Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems. 

Porous crystalline materials for memories and neuromorphic computing systems.

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so‐called cycle‐to‐cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experimental characterization to the adequation of modeling and simulation techniques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, mesoscopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.

pdf/variability-in-resistive-memories-1kn2yy9f.pdf

Variability in Resistive Memories

The imitation of human visual memory, which demands the multifunctional integration of light sensors similar to the eyes, and image memory, similar to the brain. Although humans have already implemented electronic devices with visual memory functions, these devices require a combination of various components and logical circuits. With the advancement of integrated circuits, the miniaturization and high integration of devices are an inevitable trend. Therefore, the combining of visual perception and high-performance information storage capabilities into a single device to achieve visual memory remains a challenging. In this study, inspired by the function of human visual memory, we designed a dual-functional perovskite-based photodetector (PD) and memristor to realize visual perception and memory capacities. As a PD, it realized an ultra-high self-powered responsivity of 276 mAW-1 , a high detectivity of 4.7×1011 Jones (530 nm; light intensities, 2.34 mWcm-2 ), and a high rectification ratio of ∼100 (± 2 V). As a memristor, an ultra-high on/off ratio (∼105 ), an ultra-low power consumption of 3×10-11  W, a low setting voltage (0.15 V), and a long retention time (>7000 s) were realized. Moreover, the dual-functional device has the capacity to perceive and remember light paths and store data with good cyclic stability. Our device exhibits perceptual and cyclic erasable memory functions, which provide new opportunities for mimicking human visual memory in future multifunctional applications. This article is protected by copyright. All rights reserved.

Dual-functional Perovskite-based Photodetector And Memristor for Visual Memory.

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field. 

Memristor-Based Artificial Chips.

Functional diversification at the single-device level has become essential for emerging optical neural network (ONN) development. Stable ferroelectricity harnessed with strong light sensitivity in α-In2Se3 holds great potential for developing ultrathin neuromorphic devices. Herein, we demonstrated an all-2D van der Waals heterostructure-based programmable synaptic field effect transistor (FET) utilizing a ferroelectric α-In2Se3 nanosheet and monolayer graphene. The devices exhibited reconfigurable, multilevel nonvolatile memory (NVM) states, which can be successively modulated by multiple dual-mode (optical and electrical) stimuli and thereby used to realize energy-efficient, heterosynaptic functionalities in a biorealistic fashion. Furthermore, under light illumination, the prototypical device can toggle between volatile (photodetector) and nonvolatile optical random-access memory (ORAM) logic operation, depending upon the ferroelectric-dipole induced band adjustment. Finally, plasticity modulation from short-term to prominent long-term characteristics over a wide dynamic range was demonstrated. The inherent operation mechanism owing to the switchable polarization-induced electronic band alignment and bidirectional barrier height modulation at the heterointerface was revealed by conjugated electronic transport and Kelvin-probe force microscopy (KPFM) measurements. Overall, robust (opto)electronic weight controllability for integrated in-sensor and in-memory logic processors and multibit ORAM systems was readily accomplished by the synergistic ferrophotonic heterostructure properties. Our presented results facilitate the technological implementation of versatile all-2D heterosynapses for next-generation perception, optoelectronic logic systems, and Internet-of-Things (IoT) entities.

Graphene-In2Se3 van der Waals Heterojunction Neuristor for Optical In-Memory Bimodal Operation.

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks towards realizing the full functionalities of biological neurons are discussed. This article is protected by copyright. All rights reserved.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adma.202205047

Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications.

