HfO₂‐Based Memristor as an Artificial Synapse for Neuromorphic Computing with Tri‐Layer HfO₂/BiFeO₃/HfO₂ Design

Abstract

Neuromorphic devices are among the most emerging electronic components to realize artificial neural systems and replace traditional complementary metal-oxide semiconductor devices in recent times. In this work, tri-layer HfO 2 /BiFeO 3 (BFO)/HfO 2 memristors are designed by inserting traditional ferroelectric BFO layers measuring ≈4 nm after thickness optimization. The novel designed memristor shows excellent resistive switching (RS) performance such as a storage window of 10 4 and multi-level storage ability. Remarkably, essential synaptic functions can be successfully realized on the basis of the linearity of conductance modulation. The pattern recognition simulation based on neuromorphic network is conducted with 91.2% high recognition accuracy. To explore the RS performance enhancement and artificial synaptic behaviors, conductive filaments (CFs) composed of Hafnium (Hf ) single crystal with a hexaganal lattice structure are observed using high-resolution transmission electron microscopy. It is reasonable to believe that the sufficient oxygen vacancies in the inserting BFO thin film play a crucial role in adjusting the morphology and growth of Hf CFs, which leads to the promising synaptic and enhanced RS behavior, thus demonstrating the potential of this memristor for use in neuromorphic computing.

Mixed‐Dimensional Formamidinium Bismuth Iodides Featuring In‐Situ Formed Type‐I Band Structure for Convolution Neural Networks

“…Furthermore, a large number of defects and low E a for iodide vacancies is also beneficial to linearity and dynamic range. [ 13,16 ]…”

“…Furthermore, a large number of defects and low E a for iodide vacancies is also beneficial to linearity and dynamic range. [13,16] I-V characteristic of memristor devices is displayed in Figure 4a, where positive voltage sweep (0 V → +1 V → 0 V) is followed by negative voltage sweep (0 V → −1 V → 0 V). ON/OFF ratio (low resistance state/high resistance state) for the mixed phase is determined to be 6.75 which is higher than those for FABi 3 I 10 (1.25) and FA 3 Bi 2 I 9 (2.25).…”

“…[ 11,12 ] Unlike ECM devices, the VCM devices using media such as metal oxides can feature very low operating voltages, which can be further reduced to less than 1 V by generating plenty of vacancies in the metal oxide. [ 13 ] Furthermore, vacancies are beneficial to achieving linear P/D characteristics. [ 13 ] However, a large number of vacancies can increase the overall current of artificial synapses because charge carriers are also generated in the case that vacancies act like dopants, [ 14,15 ] which is a disadvantage to energy consumption.…”

“…[ 13 ] Furthermore, vacancies are beneficial to achieving linear P/D characteristics. [ 13 ] However, a large number of vacancies can increase the overall current of artificial synapses because charge carriers are also generated in the case that vacancies act like dopants, [ 14,15 ] which is a disadvantage to energy consumption. In addition, high level of current may disturb scalable neuromorphic computing because the amount of current that flows in a wire is limited and the raised current can be saturated in artificial neurons connected with artificial synapses.…”

Proton gated oxide neuromorphic transistors with bionic vision enhancement and information decoding

“…35 Using two-terminal memristor based neuromorphic devices, high recognition accuracies of above 90% have been reported using a supervised learning algorithm. 36,37 An excellent recognition accuracy of above 90% has also been realized for artificial neural networks (ANNs) using neuromorphic transistors. 38–40 However, it should be noted here that an effective synaptic weight updating strategy is still demanded for linear synaptic weight updating for ANN applications.…”