Journal Article

Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour

Nijhuis, Christian A., Zhang, Qian, Honnigfort, Christian, Astier, Hippolyte P. A. G., del Barco, Enrique, Soni, Saurabh, Wang, Yulong, Han, Yingmei, Nickle, Cameron, Qi, Dong-Cheng, Borrini, Alessandro, Alami, Fuad A., Zhang, Ziyu, Braunschweig, Björn, Leoncini, Andrea, Thompson, Damien

2022

Overview

To realize molecular-scale electrical operations beyond the von Neumann bottleneck, new types of multifunctional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here, we report a molecule that switches from high to low conductance states with massive negative memristive behaviour that depends on the drive speed and number of past switching events, with all the measurements fully modelled using atomistic and analytical models. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning, all within a 2.4-nm-thick layer that is three orders of magnitude thinner than a neuronal synapse. The dynamic molecular switch provides all the fundamental logic gates necessary for deep learning because of its time-domain and voltage-dependent plasticity. The synapse-mimicking multifunctional dynamic molecular switch represents an adaptable molecular-scale hardware operable in solid-state devices, and opens a pathway to simplify dynamic complex electrical operations encoded within a single ultracompact component. To realize electronic operations beyond the von Neumann bottleneck, a new type of switch that can mimic self-learning is needed. Here, the authors demonstrate all-in-one-place logic and memory operations based on dynamic molecular switch that can emulate brain-like synaptic and Pavlovian response, bringing the field a step closer to molecular-scale hardware.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 140/131 / 140/146 / 639/925/927/339 / 639/925/927/998 / Bias / Biomaterials / Chemistry / Chemistry and Materials Science / Condensed Matter Physics / Deep learning / Electricity / Energy / Hardware / Logic circuits / Materials Science / Mathematical models / Molecular machines / Nanotechnology / Optical and Electronic Materials / Physics / Solid state devices / Switches / Synapses