Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)
19 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1403-1411
Number of pages9
JournalNature materials
Issue number12
Early online date21 Nov 2022
Publication statusPublished - Dec 2022


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