Sensory Adaptation and Neuromorphic Phototransistors Based on CsPb(Br1-xIx)3 Perovskite and MoS2 Hybrid Structure

Highly improved photocurrent of a flexible MoS2 photodetector via a backside Al metal mirror and its in- and outward folding states

High-performance foldable and flexible photodetectors have been extensively studied for next-generation hinged electronics and AR/VR technology. The need to maintain efficiency in the folded state restricts the development of strategies aimed at further improving the efficiency of photodetectors. For the first time, we introduce a simple and effective method for the photocurrent improvement of a molybdenum disulfide (MoS2) flexible photodetector by attaching a backside Al metal mirror and folding it in- and outward. Under light illumination, the Al film underneath the MoS2 photodetector acts as a flat reflective metal mirror and further functions as a concave mirror under the inward folded state. In this state, the proposed device with the backside Al metal mirror improved photocurrent from 4.04 μA to 8.53 μA under illumination (a λ ex of 405 nm with a P inc of 0.3 mW cm-2) compared to the device on a flat polyimide (PI) substrate without the Al metal mirror. Our work provides new insights into high-performance flexible photodetectors in the inward and outward folded states, which will be beneficial for future foldable/hinged electronics and AR/VR devices.

Bidirectional Photovoltage-Driven Oxide Transistors for Neuromorphic Visual Sensors

Biological vision is one of the most important parts of the human perception system. However, emulating biological visuals is challenging because it requires complementary photoexcitation and photoinhibition. Here, the study presents a bidirectional photovoltage-driven neuromorphic visual sensor (BPNVS) that is constructed by monolithically integrating two perovskite solar cells (PSCs) with dual-gate ion-gel-gated oxide transistors. PSCs act as photoreceptors, converting external visual stimuli into electrical signals, whereas oxide transistors generate neuromorphic signal outputs that can be adjusted to produce positive and negative photoresponses. This device mimics the human vision system's ability to recognize colored and color-mixed patterns. The device achieves a static color recognition accuracy of 96% by utilizing the reservoir computing system for feature extraction. The BPNVS mem-reservoir chip is also proposed for handing object movement and dynamic color recognition. This work is a significant step forward in neuromorphic sensing and complex pattern recognition.

Low-Cost, High-Efficiency Aluminum Zinc Oxide Synaptic Transistors: Blue LED Stimulation for Enhanced Neuromorphic Computing Applications

Neuromorphic devices are electronic devices that mimic the information processing methods of neurons and synapses, enabling them to perform multiple tasks simultaneously with low power consumption and exhibit learning ability. However, their large-scale production and efficient operation remain a challenge. Herein, we fabricated an aluminum-doped zinc oxide (AZO) synaptic transistor via solution-based spin-coating. The transistor is characterized by low production costs and high performance. It demonstrates high responsiveness under UV laser illumination. In addition, it exhibits effective synaptic behaviors under blue LED illumination, indicating high-efficiency operation. The paired-pulse facilitation (PPF) index measured from optical stimulus modulation was 179.6%, indicating strong synaptic connectivity and effective neural communication and processing. Furthermore, by modulating the blue LED light pulse frequency, an excitatory postsynaptic current gain of 4.3 was achieved, demonstrating efficient neuromorphic functionality. This study shows that AZO synaptic transistors are promising candidates for artificial synaptic devices.

Electronically Reconfigurable Memristive Neuron Capable of Operating in Both Excitation and Inhibition Modes

Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality.

Retinomorphic Color Perception Based on Opponent Process Enabled by Perovskite Bipolar Photodetectors

The ability to perceive color by the retina can be attributed to both its trichromatic photoreceptors and the antagonistic neural wiring known as the opponent process. While neuromorphic sensors have been shown to demonstrate memory and adaptation capabilities, color perception is still challenging due to the intrinsic lack of spectral selectivity in narrow bandgap semiconductors. Furthermore, research on emulating neural wiring is severely lacking. The combination of halide perovskite materials with a tunable bandgap and a novel bipolar photodetector design emulates the efficiency of the retina in processing color information. The stimuli-responsive material is also responsible for maintaining partial color constancy-an adaptation feature. Leveraging the unique enhancement of color contrasts, an in-sensor data compression and edge detection can also be demonstrated. The color perception, chromatic adaptation, and color contrast enhancement make perovskite bipolar photodetectors a unique example where the sensor and neural wiring can be co-developed in conjunction.

