TY - JOUR
T1 - Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour
AU - Wang, Yulong
AU - Zhang, Qian
AU - Astier, Hippolyte P.A.G.
AU - Nickle, Cameron
AU - Soni, Saurabh
AU - Alami, Fuad A.
AU - Borrini, Alessandro
AU - Zhang, Ziyu
AU - Honnigfort, Christian
AU - Braunschweig, Björn
AU - Leoncini, Andrea
AU - Qi, Dong Cheng
AU - Han, Yingmei
AU - del Barco, Enrique
AU - Thompson, Damien
AU - Nijhuis, Christian A.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85142371722&partnerID=8YFLogxK
U2 - 10.1038/s41563-022-01402-2
DO - 10.1038/s41563-022-01402-2
M3 - Article
AN - SCOPUS:85142371722
SN - 1476-1122
VL - 21
SP - 1403
EP - 1411
JO - Nature Materials
JF - Nature Materials
IS - 12
ER -