In Situ Observation of Low‐Power Nano‐Synaptic Response in Graphene Oxide Using Conductive Atomic Force Microscopy. Issue 26 (3rd June 2021) - Hui, Fei; Liu, Peisong; Hodge, Stephen A.; Carey, Tian; Wen, Chao; Torrisi, Felice; Galhena, D. Thanuja L.; Tomarchio, Flavia; Lin, Yue; Moreno, Enrique; Roldan, Juan B.; Koren, Elad; et al.
Abstract: Multiple studies have reported the observation of electro‐synaptic response in different metal/insulator/metal devices. However, most of them analyzed large (> 1 µ m 2 ) devices that do not meet the integration density required by industry (10 10 devices/mm 2 ). Some studies emploied a scanning tunneling microscope (STM) to explore nano‐synaptic response in different materials, but in this setup there is a nanogap between the insulator and one of the metallic electrodes (i.e., the STM tip), not present in real devices. Here, it is demonstrated how to use conductive atomic force microscopy to explore the presence and quality of nano‐synaptic response in confined areas < 50 nm 2 . Graphene oxide (GO) is selected due to its easy fabrication. Metal/GO/metal nano‐synapses exhibit potentiation and paired pulse facilitation with low write current levels < 1 µ A (i.e., power consumption ≈ 3 µ W), controllable excitatory post‐synaptic currents, and long‐term potentiation and depression. The results provide a new method to explore nano‐synaptic plasticity at the nanoscale, and point to GO as an important candidate for the fabrication of ultrasmall (< 50 nm 2 ) electronic synapses fulfilling the integration density requirements of neuromorphic systems. Abstract : Graphene oxide (GO) can be used as resistive switching medium to fabricate ultrasmall (area <50 nm 2 ) memristive electronic synapses. Here, a conductive atomic force microscope based electrical system is used to prove multiple nano‐synaptic behaviors. It is observed that GO can be used to fabricate ultrasmall and ultralow power electronic synapses, which comply with the integration density requirements of industrial artificial neural networks.