Ferroelectric memristor based on Li-doped BiFeO3 for information processing

Abstract

As a nanoscale semiconductor memory device, a ferroelectric memristor has promising prospects to break through the von Neumann framework in terms of artificial synaptic function, information processing, and integration. This study presents the fabrication of Li0.09Bi0.91FeO3 as the functional layer for a memristor device based on the Si substrate, enabling the integration of silicon complementary metal oxide semiconductor technology. In addition, it exhibits bipolar resistance switching characteristics in a direct current mode and can rapidly achieve stable conductance tunability at higher frequencies through the applied pulse for biosynapse simulation. More importantly, multiple devices are connected into electrical circuits to realize storage functions with information processing and programmable characteristics. This work paves the way for near-future applications of ferroelectric memristors in information processing.

Superlow Power Consumption Memristor Based on Borphyrin–Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing

“…2 The memristor is a two-terminal neuromorphic device that is considered an ideal building block for artificial synapses because of its compatibility with the complementary metal− oxide−semiconductor process, ease of fabrication, small size, and high packing density. 6−8 Various materials have been used to fabricate memristors and imitate neural synaptic functions, including ferroelectrics, 9,10 transition metal dichalcogenides, 11−13 transition metal oxides, 14,15 and halide perovskites. 16,17 However, the majority of these materials are inorganic and rely on complex fabrication procedures.…”

“…Neuromorphic computing is considered as an unconventional computing, which aims to realize an intelligent system that can imitate neurobiological processes with learning and memory functions by building a computational architecture analogous to the biological brain structure. , Information is processed and stored by neurons and synapses in a neural network, which is a highly connected and parallel approach that not only consumes less energy but also more efficiently carries out complicated cognitive tasks than conventional computing approaches based on the von Neumann architecture. − Synaptic devices, which simulate the biological synapses of the brain, are a fundamental component of a neural network . The memristor is a two-terminal neuromorphic device that is considered an ideal building block for artificial synapses because of its compatibility with the complementary metal–oxide–semiconductor process, ease of fabrication, small size, and high packing density. − Various materials have been used to fabricate memristors and imitate neural synaptic functions, including ferroelectrics, , transition metal dichalcogenides, − transition metal oxides, , and halide perovskites. , However, the majority of these materials are inorganic and rely on complex fabrication procedures. Growing concern over the impact of toxic metals and compounds in electronic waste on the ecological environment has increased interest in the development of devices based on biodegradable and environmentally friendly organic materials.…”