In our brain, information is exchanged among neurons in the form of spikes where both the space (which neuron fires) and time (when the neuron fires) contain relevant information. Every neuron is connected to other neurons by synapses, which are continuously created, updated, and stimulated to enable information processing and learning. Realizing the brain-like neuron/synapse network in silicon would enable artificial autonomous agents capable of learning, adaptation, and interaction with the environment. Toward this aim, the conventional microelectronic technology, which is based on complementary metal–oxide–semiconductor transistors and the von Neumann computing architecture, does not provide the desired energy efficiency and scaling potential. A generation of emerging memory devices, including resistive switching random access memory (RRAM) also known as the memristor, can offer a wealth of physics-enabled processing capabilities, including multiplication, integration, potentiation, depression, and time-decaying stimulation, which are suitable to recreate some of the fundamental phenomena of the human brain in silico. This work provides an overview about the status and the most recent updates on brain-inspired neuromorphic computing devices. After introducing the RRAM device technologies, we discuss the main computing functionalities of the human brain, including neuron integration and fire, dendritic filtering, and short- and long-term synaptic plasticity. For each of these processing functions, we discuss their proposed implementation in terms of materials, device structure, and brain-like characteristics. The rich device physics, the nano-scale integration, the tolerance to stochastic variations, and the ability to process information in situ make the emerging memory devices a promising technology for future brain-like hardware intelligence.

/pdf/brain-inspired-computing-via-memory-device-physics-26ohole99d.pdf

Brain-inspired computing via memory device physics

With the rapid development of next-generation artificial intelligence technology, research on advanced machine vision has received extensive attention. It is well-known that significant progress has been made in artificial vision systems based on light sensors, but the separate light sensor and memory require additional time for information transfer to realize computation due to the limitation of the von Neumann architecture, which delays the computational speed and hinders large-scale integration. In recent years, the emergence of photoelectric memristors has brought new inspiration to the study of machine vision, which is expected to overcome the above problems. Photoelectric memristors can not only respond directly to light stimuli but also perform temporary memory and real-time processing of visual information and sensory data, providing a promising hardware foundation for the development of artificial vision systems. In this review, the background and related theory of photoelectric memristors and machine vision are first introduced. Then, the research progress of photoelectric memristors and machine vision based on them is reviewed. Finally, the existing problems impeding the progress of machine vision based on photoelectric memristors are summarized, and the future development is predicted. 

Photoelectric Memristor-Based Machine Vision for Artificial Intelligence Applications

ConspectusAs a new kind of carbon based functional material, carbon dots (CDs) have sparked much interest in recent years. The tunable structure, composition, and morphology of CDs unlocks opportunities to enable diversity in their photoelectrochemical properties, and thus they show great potential in various applications such as biology, catalysis, sensors, and energy storage. Nevertheless, the related understanding of CDs is insufficient at present due to their inherent complexity of microstructure, which involves the intersection of high polymer, bulk carbon, and quantum dot (QD). A good understanding of the underlying mechanism behind the properties of CDs is still a formidable challenge, requiring the integration of robust knowledge from organic chemistry, materials science, and solid state physics. Within this context, discovering more appealing properties, elucidating fundamental factors that affect the properties and proposing effective engineering strategies that can realize specific functions for CDs are now highly pursued by researchers.At the beginning of this Account, the main features of CDs are introduced, where not only the basic structural, compositional and morphological characteristics but also the rich photoelectrochemical properties are elucidated, among which the band gap, chirality, photoinduced potential, and electron sink effect are particularly emphasized. Furthermore, new analysis techniques including transient photoinduced current (TPC), transient photoinduced voltage (TPV), and machine learning (ML) to reveal the unique properties of CDs are described. Then, several appealing strategies that aim to rationally tailor CDs for oriented applications are highlighted. These regulation strategies are morphology modulation (e.g., developing CDs with new geometrical configuration, controlling the particle size), phase engineering (e.g., altering the phase crystallinity, introducing the foreign atoms), surface functionalization (e.g., grafting various types of functional groups), and interfacial tuning (e.g., building CD-based nanohybrids with well-defined interfaces). Although the fundamental investigation of CDs is relatively undeveloped because of their complexity, this does not hinder their wide application. At the same time, exploring the extensive applications of CDs will promote their in-depth understanding. Finally, the chances for building a CD-centered blueprint for sustainable society are explored and challenges for future research in the field of CDs are proposed as follows: (i) the controllable synthesis of CDs with uniform size; (ii) search for novel CDs with unique structure, morphology, or composition; (iii) quantitative understanding of the property of CDs; (iv) performance enhancement by external forces such as magnetism or heat injection; (v) construction of the dual carbon concept; (vi) further research on different photocatalytic applications. On the whole, this Account may provide meaningful references for the understanding of the microstructure-property correlation as well as the regulation of CDs, thereby promoting their transition from fundamental research to practical application.

