The unique electron spin, transfer, polarization and magnetoelectric coupling characteristics of ABO3 multiferroic perovskite materials make them promising candidates for application in multifunctional nanoelectronic devices. Reversible ferroelectric polarization, controllable defect concentration and domain wall movement originated from the ABO3 multiferroic perovskite materials promotes its memristive effect, which further highlights data storage, information processing and neuromorphic computing in diverse artificial intelligence applications. In particular, ion doping, electrode selection, and interface modulation have been demonstrated in ABO3-based memristive devices for ultrahigh data storage, ultrafast information processing, and efficient neuromorphic computing. These approaches presented today including controlling the dopant in the active layer, altering the oxygen vacancy distribution, modulating the diffusion depth of ions, and constructing the interface-dependent band structure were believed to be efficient methods for obtaining unique resistive switching (RS) behavior for various applications. In this review, internal physical dynamics, preparation technologies, and modulation methods are systemically examined as well as the progress, challenges, and possible solutions are proposed for next generation emerging ABO3-based memristive application in artificial intelligence.

ABO3 multiferroic perovskite materials for memristive memory and neuromorphic computing

Although the structure and function of the human brain are still far from being fully understood, brain‐inspired computing architectures mainly consisting of artificial neurons and artificial synapses have been attracting more and more attentions due to their powerful computing capability and energy efficient operation. Synaptic plasticity is believed to be the origin of learning and memory. However, it is still a big challenge to realize artificial synapses with high reliability, good scalability, and low energy consumption, comparable to their biological counterparts. The memristor is a two‐terminal electronic device whose conductance can be reversibly regulated by electric stimuli. Memristive devices are considered ideal synaptic emulators due to their superior performance such as high speed and low power operation. This work reviews the recent advances in the development of memristive synapses based on different types of memristors. First, various working mechanisms of memristive synapses are discussed and compared. Then, different integration approaches of synaptic devices are described and compared. Various cognitive functions implemented with synaptic crossbar circuits are also described. Finally, the approaches for optimizing the performance parameters of memristive synapses and challenges to integrate the synaptic devices with complementary metal oxide semiconductor (CMOS) or memristive neurons are overviewed and discussed briefly.

Memristive Synapses for Brain-Inspired Computing

Abstract Recently, various resistance-based memory devices are being studied to replace charge-based memory devices to satisfy high-performance memory requirements. Resistance random access memory (RRAM) shows superior performances such as fast switching speed, structural scalability, and long retention. This work presented the different filament control by the DC voltages and verified its characteristics as a synaptic device by pulse measurement. Firstly, two current–voltage ( I – V ) curves are characterized by controlling a range of DC voltages. The retention and endurance for each different I – V curve were measured to prove the reliability of the RRAM device. The detailed voltage manipulation confirmed the characteristics of multi-level cell (MLC) and conductance quantization. Lastly, synaptic functions such as potentiation and depression, paired-pulse depression, excitatory post-synaptic current, and spike-timing-dependent plasticity were verified. Collectively, we concluded that Pt/Al 2 O 3 /TaN is appropriate for the neuromorphic device. 

https://nanoscalereslett.springeropen.com/counter/pdf/10.1186/s11671-022-03722-3

Multi-level Cells and Quantized Conductance Characteristics of Al2O3-Based RRAM Device for Neuromorphic System

Abstract With the burgeoning developments in artificial intelligence, hardware implementation of artificial neural network is also gaining pace. In this pursuit, ferroelectric devices (i.e., tunneling junctions and transistors) with voltage thresholds were recently proposed as suitable candidates. However, their development is hindered by the inherent integration issues of inorganic ferroelectrics, as well as poor properties of conventional organic ferroelectrics. In contrast to the conventional ferroelectric synapses, here we demonstrated a two-terminal ferroelectric synaptic device using a molecular ferroelectric (MF)/semiconductor interface. The interfacial resistance can be tuned via the polarization-controlled blocking effect of the semiconductor, owing to the high ferroelectricity and field amplification effect of the MF. Typical synaptic features including spike timing-dependent plasticity are substantiated. The introduction of the semiconductor also enables the attributes of optoelectronic synapse and in-sensor computing with high image recognition accuracies. Such interfaces may pave the way for the hardware implementation of multifunctional neuromorphic devices. 

/pdf/molecular-ferroelectric-semiconductor-interfacial-memristors-3m5vsphx.pdf

Molecular ferroelectric/semiconductor interfacial memristors for artificial synapses

Recently, due to the development of big data and computer technology, artificial intelligence (AI) has received extensive attention and made great progress. Edge intelligence pushes the computing center of AI from the cloud to individual users, making AI closer to life, but at the same time puts forward higher requirements for the realization of hardware, especially for edge acceleration. Taking convolutional neural networks (CNNs) as an example, which show excellent problem‐solving capabilities in different fields of academia and industry, it still faces issues of enormous computing volume and complex mapping architecture. Based on the computing‐in‐memory property and parallel multiply accumulate (MAC) operations of the emerging nonvolatile memristor arrays, herein the recent research progress of the edge intelligence memristive convolution accelerator is summarized. Furthermore, aiming at improving memristive convolutional accelerators, two potential optimization schemes are also discussed: The compression methods represented by quantization show great potential for static image processing, and the combination of a CNN with a long short‐term memory (LSTM) neural network makes up for the CNN's shortcomings of dynamic target processing. Finally, the future challenges and opportunities of edge intelligence accelerators based on memristor arrays are also discussed.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202000114

Recent Progress on Memristive Convolutional Neural Networks for Edge Intelligence

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfO
 x
 /Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing

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Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing