Inspired by the biological neuromorphic system, which exhibits a high degree of connectivity to process huge amounts of information, photonic memory is expected to pave a way to overcome the von ...Neumann bottleneck for nonconventional computing. Here, a photonic flash memory based on all‐inorganic CsPbBr3 perovskite quantum dots (QDs) is demonstrated. The heterostructure formed between the CsPbBr3 QDs and semiconductor layer serves as a basis for optically programmable and electrically erasable characteristics of the memory device. Furthermore, synapse functions including short‐term plasticity, long‐term plasticity, and spike‐rate‐dependent plasticity are emulated at the device level. The photonic potentiation and electrical habituation are implemented and the synaptic weight exhibits multiple wavelength response from 365, 450, 520 to 660 nm. These results may locate the stage for further thrilling novel advances in perovskite‐based memories.
Photonic flash memory based on all‐inorganic CsPbBr3 perovskite quantum dots is demonstrated. Synapse functions including short‐term plasticity, long‐term plasticity, spike‐rate‐dependent plasticity, as well as the photonic potentiation and electrical habituation are emulated at the device level. These results may locate the stage for further thrilling novel advances in perovskite‐based memories.
The Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity is regarded as the most precise model of the synapse, and is more compatible with neuromorphic computing. However, the ...development in BCM synaptic modification is rather limited since the memristive devices used to emulate the BCM lack tunable forgetting rate. Compared with memristors, memtransistors provide another gate‐tunable freedom degree, which will help to modulate the forgetting rate. In this work, the authors demonstrate a perfect BCM learning rule based on the 2D heterostructure memtransistor through using triplet‐spike timing dependent plasticity model. Two critical characteristics of the BCM rule, sliding frequency threshold and enhanced depression effect, are perfectly presented due to their spontaneous/gate‐assistant forgetting effect. The experimental results are extremely consistent with the BCM learning rule and suggest the potential application of 2D memtransistors in high‐order spatiotemporal recognition.
A 2D heterostructure‐based memtransistor is designed to emulate the Bienenstock–Cooper–Munro (BCM) theory, since this structure not only induces spontaneous forgetting process but also offers another gate‐tunable forgetting effect. BCM learning rule is perfectly demonstrated on this memtransistor using triplet‐STDP. Furthermore, high‐order spatiotemporal recognition is achieved in a feedforward neuron network based on the memtransistor.
MOFs have a highly ordered self‐assembled nanostructure, high surface area, nanoporosity with tunable size and shape, reliable host–guest interactions, and responsiveness to physical and chemical ...stimuli which can be exploited to address critical issues in sensor applications. On the one hand, the nanoscale pore size of MOFs ranging from less than 1 nm to ≈ 10 nm not only allows the diffusion of small molecules into the pores or through the MOF layer, but also excludes other larger molecules depending on the size, shape, and conformation of MOFs. On the other hand, MOFs with flexible structure exhibit a dynamic response to external stimuli, including guest molecules, temperature, pressure, pH, and light. Due to the unsaturated coordination metal sites and active functional groups, the interaction between certain analytes and active sites results in high selectivity. In this review, we summarize the latest studies on MOF‐based electronic sensors in terms of the function of MOFs, discuss challenges, and suggest perspectives.
Metal–organic frameworks (MOFs) that respond to physical and chemical stimuli are promising materials for electronic sensors owing to their outstanding sensing performance. In this Review, the functionality of MOFs as a mass‐loaded layer, filtration layer, electronic function layer, and optically sensitive layer is discussed.
Recently, in‐sensor computing with individual sensors or multiple connected sensors directly processing information has been proposed to improve energy, area, and time efficiency of artificial ...intelligence systems. Current investigations mainly focus on a single sensory processing such as auditory, visual, tactile, olfactory, and so on. However, a human perception system can sense and process different types of information with a complex environment and small perceptive field simultaneously. For example, the recognition accuracy of human eyes is highly affected by the environment such as extremely low or high relative humidity (RH). Here, a multi‐modal MXene‐ZnO memristor that combines visual data sensing, RH sensing, and pre‐processing functions to emulate the unique environmental adaptive behavior of the human eye is designed and constructed. The multi‐field controlled resistive switching of the MXene‐ZnO memristor is originated from the photon‐/protons‐regulated formation of oxygen vacancies filaments. Finally, in‐sensor computing with a MXene‐ZnO memristor functioning as both filter to preprocess the information and synapse to implement a weight updating process with different humidity adaptability has been demonstrated. Multimodal in‐sensor computing provides the potential to reduce the underlying circuitry complexity of the traditional neuromorphic visual system and contributes to the development of intelligence in device‐level implementations.