Diversity and Tailorability of Photoelectrochemical Properties of Carbon Dots.

In neuromorphic computing networks, a flexible synaptic memristor with high recognition accuracy is highly desired. In this study, ZnO nanosheets (ZnO NS) embedded within a polymethyl methacrylate host material are used as the intermediate layer to prepare flexible synaptic memristor at a low‐temperature of 80 °C. The device shows excellent switching characteristics with low SET/RESET voltages (−0.4 V/0.4 V) and stable retention characteristic (104 s). By modulating the conductance continuously, the flexible synaptic memristor simulates typical synaptic plasticities, including excitation post‐synaptic current, paired‐pulse facilitation, and spike‐timing dependent plasticity. Especially, the neuromorphic system built from flexible ZnO NS‐based memristors achieves a high recognition accuracy up to 97.7% for handwriting digit. Under the influence of 5% Uniform noise and 5% Gaussian noise, recognition accuracies are maintained at 94.6% and 93.7%, respectively. These properties are well maintained even when bending 1000 times at a radius of 5 mm. The flexible ZnO NS‐based memristor shows great prospects in wearable devices and neural morphology calculation.

Flexible ZnO Nanosheet‐Based Artificial Synapses Prepared by Low‐Temperature Process for High Recognition Accuracy Neuromorphic Computing

Artificial synapses enabled neuromorphic computing: From blueprints to reality

An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics. A graphene-based device can help computer chips behave more like human brains by transmitting current across thread-like wires. Neural synapses store memories by accessing different types of conductive states. Chinese researchers led by Haiyang Xu at Northeast Normal University in Changchun and Zhenhui Kang at Soochow University in Suzhou now demonstrate that graphene sheets with different conductivity levels—caused by adding or removing oxygen atoms—can also exhibit synapse-like behavior. The team developed a carbon-nitrogen composite to sandwich between two graphene electrodes with high and low levels of conductivity. Exposing the composite to ultraviolet light created numerous tiny filaments between the electrodes that physically restrict electron flow and provide gradual smooth transitions between the graphene electrodes’ two conductive states. The organic framework of this device also provides inherent flexibility for wearable devices. All-carbon memristive synapse is built through photo-reduction of a nanocomposite comprised of graphene oxide and N-doped carbon quantum dots. The analog-type resistive switching was demonstrated, which enabled the emulation of synaptic learning and pattern recognition with high accuracy. The all-carbon devices possess excellent transferability, flexibility and resistance to high temperature, showing the potential for the development of wearable neuromorphic computing system.

/pdf/photoreduced-nanocomposites-of-graphene-oxide-n-doped-carbon-brjvwj8isx.pdf

Photoreduced nanocomposites of graphene oxide/N-doped carbon dots toward all-carbon memristive synapses

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Photoreduced nanocomposites of graphene oxide/N-doped carbon dots toward all-carbon memristive synapses