Memory technologies and applications implemented fully or partially using emerging 2D materials have attracted increasing interest in the research community in recent years. Their unique characteristics provide new possibilities for highly integrated circuits with superior performances and low power consumption, as well as special functionalities. Here, an overview of progress in 2D‐material‐based memory technologies and applications on the circuit level is presented. In the material growth and fabrication aspects, the advantages and disadvantages of various methods for producing large‐scale 2D memory devices are discussed. Reports on 2D‐material‐based integrated memory circuits, from conventional dynamic random‐access memory, static random‐access memory, and flash memory arrays, to emerging memristive crossbar structures, all the way to 3D monolithic stacking architecture, are systematically reviewed. Comparisons between experimental implementations and theoretical estimations for different integration architectures are given in terms of the critical parameters in 2D memory devices. Attempts to use 2D memory arrays for in‐memory computing applications, mostly on logic‐in‐memory and neuromorphic computing, are summarized here. Finally, challenges that impede the large‐scale applications of 2D‐material‐based memory are reviewed, and perspectives on possible approaches toward a more reliable system‐level fabrication are also given, hopefully shedding some light on future research.

Circuit‐Level Memory Technologies and Applications based on 2D Materials


 Synaptic devices with tunable weight hold great promise in enabling non-von Neumann architecture for energy efficient computing. However, conventional metal-insulator-metal (MIM) based two-terminal memristors share the same physical channel for both programming and reading, therefore the programming power consumption is dependent on the synaptic resistance states and can be particularly high when the memristor is in the low resistance states. Three terminal synaptic transistors, on the other hand, allow synchronous programming and reading and have been shown to possess excellent reliability. Here we present a binary oxide based three-terminal MoS2 synaptic device, in which the channel conductance can be modulated by interfacial charges generated at the oxide interface driven by Maxwell-Wagner instability. The binary oxide stack serves both as an interfacial charge host and gate dielectrics. Both excitatory and inhibitory behaviors are experimentally realized, and the presynaptic potential polarity can be effectively controlled by engineering the oxide stacking sequence, which is a unique feature compared with existing charge-trap based synaptic devices and provides a new tuning knob for controlling synaptic device characteristics. By adopting a three-terminal transistor structure, the programming channel and reading channel are physically separated and the programming power consumption can be kept constantly low (~ 50 pW) across a wide dynamic range of 105. This work demonstrates a complementary metal oxide semiconductor (CMOS) compatible approach to build power efficient synaptic devices for artificial intelligence (AI) applications.

Power efficient MoS2 synaptic devices based on maxwell-wagner interfacial charging in binary oxides

The development of ultrathin, stable ferroelectric materials is crucial for advancing high-density, low-power electronic devices. Nonetheless, ultrathin ferroelectric materials are rare due to the critical size effect. Here, we present a novel ferroelectric material, magnesium molybdenum oxide (Mg2 Mo3 O8 ). High-quality ultrathin Mg2 Mo3 O8 crystals have been synthesized using chemical vapor deposition. Ultrathin Mg2 Mo3 O8 has a wide bandgap (∼4.4 eV) and nonlinear optical response. Mg2 Mo3 O8 crystals of varying thicknesses exhibit out-of-plane ferroelectric properties at room temperature, with ferroelectricity retained even at a 2 nm thickness. The Mg2 Mo3 O8 exhibits a relatively large remanent polarization ranging from 33 to 52 μC/cm2 , which is tunable by changing its thickness. Notably, Mg2 Mo3 O8 possess a high Curie temperature (>980 °C) across various thickness. Moreover, the as-grown Mg2 Mo3 O8 crystals display remarkable stability under harsh environments. This work introduces nolanites-type crystal into ultrathin ferroelectrics. The scalable synthesis of stable ultrathin ferroelectric Mg2 Mo3 O8 expands the scope of ferroelectric materials and may prosper applications of ferroelectrics. This article is protected by copyright. All rights reserved.

Scalable Synthesis of High-quality Ultrathin Ferroelectric Magnesium Molybdenum Oxide.

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Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications.

Circuit‐Level Memory Technologies and Applications based on 2D Materials

Power efficient MoS2 synaptic devices based on maxwell-wagner interfacial charging in binary oxides

Scalable Synthesis of High-quality Ultrathin Ferroelectric Magnesium Molybdenum Oxide.