Artificial visual systems enabled by quasi–two-dimensional electron gases in oxide superlattice nanowires

Abstract

Rapid development of artificial intelligence techniques ignites the emerging demand on accurate perception and understanding of optical signals from external environments via brain-like visual systems. Here, enabled by quasi–two-dimensional electron gases (quasi-2DEGs) in InGaO3(ZnO)3 superlattice nanowires (NWs), an artificial visual system was built to mimic the human ones. This system is based on an unreported device concept combining coexistence of oxygen adsorption-desorption kinetics on NW surface and strong carrier quantum-confinement effects in superlattice core, to resemble the biological Ca2+ ion flux and neurotransmitter release dynamics. Given outstanding mobility and sensitivity of superlattice NWs, an ultralow energy consumption down to subfemtojoule per synaptic event is realized in quasi-2DEG synapses, which rivals that of biological synapses and now available synapse-inspired electronics. A flexible quasi-2DEG artificial visual system is demonstrated to simultaneously perform high-performance light detection, brain-like information processing, nonvolatile charge retention, in situ multibit-level memory, orientation selectivity, and image memorizing.

Quantum Artificial Synapses

“…[50] In these cases, the interaction of simultaneous light pulses with adsorbed and bulk oxygen in the surface depletion region play a key role in regulating the NW conductance, that is, the synaptic weight. [9,51] In a recent work, both volatile and non-volatile resistive switching have been achieved in Ag-contacted ZnO nanowire devices, mainly enabled by the atomic Ag diffusion along the NW (Figure 2f,g), which well emulate the ion migration dynamics in biological synapses. [35] With detailed investigation of the memristive mechanism in the Ag-ZnO system, the underlying electrochemical fundamentals of filament formation/dissolution highlight the Ag + /Ag redox reactions and transport characteristics on the crystalline NW surface.…”

“…[90] Given high carrier mobility together with high sensitivity to various stimuli, quasi-2DEGs systems may enable more accurate probing of complex biological dynamics than that can be achieved by other material systems. [9,91] Lately, Dror Miron et al demonstrated a non-volatile resistive switching device based on quasi-2DEGs established between the SrTiO 3 substrate and the amorphous Al 2 O 3 layer. [88] The oxygen vacancies from the SrTiO 3 /Al 2 O 3 conductive interface could drift into (out of) the Al 2 O 3 layer under negative (positive) bias, leading to the formation (breakage) of the conductive filament.…”

“…Reproduced with permission. [9] Copyright 2020, The Authors, some rights reserved; exclusive licensee AAAS. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).…”

“…Most recently, Meng et al demonstrated the quasi-2DEGs photonic synapses based on InGaO 3 (ZnO) 3 superlattice NWs (Figure 4a), by utilizing an unreported device operation concept, which combines the oxygen adsorption-desorption kinetics on NW surface and the strong quantum-confinement effects of carriers in superlattice cores (Figure 4b). [9] These devices duplicate the Ca 2+ ion flux and neurotransmitter release characteristics in biological synapses. Importantly, these quasi-2DEGs photonic synapses can precisely define their conductance state (or synaptic weight) for emulating synaptic behaviors (Figure 4c-e), which shows the bionic performance that is unreachable by other conventional materials.…”

“…[7,8] Recently, lowdimensional quantum materials have emerged as leading candidate materials for the neuromorphic computing and bioelectric interface. [9][10][11] In contrast to the conventional materials that have been utilized in many neuromorphic devices and systems, low-dimensional quantum materials exhibit novel device functionality, better mimicking the biological neurons. [12] The rich structural and compositional manipulation of quantum materials, coupled with defect engineering and interfacial chemistry, offers significant opportunities to realize ultralow-power artificial neural networks.…”