Progressive and Stable Synaptic Plasticity with Femtojoule Energy Consumption by the Interface Engineering of a Metal/Ferroelectric/Semiconductor

In the era of big data, the cognitive system of the human brain is being mimicked through hardware implementation of highly accurate neuromorphic computing by progressive weight update in synaptic electronics. Low-energy synaptic operation requires both low reading current and short operation time to be applicable to large-scale neuromorphic computing systems. In this study, an energy-efficient synaptic device is implemented comprising a Ni/Pb(Zr0.52Ti0.48)O-3 (PZT)/0.5 wt.% Nb-doped SrTiO3 (Nb:STO) heterojunction with a low reading current of 10 nA and short operation time of 20-100 ns. Ultralow femtojoule operation below 9 fJ at a synaptic event, which is comparable to the energy required for synaptic events in the human brain (10 fJ), is achieved by adjusting the Schottky barrier between the top electrode and ferroelectric film. Moreover, progressive domain switching in ferroelectric PZT successfully induces both low nonlinearity/asymmetry and good stability of the weight update. The synaptic device developed here can facilitate the development of large-scale neuromorphic arrays for artificial neural networks with low energy consumption and high accuracy.

Automated summary

In this study, an energy-efficient synaptic device is developed for large-scale neuromorphic arrays, which mimics the cognitive system of the human brain. The device operates with low energy consumption and short operation time, comparable to synaptic events in the human brain. By adjusting the Schottky barrier and domain switching in the ferroelectric film, the weight update is achieved with low nonlinearity, asymmetry, and excellent stability.