Recent advances in artificial neuromorphic applications based on perovskite composites

High-performance perovskite materials with excellent physical, electronic, and optical properties play a significant role in artificial neuromorphic devices. However, the development of perovskites in microelectronics is inevitably hindered by their intrinsic non-ideal properties, such as high defect density, environmental sensitivity, and toxicity. By leveraging materials engineering, integrating various materials with perovskites to leverage their mutual strengths presents great potential to enhance ion migration, energy level alignment, photoresponsivity, and surface passivation, thereby advancing optoelectronic and neuromorphic device development. This review initially provides an overview of perovskite materials across different dimensions, highlighting their physical properties and detailing their applications and metrics in two- and three-terminal devices. Subsequently, we comprehensively summarize the application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and crystalline porous materials (CPMs), to develop advanced devices such as memristors, transistors, photodetectors, sensors, light-emitting diodes (LEDs), and artificial neuromorphic systems. Lastly, we outline the challenges and future research directions in synthesizing perovskite composites for neuromorphic devices. Through the review and analysis, we aim to broaden the utilization of perovskites and their composites in neuromorphic research, offering new insights and approaches for grasping the intricate physical working mechanisms and functionalities of perovskites.

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

Bionic Scotopic Adaptation Transistors for Nighttime Low Illumination Imaging

Human vision excels in perceiving nighttime low illumination due to biological feedforward adaptation. Replicating this ability in biomimetic vision using solid-state devices has been highly sought after. However, emulating scotopic adaptation, entailing a confluence of efficient photoexcitation and dynamic carrier modulation, presents formidable challenges. Here, we demonstrate a low-power and bionic scotopic adaptation transistor by coupling a light-absorption layer and an electron-trapping layer at the bottom of the semiconducting channel, enabling simultaneous achievement of efficient generation of free photocarriers and adaptive carrier accumulation within a single device. This innovation empowers our transistor to exhibit sensitivity-potentiated characteristics after adaptation, detecting scotopic-level illumination (0.001 lx) with exceptional photosensitivity up to 103 at low voltages below 2 V. Moreover, we have successfully replicated diverse scotopic vision functions, encompassing time-dependent visual threshold enhancement, light intensity-dependent adaptation index, imaging contrast enhancement for nighttime low illumination imaging, opening an opportunity for artificial night vision.

Engineering Graphene Phototransistors for High Dynamic Range Applications

Phototransistors are light-sensitive devices featuring a high dynamic range, low-light detection, and mechanisms to adapt to different ambient light conditions. These features are of interest for bioinspired applications such as artificial and restored vision. In this work, we report on a graphene-based phototransistor exploiting the photogating effect that features picowatt- to microwatt-level photodetection, a dynamic range covering six orders of magnitude from 7 to 107 lux, and a responsivity of up to 4.7 × 103 A/W. The proposed device offers the highest dynamic range and lowest optical power detected compared to the state of the art in interfacial photogating and further operates air stably. These results have been achieved by a combination of multiple developments. For example, by optimizing the geometry of our devices with respect to the graphene channel aspect ratio and by introducing a semitransparent top-gate electrode, we report a factor 20-30 improvement in responsivity over unoptimized reference devices. Furthermore, we use a built-in dynamic range compression based on a partial logarithmic optical power dependence in combination with control of responsivity. These features enable adaptation to changing lighting conditions and support high dynamic range operation, similar to what is known in human visual perception. The enhanced performance of our devices therefore holds potential for bioinspired applications, such as retinal implants.

