Work

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

Year: 2022

Type: conference-paper

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$I_{on}-\mathrm{I}_{off}$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/Al2O3FTJs.

Source: Proceedings of ESSCIRC

DOI: 10.1109/esscirc55480.2022.9911392

Authors: Suzanne Lancaster, Quang T. Duong, Erika Covi, Thomas Mikolajick, Stefan Slesazeck

Institutions: NaMLab (Germany), Technische Universität Dresden

Language: English

Topic: Ferroelectric and Negative Capacitance Devices

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: California Department of Fish and Game, European Commission, Deutsche Forschungsgemeinschaft

Awards: BEOL technology platform based on ferroelectric synaptic devices for advanced neuromorphic processors, G634476351