Advanced Materials · 2021 · 124 citations · 49 references
Ferroelectrics have been demonstrated as excellent building blocks for high-performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in-memory computing. Here, it is reported that the emerging van der Waals ferroelectric α-In<sub>2</sub> Se<sub>3</sub> can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance-switching ratio of heterosynaptic memristors above 10<sup>3</sup> , which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α-In<sub>2</sub> Se<sub>3</sub> channel is responsible for the resistance switching at various paired terminals. The third terminal of α-In<sub>2</sub> Se<sub>3</sub> memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read-energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α-In<sub>2</sub> Se<sub>3</sub> neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy-efficient, brain-inspired computing systems and logic-in-memory computers.
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE · 1998 · 56.5K citations · Full text
Engineering, Machine Learning, Multilayer Neural Networks +17
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Sung Hyun Jo, Ting‐Chang Chang, Idongesit E. Ebong et al. · Nano Letters · 2010 · 4.1K citations