can emulate both inhibitory and excitatory activities have not been demonstrated. This talk presents a work that will overcome this challenge by exploiting the persistence in the photoconductivity of undoped and magnesium-doped scandium nitride (ScN). The negative and positive photoconductivity in undoped and magnesium-doped ScN can be equated to the inhibitory and excitatory synaptic plasticity of the biological synapses, respectively. These artificial optoelectronic synapses can mimic primary functionalities of a biological synapse like short-term memory (STM), long-term memory (LTM), the transition from STM-to-LTM, learning and forgetting cycles, frequency-selective optical filtering, frequency-dependent potentiation and depression, Hebbian learning, and logic gate operation. This work opens up the possibility of utilising a group-III epitaxial semiconducting nitride material with inhibitory and excitatory optoelectronic synaptic functionalities for practical neuromorphic applications.
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