Molecular-Scale Hardware that Mimic Synapses

Department of Molecules and Materials, MESA+ Institute for Nanotechnology, Molecules Center and Center for Brain-Inspired Nano Systems (BRAINS), Faculty of Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.

Inspired by the energy efficiency of brains and the ever-increasing demand for miniaturised electronics, there is a drive to develop devices that mimic the dynamic character of neurons and synapses to realize, for instance, non-von Neumann neuromorphic computing. Mostly, such operations are realized with energy inefficient and complex silicon-based circuits or with mesoscale memristors, but molecular hardware for doing so is not available yet [1]. In this context, molecular switches are an interesting approach, but so far molecular switches lack the dynamical, time-dependent character inherent to synapses [2,3]. It has been notoriously difficult, however, to reversibly address molecular switches in solid-state tunnel junctions. After a brief introduction, I will discuss our recent efforts to develop multi-functional molecular devices[3,4,5]. Recently, we developed a new type of an electrically driven molecular switch that can toggle between two different functionalities.[5] By coupling fast electron transport to slow proton addition steps, we created dynamic molecular switches that show large hysteretic negative differential conductance [6]. These switches mimic basic spike-rate dependent plasticity, Pavlovian learning, and emulate all Boolean logic gates. These molecular switches are promising to develop spiking neural networks and open new ways to design molecular-electronic devices.

1)    Christensen et al. Neuromorph. Comput. Eng. 2022, 2, 022501.
2)    Gehring, P., Thijssen, J. M., van der Zant, H. S. J. Nat. Rev. Phys. 2019, 1, 381-396.
3)    Thompson, D.; Barco, E. d.; Nijhuis, C. A. Appl. Phys. Lett. 2020, 117, 030502.
4)    Chen, X.; Roemer, M.; Yuan, L.; Du, W.; Thompson, D.; del Barco, E.; Nijhuis, C. A. Nat. Nanotechnol. 2017, 12, 797–803.
5)    Han, Y.; Nickle, C.; Zhang, Z.; Asstier, P. A. G.; Duffin, T. J.; Qi, D.; Wang, Z.; del Barco, E.; Thompson D.; Nijhuis, C. A. Nat. Mater. 2020, 19, 843-848.
6)    Wang, Y.; Zhang, Q.; Nickle, C.; Venkatakrishnarao, D.; Zhang, Z.; Leoncini A.; Qi, D.-C.; Han, Y.; del Barco, E.; Thompson, D.; Nijhuis, C. A. Nat. Mater. 2022, Accepted.