Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads.

ABSTRACT: This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and Hf0.5Zr0.5O2 (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficie

SUBMITTER: Yu E 

PROVIDER: S-EPMC11551200 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature