Co-assembled perylene/graphene oxide photosensitive heterobilayer for efficient neuromorphics

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

A memristive-photoconductive transduction methodology for accurately nondestructive memory readout

Crossbar resistive memory architectures enable high-capacity storage and neuromorphic computing, accurate retrieval of the stored information is a prerequisite during read operation. However, conventional electrical readout normally suffer from complicated process, inaccurate and destructive reading due to crosstalk effect from sneak path current. Here we report a memristive-photoconductive transduction (MPT) methodology for precise and nondestructive readout in a memristive crossbar array. The individual devices present dynamic filament form/fuse for resistance modulation under electric stimulation, which leads to photogenerated carrier transport for tunable photoconductive response under subsequently light pulse stimuli. This coherent signal transduction can be used to directly detect the memorized on/off states stored in each cell, and a prototype 4 * 4 crossbar memories has been constructed and validated for the fidelity of crosstalk-free readout in recall process.

Ultra-low power carbon nanotube/porphyrin synaptic arrays for persistent photoconductivity and neuromorphic computing

Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials, it can achieve optical storage and neuromorphic computing, surpassing the von Neuman architecture-based systems. However, previous research implemented PPC required additional gate voltages and low temperatures, which need additional energy consumption and PPC cannot be achieved across a wide temperature range. Here, we fabricated a simple heterojunctions using zinc(II)-meso-tetraphenyl porphyrin (ZnTPP) and single-walled carbon nanotubes (SWCNTs). By leveraging the strong binding energy at the heterojunction interface and the unique band structure, the heterojunction achieved PPC over an exceptionally wide temperature range (77 K-400 K). Remarkably, it demonstrated nonvolatile storage for up to 2×104 s, without additional gate voltage. The minimum energy consumption for each synaptic event is as low as 6.5 aJ. Furthermore, we successfully demonstrate the feasibility to manufacture a flexible wafer-scale array utilizing this heterojunction. We applied it to autonomous driving under extreme temperatures and achieved as a high impressive accuracy rate as 94.5%. This tunable and stable wide-temperature PPC capability holds promise for ultra-low-power neuromorphic computing.

Heterointerface engineering of layered double hydroxide/MAPbBr3 heterostructures enabling tunable synapse behaviors in a two-terminal optoelectronic device

Solution-processable semiconductor heterostructures enable scalable fabrication of high performance electronic and optoelectronic devices with tunable functions via heterointerface control. In particular, artificial optical synapses require interface manipulation for nonlinear signal processing. However, the limited combinations of materials for heterostructure construction have restricted the tunability of synaptic behaviors with simple device configurations. Herein, MAPbBr3 nanocrystals were hybridized with MgAl layered double hydroxide (LDH) nanoplates through a room temperature self-assembly process. The formation of such heterostructures, which exhibited an epitaxial relationship, enabled effective hole transfer from MAPbBr3 to LDH, and greatly reduced the defect states in MAPbBr3. Importantly, the ion-conductive nature of LDH and its ability to form a charged surface layer even under low humidity conditions allowed it to attract and trap holes from MAPbBr3. This imparted tunable synaptic behaviors and short-term plasticity (STP) to long-term plasticity (LTP) transition to a two-terminal device based on the LDH-MAPbBr3 heterostructures. The further neuromorphic computing simulation under varying humidity conditions showcased their potential in learning and recognition tasks under ambient conditions. Our work presents a new type of epitaxial heterostructure comprising metal halide perovskites and layered ion-conductive materials, and provides a new way of realizing charge-trapping induced synaptic behaviors.

Progress in Advanced Infrared Optoelectronic Sensors

Infrared optoelectronic sensors have attracted considerable research interest over the past few decades due to their wide-ranging applications in military, healthcare, environmental monitoring, industrial inspection, and human-computer interaction systems. A comprehensive understanding of infrared optoelectronic sensors is of great importance for achieving their future optimization. This paper comprehensively reviews the recent advancements in infrared optoelectronic sensors. Firstly, their working mechanisms are elucidated. Then, the key metrics for evaluating an infrared optoelectronic sensor are introduced. Subsequently, an overview of promising materials and nanostructures for high-performance infrared optoelectronic sensors, along with the performances of state-of-the-art devices, is presented. Finally, the challenges facing infrared optoelectronic sensors are posed, and some perspectives for the optimization of infrared optoelectronic sensors are discussed, thereby paving the way for the development of future infrared optoelectronic sensors.

Mimicking Bidirectional Inhibitory Synapse Using a Porous-Confined Ionic Memristor with Electrolyte/Tris(4-aminophenyl)amine Neurotransmitter

Ionic memristors can emulate brain-like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory-excitatory and excitatory-inhibitory synapses, reports on memristors with the inhibitory-inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de-trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature-inferencing/-training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices.