Strain-insensitive viscoelastic perovskite film for intrinsically stretchable neuromorphic vision-adaptive transistors

Stretchable neuromorphic optoelectronics present tantalizing opportunities for intelligent vision applications that necessitate high spatial resolution and multimodal interaction. Existing neuromorphic devices are either stretchable but not reconcilable with multifunctionality, or discrete but with low-end neurological function and limited flexibility. Herein, we propose a defect-tunable viscoelastic perovskite film that is assembled into strain-insensitive quasi-continuous microsphere morphologies for intrinsically stretchable neuromorphic vision-adaptive transistors. The resulting device achieves trichromatic photoadaptation and a rapid adaptive speed (<150 s) beyond human eyes (3 ~ 30 min) even under 100% mechanical strain. When acted as an artificial synapse, the device can operate at an ultra-low energy consumption (15 aJ) (far below the human brain of 1 ~ 10 fJ) with a high paired-pulse facilitation index of 270% (one of the best figures of merit in stretchable synaptic phototransistors). Furthermore, adaptive optical imaging is achieved by the strain-insensitive perovskite films, accelerating the implementation of next-generation neuromorphic vision systems.

Photocurrent Enhancement by Copper Incorporation in Chemical-Solution-Synthesized Inorganic Lead Perovskite Thin Films

Perovskite thin films are at the forefront of highly promising photovoltaic technologies due to their remarkable optoelectronic properties. Herein, we explore a low-cost, reproducible, and industry-scalable methodology to synthesize an all-inorganic CsPbI1.5Br1.5 perovskite thin film with additional incorporation of copper and chloride ions into the lattice structure. The synthesis process involves chemical bath deposition of PbS, followed by a gas-solid iodination reaction to yield PbI2. Subsequently, dip-coating incorporates Cs+, Cu2+, Br-, and Cl- ions into PbI2, and annealing at 270 °C produces perovskite thin films. The results show a large coverage area and a uniform thickness of each perovskite thin film. Comprehensive characterization, including X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, scanning electron microscopy, and photoluminescence, provides the structural, chemical, and optical properties of the synthesized thin films. To evaluate the practical implications of our methodology, we fabricated photodetectors employing CsPbI1.5Br1.5 and (Cs0.95:Cu0.01)PbI1.5Br1.3Cl0.1 perovskite films. A comparative analysis unequivocally demonstrates a significant increase in photodetector performance when utilizing (Cs0.95:Cu0.01)PbI1.5Br1.3Cl0.1 perovskite films. While our findings quantitatively assess the tangible enhancement in photocurrent, we acknowledge the potential for improvement in device fabrication to enhance the overall performance. This study not only affirms the successful low-cost synthesis of perovskite thin films but also emphasizes the pivotal role of Cu2+ and Cl- ions in enhancing the performance of perovskite-based optoelectronic devices.

Biomimetic nanocluster photoreceptors for adaptative circular polarization vision

Nanoclusters with atomically precise structures and discrete energy levels are considered as nanoscale semiconductors for artificial intelligence. However, nanocluster electronic engineering and optoelectronic behavior have remained obscure and unexplored. Hence, we create nanocluster photoreceptors inspired by mantis shrimp visual systems to satisfy the needs of compact but multi-task vision hardware and explore the photo-induced electronic transport. Wafer-scale arrayed photoreceptors are constructed by a nanocluster-conjugated molecule heterostructure. Nanoclusters perform as an in-sensor charge reservoir to tune the conductance levels of artificial photoreceptors by a light valve mechanism. A ligand-assisted charge transfer process takes place at nanocluster interface and it features an integration of spectral-dependent visual adaptation and circular polarization recognition. This approach is further employed for developing concisely structured, multi-task, and compact artificial visual systems and provides valuable guidelines for nanocluster neuromorphic devices.

