Bioinspired Artificial Photonic Synapses Based on Polycrystalline β-Ga₂O₃ for Neuromorphic Applications

Recently, artificial intelligence technology that mimics the efficient information processing mechanism of the human brain has emerged as a core technology for big data processing. In particular, a neuromorphic computing chip can directly implement the human brain structure and computational process in hardware, enabling efficient low-power intelligent computation at high speeds. This enormous potential of neuromorphic computing has prompted a worldwide increase in research to realize the technology. More recently, complex biological activities and perceptions, such as memory and forgetting processes, classically conditioned learning experiments, and artificial sensory functions, have been emulated using neuromorphic artificial synaptic devices, including memristors and field-effect transistors (FETs). The biological nervous system represents associative learning through sensory systems. To comprehensively mimic brain functions, artificial synaptic devices integrating sensing and processing functions must be developed. Vision plays a vital role in organisms' reactions to the external environment, primarily because more than 80% of the information acquired through the sensory organs is obtained through vision. In addition to visible light, various animal species contain photoreceptor cells that can absorb ultraviolet (UV) light in the retina and transduce it into cellular signals. For example, bees can be trained to search for sugar water because of their UV-sensitive visual and nervous systems. In the dim Arctic twilight, reindeer use their ability to sense contrast in the UV to locate plants, including lichen and moss, and effectively evade predators (wolves and polar bears). Since humans cannot perceive UV light, the development of UV neuromorphic sensors complements our understanding of UV light and can be utilized in a variety of applications, such as chemical/biological sensors, on-chip optical communications, ultraviolet astronomy, and early warning systems. In particular, the incorporation of light into the operation of synaptic FETs can expand the bandwidth and effectively improve the interconnection problem of synaptic devices. Synaptic devices of oxide semiconductors are stimulated by deep UV signals because of their large optical bandgaps. Among them, gallium oxide (Ga₂O₃), which has an ultra-wide bandgap of 4.9 eV is particularly noteworthy. The ultra-wide bandgap provides a controllable number of large memory (storage) states. Moreover, Ga₂O₃ is suitable for optical device applications because of its almost direct bandgap properties and can also be utilized in synaptic devices that respond to optical stimuli. In this study, <i>β</i>-Ga₂O₃-based synaptic phototransistors were fabricated for the first time. Optical and electrical spikes stimulated the Sn-doped 100-nm thick polycrystalline <i>β</i>-Ga₂O₃ synaptic FETs. The device mimicked crucial synaptic functions, such as EPSCs, IPSCs, PPF, SNDP, SRDP, and STDP using two types of stimuli (optical and electrical). With light stimulation, a linear increase in synaptic weight (current) was confirmed owing to the PPC effect in the device that was fabricated with a back-gate structure. Synaptic reinforcement and inhibition were achieved by gate dielectric (SiO₂) interfacial states induced by gate voltage control. We confirmed the modulation of weights through the synergistic effect of excitatory and inhibitory synapses and mimicked the associative learning process, an expected neurological behavior. Moreover, convolutional neural network (CNN) simulation was used to perform National Institute of Standards and Technology (MNIST) handwritten digit images pattern recognition to further demonstrate the applicability of the synaptic <i>β</i>-Ga₂O₃ phototransistor in the neuromorphic field and achieved using the proposed optimized training method.