Brain-like spatiotemporal information processing with nanosized second-order synaptic emulators; “solid-state memory visualizer” - Kumar, Mohit; Singh, Ranveer; Kang, Hyunwoo; Park, Ji Yong; Kim, Sangwan; Seo, Hyungtak (Nano Energy, 2020)
Emulating bio-counterpart artificial intelligence abilities at the hardware level requires high-density integration of artificial synapses. For that, not only nanosize artificial synapses with reliable multilevel functionality is needed but visualization of critical information processing within it is also essential. Here, we demonstrate nanosize second-order synaptic emulator and observed the dynamics of neuromorphic spatiotemporal information processing using local probe force microscopy. Particularly, the device shows stable analog hysteresis loop opening and multilevel memory storage, which was confirmed by current mapping. All versatile and necessary synaptic features like shot-, long-term memory, and corresponding dynamics, long term potentiation, and depression and pulse pair facilitation are demonstrated. Further, both Bienenstock, Cooper, and Munro learning rules e.g., frequency-dependent synaptic weight change and sliding threshold characteristics ‒ spike rate-dependent plasticity are confirmed. Based on Kelvin probe force microscopy measurements, the observed results are quantitatively explained as a dynamic of charge trapping/detrapping. Benefited from high-density integration and multilevel data storage along with processing, our approach exhibit an attractive future to build advance neuromorphic circuits at nanoscale.
Keywords: Frequency dependent; High-density integration; Kelvin probe force microscopy; Long-term potentiations; Neuromorphic circuits; Rate-dependent plasticity; Spatiotemporal information; Threshold characteristics; Information processing; Memory visualizer; Solid-state; Spatiotemporal; Synaptic emulators