Improvement of FTJ on-current by work function engineering for massive parallel neuromorphic computing

TL;DR

This paper explores work function engineering in HfO2-based ferroelectric tunnel junctions to enhance their on-current, aiming to improve their suitability for massive parallel neuromorphic computing circuits.

Contribution

It demonstrates how electrode work function engineering can optimize FTJ device performance, focusing on increasing on-current for neuromorphic applications.

Findings

Work function engineering improves FTJ on-current.

Absolute current difference (Ion - Ioff) is a key performance metric.

Potential for optimized FTJ devices in neuromorphic circuits.

Abstract

HfO2-based ferroelectric tunnel junctions (FTJs) exhibit attractive properties for adoption in neuromorphic applications. The combination of ultra-low-power multi-level switching capability together with the low on-current density suggests the application in circuits for massive parallel computation. In this work, we discuss one example circuit of a differential synaptic cell featuring multiple parallel connected FTJ devices. Moreover, from the circuit requirements we deduce that the absolute difference in currents (Ion - Ioff) is a more critical figure of merit than the tunneling electroresistance ratio (TER). Based on this, we discuss the potential of FTJ device optimization by means of electrode work function engineering in bilayer HZO/Al2O3 FTJs.