New Publication "Synaptic Plasticity in Semiconducting Single‐Walled Carbon Nanotubes Transistors"

Recently, a significant amount of attention went toward the development of artificial neural networks, as these promise efficient computation for specific tasks that are expensive or even impossible for von Neumann architecture‐based computers. A considerable amount of research is conducted to develop materials in which layered neural networks are easily implemented. Herein, the use of high‐quality semiconducting single‐walled carbon nanotube (s‐SWCNT) inks to fabricate artificial synapses using simple bottom‐gate field‐effect transistors (FETs) is reported. The synapse utilizes the otherwise often dreaded hysteresis, the characteristic of the device structure, and the materials used therein. This hysteresis spans several orders of magnitude. The SWCNT synaptic transistor exhibits a clear spike‐time‐dependent plasticity (STDP), indicating the ability to achieve learning, following a mechanism similar to biological systems: asymmetric anti‐Hebbian learning. The very well‐defined STDP shape, together with the use of a simple pulse shape to obtain it, has not been reported previously for synaptic transistors. This represents a crucial step for further development of actual hardware implementation of artificial synapses in a more extensive, layered network. Interestingly, the time response of the device is very similar to the one of biological synapse, opening opportunities unavailable to CMOS emulations.

Reference: Talsma, W., van Loo, H., Shao, S., Jung, S., Allard, S., Scherf, U. and Loi, M.A. (2020), Synaptic Plasticity in Semiconducting Single‐Walled Carbon Nanotubes Transistors. Adv. Intell. Syst.. doi:10.1002/aisy.202000154