The resistive switching characteristics of Mxene‐ZnO can be modulated by altering the intensity of either light or humidity, which has been never realized with other materials. Both low‐level in‐sensory processing (noise suppression and filtering) and high‐level in‐sensor computing (weight updating) have been demonstrated.
Ambipolar transistors represent a class of transistors where positive (holes) and negative (electrons) charge carriers both can transport concurrently within the semiconducting channel. The basic ...switching states of ambipolar transistors are comprised of common off‐state and separated on‐state mainly impelled by holes or electrons. During the past years, diverse materials are synthesized and utilized for implementing ambipolar charge transport and their further emerging applications comprising ambipolar memory, synaptic, logic, and light‐emitting transistors on account of their special bidirectional carrier‐transporting characteristic. Within this review, recent developments of ambipolar transistor field involving fundamental principles, interface modifications, selected semiconducting material systems, device structures, ambipolar characteristics, and promising applications are highlighted. The existed challenges and prospective for researching ambipolar transistors in electronics and optoelectronics are also discussed. It is expected that the review and outlook are well timed and instrumental for the rapid progress of academic sector of ambipolar transistors in lighting, display, memory, as well as neuromorphic computing for artificial intelligence.
Ambipolar transistors represent transistors that allow synchronous transport of electrons and holes and their accumulation within semiconductors. This review provides a comprehensive summary of recent advances in various semiconducting materials realized in ambipolar transistors and their functional memory, synapse, logic, as well as light‐emitting applications.
Flexible sensors that efficiently detect various stimuli relevant to specific environmental or biological species have been extensively studied due to their great potential for the Internet of Things ...and wearable electronics applications. The application of flexible and stretchable electronics to device‐engineering technologies has enabled the fabrication of slender, lightweight, stretchable, and foldable sensors. Here, recent studies on flexible sensors for biological analytes, ions, light, and pH are outlined. In addition, contemporary studies on device structure, materials, and fabrication methods for flexible sensors are discussed, and a market overview is provided. The conclusion presents challenges and perspectives in this field.
Flexible sensors have been extensively studied due to their great potential for Internet of Things and wearable electronics applications. Recent studies on flexible sensors for biological analytes, ions, light, and pH are outlined. Contemporary studies on device structure, materials, fabrication methods, and a market overview for flexible sensors are discussed. In conclusion, challenges and perspectives in this field are presented.
The human brain is a sophisticated, high‐performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been ...pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike‐based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain‐like computing.
Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the brain, providing a new insight for building machines having artificial intelligence. A comprehensive overview of the development of neuromorphic engineering from biological nervous systems to spike‐based neuromorphic computing platforms is provided.
Because of current fabrication limitations, miniaturizing nonvolatile memory devices for managing the explosive increase in big data is challenging. Molecular memories constitute a promising ...candidate for next‐generation memories because their properties can be readily modulated through chemical synthesis. Moreover, these memories can be fabricated through mild solution processing, which can be easily scaled up. Among the various materials, polyoxometalate (POM) molecules have attracted considerable attention for use as novel data‐storage nodes for nonvolatile memories. Here, an overview of recent advances in the development of POMs for nonvolatile memories is presented. The general background knowledge of the structure and property diversity of POMs is also summarized. Finally, the challenges and perspectives in the application of POMs in memories are discussed.
The application of clusters of polyoxometalates (POMs) in electronic devices, particularly in memory devices is discussed. The multielectron redox behavior and high chemical stability and tenability, as well as the compatibility with nanoscale‐level scaling‐down techniques are impressive figures of merits. POMs can be used in a wide range of fields, from chemistry to catalysis, and from memory devices to energy‐storage devices.
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