With the exponential growth of global data processing demands, the concurrent slowdown in processing speed has become apparent. As a remedy, research endeavors in neuromorphic systems, emulating the structural and functional dynamics of the human brain, are actively underway. Neuromorphic systems research is classified into two distinct domains, wherein synapse investigation and neuron exploration are pursued independently. As with our previous studies[1], synaptic studies have witnessed advancements across diverse realms including materials, structures, and properties[2], but neuronal investigations have lagged considerably behind synaptic endeavors. This disparity primarily arises from the formidable challenges associated with replicating the intricate characteristics inherent to human neurons. To date, the majority of scrutinized neuron components have embraced a two-terminal configuration, renowned for its structural simplicity. Nevertheless, it has been beset by technical challenges on repeatability and reliability[3, 4]. The initial research stage on three-terminal neuron devices focused on Si-based transistors, which possess a complex structure since silicon itself cannot perform the neuron function, resulting in significantly increased power consumption, disadvantageous in light of the semiconductor trend toward miniaturization.[5]. In contrast to prior investigations, this study endeavors to employ titanium dioxide (TiO2), an oxide semiconductor, as the channel material, and seeks to realize integrate-and-fire (IF) characteristics utilizing a simple 1-transistor (1T) structure with a dual gate dielectric, one of which induces abrupt changes in conductivity in the transfer curve by supplying charges and capturing them at the interface of channel layer and dielectrics. Utilizing a 1T configuration obviates the need for intricate circuitry, thereby yielding reduced power consumption. Furthermore, the most significant feature of this study is the successful implementation of IF characteristics induced solely through light stimulation without electrical stimuli, unprecedented in previous reports. While conventional neuron components primarily exhibit firing characteristics induced by electrical stimuli, with certain investigations extending to modulation through the addition of light[6], our research uniquely simulates the emergence of IF properties exclusively via optical stimulation, devoid of any voltage input. The feasibility of such photo-induced firing is attributed to the outstanding photoresponsivity of TiO2 material and its capacity to control the lifetime of the device under 365 nm wavelength light stimulation. Hence, our study represents a significant advancement in the research phase devoted to mimicking human neuronal characteristics. It holds promise for emulating a broader spectrum of human brain activities, particularly regarding low-power consumption. As a result, we anticipate an enhanced capacity to replicate sensory perceptions, including visual stimuli. Acknowledgments This work was supported by an Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [24ZB1330] and UST Young Scientist+ Research Program 2023 through the University of Science and Technology [23AB2710]. References Lee, J. and J.W. Lim, Replicating the Bright-Light Therapy of Seasonal Affective Disorder Using Dual-Gate Dielectric Synaptic Transistors: An Exploration of Neuromorphic Computing Approaches. ACS Applied Electronic Materials, 2024. 6(3): p. 1904-1911. Dai, S., et al., Recent Advances in Transistor ‐Based Artificial Synapses. Advanced Functional Materials, 2019. 29(42). Zhang, X., et al., An Artificial Neuron Based on a Threshold Switching Memristor. IEEE Electron Device Letters, 2018. 39(2): p. 308-311. Park, S.O., et al., Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing. Nat Commun, 2022. 13(1): p. 2888. Han, J.K., et al., A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing. Advanced Functional Materials, 2022. 32(33). Han, J.K., et al., Bioinspired Photoresponsive Single Transistor Neuron for a Neuromorphic Visual System. Nano Lett, 2020. 20(12): p. 8781-8788. Figure 1
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