An Organic Optoelectronic Synapse with Multilevel Memory Enabled by Gate Modulation

Artificial synaptic devices are emerging as contenders for next-generation computing systems due to their combined advantages of self-adaptive learning mechanisms, high parallel computation capabilities, adjustable memory level, and energy efficiency. Optoelectronic devices are particularly notable for their responsiveness to both voltage inputs and light exposure, making them attractive for dynamic modulation. However, engineering devices with reconfigurable synaptic plasticity and multilevel memory within a singular configuration present a fundamental challenge. Here, we have established an organic transistor-based synaptic device that exhibits both volatile and nonvolatile memory characteristics, modulated through gate voltage together with light stimuli. Our device demonstrates a range of synaptic behaviors, including both short/long-term plasticity (STP and LTP) as well as STP-LTP transitions. Further, as an encoding unit, it delivers exceptional read current levels, achieving a program/erase current ratio exceeding 105, with excellent repeatability. Additionally, a prototype 4 × 4 matrix demonstrates potential in practical neuromorphic systems, showing capabilities in the perception, processing, and memory retention of image inputs.

Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception

The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W-1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and "learning-experience" behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.

Recent Advances in Low-Dimensional Nanomaterials for Photodetectors

New emerging low-dimensional such as 0D, 1D, and 2D nanomaterials have attracted tremendous research interests in various fields of state-of-the-art electronics, optoelectronics, and photonic applications due to their unique structural features and associated electronic, mechanical, and optical properties as well as high-throughput fabrication for large-area and low-cost production and integration. Particularly, photodetectors which transform light to electrical signals are one of the key components in modern optical communication and developed imaging technologies for whole application spectrum in the daily lives, including X-rays and ultraviolet biomedical imaging, visible light camera, and infrared night vision and spectroscopy. Today, diverse photodetector technologies are growing in terms of functionality and performance beyond the conventional silicon semiconductor, and low-dimensional nanomaterials have been demonstrated as promising potential platforms. In this review, the current states of progress on the development of these nanomaterials and their applications in the field of photodetectors are summarized. From the elemental combination for material design and lattice structure to the essential investigations of hybrid device architectures, various devices and recent developments including wearable photodetectors and neuromorphic applications are fully introduced. Finally, the future perspectives and challenges of the low-dimensional nanomaterials based photodetectors are also discussed.

Nearly Panoramic Neuromorphic Vision with Transparent Photosynapses

Neuromorphic vision based on photonic synapses has the ability to mimic sensitivity, adaptivity, and sophistication of bio-visual systems. Significant advances in artificial photosynapses are achieved recently. However, conventional photosyanptic devices normally employ opaque metal conductors and vertical device configuration, performing a limited hemispherical field of view. Here, a transparent planar photonic synapse (TPPS) is presented that offers dual-side photosensitive capability for nearly panoramic neuromorphic vision. The TPPS consisting of all two dimensional (2D) carbon-based derivatives exhibits ultra-broadband photodetecting (365-970 nm) and ≈360° omnidirectional viewing angle. With its intrinsic persistent photoconductivity effect, the detector possesses bio-synaptic behaviors such as short/long-term memory, experience learning, light adaptation, and a 171% pair-pulse-facilitation index, enabling the synapse array to achieve image recognition enhancement (92%) and moving object detection.

Biosensing Systems Based on Graphene Oxide Fluorescence Quenching Effect

Graphene oxide (GO) is a versatile material obtained by the strong oxidation of graphite. Among its peculiar properties, there is the outstanding ability to significantly alter the fluorescence of many common fluorophores and dyes. This property has been exploited in the design of novel switch-ON and switch-OFF fluorescence biosensing platforms for the detection of a plethora of biomolecules, especially pathological biomarkers and environmental contaminants. Currently, novel advanced strategies are being developed for therapeutic, diagnostic and theranostic approaches to widespread pathologies caused by viral or bacterial agents, as well as to cancer. This work illustrates an overview of the most recent applications of GO-based sensing systems relying on its fluorescence quenching effect.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles

Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain-machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.

Near-Infrared Artificial Optical Synapse Based on the P(VDF-TrFE)-Coated InAs Nanowire Field-Effect Transistor

Optical synapse is the basic component for optical neuromorphic computing and is attracting great attention, mainly due to its great potential in many fields, such as image recognition, artificial intelligence and artificial visual perception systems. However, optical synapse with infrared (IR) response has rarely been reported. InAs nanowires (NWs) have a direct narrow bandgap and a large surface to volume ratio, making them a promising material for IR detection. Here, we demonstrate a near-infrared (NIR) (750 to 1550 nm) optical synapse for the first time based on a poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE))-coated InAs NW field-effect transistor (FET). The responsivity of the P(VDF-TrFE)-coated InAs NW FET reaches 839.3 A/W under 750 nm laser illumination, demonstrating the advantage of P(VDF-TrFE) coverage. The P(VDF-TrFE)-coated InAs NW device exhibits optical synaptic behaviors in response to NIR light pulses, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF) and a transformation from short-term plasticity (STP) to long-term plasticity (LTP). The working mechanism is attributed to the polarization effect in the ferroelectric P(VDF-TrFE) layer, which dominates the trapping and de-trapping characteristics of photogenerated holes. These findings have significant implications for the development of artificial neural networks.