Maximizing the Synaptic Efficiency of Ferroelectric Tunnel Junction Devices Using a Switching Mechanism Hidden in an Identical Pulse Programming Learning Scheme

Abstract

Memristors play a pivotal role in advanced computing, with memristor-based crossbar arrays showing promise for various artificial neural networks. Among these, HfO₂-based ferroelectric tunnel junctions (FTJs) stand out as ideal synaptic devices for neuromorphic computing. Their compatibility with the complementary metal oxide semiconductor process and intrinsic energy efficiency make them particularly appealing. While an increasing number of studies adopt identical pulse programming (IPP) with short width to update the conductance of HfO₂-based FTJs synaptic devices, conventional ferroelectric switching models fall short in describing updates the conductance with the IPP scheme. Consequently, studies achieving conductance updates via IPP lack an underlying mechanism explanation, potentially limiting the application of HfO₂-based FTJs as synaptic devices. This study explores the potential of ferroelectric Zr-doped HfO₂ (HZO) FTJs to undergo learning through the IPP scheme. Synaptic characteristics, including the number of conductance states, symmetry, linearity, write energy, and latency by modulating IPP scheme conditions are optimized. Finally, the applicability of HZO FTJ as a synaptic device by assessing learning accuracy in pattern recognition through artificial neural network simulation based on the optimized synaptic characteristics is evaluated.