As a new member of carbon allotropes, graphdiyne (GDY) has the characteristics of being one-atom-thick with two-dimensional layers comprising sp and sp2 hybridized carbon atoms, and represents a trend in the development of carbon materials. Its unique chemical and electronic structures give GDY many unique and fascinating properties such as rich chemical bonds, highly conjugated and super-large π structures, infinitely distributed pores and high inhomogeneity of charge distribution. GDY has entered a period of rapid development, especially with the significant emergence of fundamental research and applied research achievements over the past five years. As one of the frontiers of chemistry and materials science, graphdiyne was listed in the Top 10 research areas in the 2020 Research Frontiers report and was jointly released in the Top 10 in the world by Clarivate and the Chinese Academy of Sciences. The research results have shown the great potential of GDY in the applications of energy, catalysis, environmental science, electronic devices, detectors, biomedicine and therapy, etc. Scientists are eager to explore and fully reveal the new properties, discover new scientific concepts and phenomena, discover the new conversion modes and mechanisms of GDY in photoelectricity, energy, and catalysis, etc., and build the important scientific value of new conversion devices. This review covers research on the foundation and application of GDY, such as the controlled preparation of new methods of GDY and GDY-based materials, studies on new mechanisms and properties in chemistry and physics, and the foundation and applications in energy, catalysis, photoelectric and devices. 

2D graphdiyne: an emerging carbon material

Ion migration as well as electron transfer and coupling in resistive switching materials endow memristors with a physically tunable conductance to resemble synapses, neurons, and their networks. Four different types of volatile memristors and another four types of nonvolatile memristors are systemically surveyed in terms of the switching mechanisms and electrical properties that are the basis of different computing applications. The volatile memristor features spontaneous conductance decay after the cease of electrical/optical stimulations, which are closely related to the surface atom diffusion, metal–insulator–transition (including charge–density–wave), thermal spontaneous emission, and charge polarization. Such unique dynamic state evolution at the edge of chaos has enabled them to emulate certain synaptic and neural dynamics, leading to various applications ranging from spiking neural networks to combinatorial optimizations. Nonvolatile resistive switching behavior originated from the electron spins, ferroelectric polarization, crystalline-amorphous transitions or interplay between ions and electrons enables the memristor array to implement the vector–matrix multiplication, which is the key convolutional operation in artificial neural networks. The progress, challenges, and opportunities for both volatile and nonvolatile memristor in the level of materials, integration technology, algorithm, and system are highlighted in this review. 

Volatile and Nonvolatile Memristive Devices for Neuromorphic Computing

Reconfigurable optoelectronic memristor for in-sensor computing applications

Non-Volatile Electrolyte-Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low-Power Neuromorphic Computing and Logic-In-Memory

Neuromorphic engineering, a methodology for emulating synaptic functions or neural systems, has attracted tremendous attention for achieving next-generation artificial intelligence technologies in the field of electronics and photonics. However, to emulate human visual memory, an active pixel sensor array for neuromorphic photonics has yet to be demonstrated, even though it can implement an artificial neuron array in hardware because individual pixels can act as artificial neurons. Here, we present a neuromorphic active pixel image sensor array (NAPISA) chip based on an amorphous oxide semiconductor heterostructure, emulating the human visual memory. In the 8 × 8 NAPISA chip, each pixel with a select transistor and a neuromorphic phototransistor is based on a solution-processed indium zinc oxide back channel layer and sputtered indium gallium zinc oxide front channel layer. These materials are used as a triggering layer for persistent photoconductivity and a high-performance channel layer with outstanding uniformity. The phototransistors in the pixels exhibit both photonic potentiation and depression characteristics by a constant negative and positive gate bias due to charge trapping/detrapping. The visual memory and forgetting behaviors of the NAPISA can be successfully demonstrated by using the pulsed light stencil method without any software or simulation. This study provides valuable information to other neuromorphic devices and systems for next-generation artificial intelligence technologies.

Neuromorphic Active Pixel Image Sensor Array for Visual Memory.

Optoelectronic synapses integrating synaptic and optical-sensing functions exhibit large advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy-efficient way. However, electric stimulation is still essential for existing optoelectronic synapses to realize bidirectional weight-updating, restricting the processing speed, bandwidth, and integration density of the devices. Herein, a two-terminal optical synapse based on a wafer-scale pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure is proposed that can emulate both the excitatory and inhibitory synaptic behaviors in an optical pathway. The simple device architecture and low-dimensional features of the heterostructure endow the optical synapse with robust flexibility for wearable electronics. This optical synapse features a linear and symmetric conductance-update trajectory with numerous conductance states and low noise, which facilitates the demonstration of accurate and effective pattern recognition with a strong fault-tolerant capability even at bending states. A series of logic functions and associative learning capabilities have been demonstrated by the optical synapses in optical pathways, significantly enhancing the information processing capability for neuromorphic computing. Moreover, an integrated visible information sensing memory processing system based on the optical synapse array is constructed to perform real-time detection, in situ image memorization, and distinction tasks. This work is an important step toward the development of optogenetics-inspired neuromorphic computing and adaptive parallel processing networks for wearable electronics.

Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing.

Graphdiyne (GDY) is emerging as a promising material for various applications owing to its unique structure and fascinating properties. However, the application of GDY in electronics and optoelectronics are still in its infancy, primarily owing to the huge challenge in the synthesis of large-area and uniform GDY film for scalable applications. Here a modified van der Waals epitaxy strategy is proposed to synthesize wafer-scale GDY film with high uniformity and controllable thickness directly on graphene (Gr) surface, providing an ideal platform to construct large-scale GDY/Gr-based optoelectronic synapse array. Essential synaptic behaviors have been realized, and the linear and symmetric conductance-update characteristics facilitate the implementation of neuromorphic computing for image recognition with high accuracy and strong fault tolerance. Logic functions including “NAND” and “NOR” are integrated into the synapse which can be executed in an optical pathway. Moreover, a visible information sensing-memory-processing system is constructed to execute real-time image acquisition, in situ image memorization and distinction tasks, avoiding the time latency and energy consumption caused by data conversion and transmission in conventional visual systems. These results highlight the potential of GDY in applications of neuromorphic computing and artificial visual systems.

https://link.springer.com/content/pdf/10.1007/s12274-021-3381-4.pdf

De-Han Qi

Papers

Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing.

Synthesis of wafer-scale graphdiyne/graphene heterostructure for scalable neuromorphic computing and artificial visual systems