For sustainable advancements in electronics technology, the field of neuromorphic electronics, i.e., electronics that imitate the principle behind biological synapses with a high degree of parallelism, has recently emerged as a promising candidate for novel computing technologies. Toward realizing a massively parallel neuromorphic system, it is essentially required to develop an artificial synapse capable of emulating various synaptic functionality, such as short- and long-term synaptic plasticity with ultralow power consumption and robust controllability. In this talk, as a first part, I will review the general introduction of neuromorphic hardware technology based on the research background and brain-inspired synaptic device requirements for high-performance and low-power artificial neural networks, followed by recent results in this field. As a second part, I will briefly introduce our recent approaches and achievements for artificial synapses/neurons, diagonal neural network architectures (DCNN), and probabilistic/reservoir computing using diverse functional nanomaterials (metal (or Si)-oxide and ferroelectric materials) on advanced device architectures [1-5]. Finally, I will present our recent study for finger-writing motion recognition in a three-dimensional free-space [6].

References
[1] S. Choi et al., Adv. Mater. 20044659 (2020), Adv. Mater. 34, 2104598 (2022). [2] S. Choi et al., Nano Energy, 84, 105947 (2021), Adv. Sci, 2104773 (2022). [3] J. Jang et al., Adv. Sci. 2201117 (2022).
[4] S. Ham et al., Science Adv. 6 : eaba1178 (2020), Nano Energy, 124, 109435 (2024), [5] S. Choi et al., Nature Comm. 15, 2044 (2024). [6] H. Cho et al., Nature Electronics. 6, 619-629 (2023)