Nanomaterial-Based Artificial Vision Systems: From Bioinspired Electronic Eyes to In-Sensor Processing Devices

High-performance robotic vision empowers mobile and humanoid robots to detect and identify their surrounding objects efficiently, which enables them to cooperate with humans and assist human activities. For error-free execution of these robots' tasks, efficient imaging and data processing capabilities are essential, even under diverse and complex environments. However, conventional technologies fall short of meeting the high-standard requirements of robotic vision under such circumstances. Here, we discuss recent progress in artificial vision systems with high-performance imaging and data processing capabilities enabled by distinctive electrical, optical, and mechanical characteristics of nanomaterials surpassing the limitations of traditional silicon technologies. In particular, we focus on nanomaterial-based electronic eyes and in-sensor processing devices inspired by biological eyes and animal visual recognition systems, respectively. We provide perspectives on key nanomaterials, device components, and their functionalities, as well as explain the remaining challenges and future prospects of the artificial vision systems.

Optical Enhancement of Indirect Bandgap 2D Transition Metal Dichalcogenides for Multi-Functional Optoelectronic Sensors

The unique electrical and optical properties of transition metal dichalcogenides (TMDs) make them attractive nanomaterials for optoelectronic applications, especially optical sensors. However, the optical characteristics of these materials are dependent on the number of layers. Monolayer TMDs have a direct bandgap that provides higher photoresponsivity compared to multilayer TMDs with an indirect bandgap. Nevertheless, multilayer TMDs are more appropriate for various photodetection applications due to their high carrier density, broad spectral response from UV to near-infrared, and ease of large-scale synthesis. Therefore, this review focuses on the modification of the optical properties of devices based on indirect bandgap TMDs and their emerging applications. Several successful developments in optical devices are examined, including band structure engineering, device structure optimization, and heterostructures. Furthermore, it introduces cutting-edge techniques and future directions for optoelectronic devices based on multilayer TMDs.

Self-powered and broadband opto-sensor with bionic visual adaptation function based on multilayer γ-InSe flakes

Visual adaptation that can autonomously adjust the response to light stimuli is a basic function of artificial visual systems for intelligent bionic robots. To improve efficiency and reduce complexity, artificial visual systems with integrated visual adaptation functions based on a single device should be developed to replace traditional approaches that require complex circuitry and algorithms. Here, we have developed a single two-terminal opto-sensor based on multilayer γ-InSe flakes, which successfully emulated the visual adaptation behaviors with a new working mechanism combining the photo-pyroelectric and photo-thermoelectric effect. The device can operate in self-powered mode and exhibit good human-eye-like adaptation behaviors, which include broadband light-sensing image adaptation (from ultraviolet to near-infrared), near-complete photosensitivity recovery (99.6%), and synergetic visual adaptation, encouraging the advancement of intelligent opto-sensors and machine vision systems.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting

Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS₂) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.

Active pixel sensor matrix based on monolayer MoS2 phototransistor array

In-sensor processing, which can reduce the energy and hardware burden for many machine vision applications, is currently lacking in state-of-the-art active pixel sensor (APS) technology. Photosensitive and semiconducting two-dimensional (2D) materials can bridge this technology gap by integrating image capture (sense) and image processing (compute) capabilities in a single device. Here, we introduce a 2D APS technology based on a monolayer MoS₂ phototransistor array, where each pixel uses a single programmable phototransistor, leading to a substantial reduction in footprint (900 pixels in ∼0.09 cm²) and energy consumption (100s of fJ per pixel). By exploiting gate-tunable persistent photoconductivity, we achieve a responsivity of ∼3.6 × 10⁷ A W^-1, specific detectivity of ∼5.6 × 10¹³ Jones, spectral uniformity, a high dynamic range of ∼80 dB and in-sensor de-noising capabilities. Further, we demonstrate near-ideal yield and uniformity in photoresponse across the 2D APS array.

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

An Irradiance-Adaptable Near-Infrared Vertical Heterojunction Phototransistor

Bioinspired artificial visual perception devices with the optical environment-adaptable function have attracted significant attention for their promising potential in applications like robotics and machine vision. In this regard, a photodetector with in-sensor adaptability is longed for in terms of complexity, efficiency, and cost. Here, a near-infrared phototransistor with a benign light irradiance-adaptability is presented. The phototransistor uses a vertically stacking graphene/lead sulfide quantum dots/graphene heterojunction as the conductive channel. Compared with ordinary lead sulfide quantum dots-decorated graphene phototransistors, the present device demonstrates a faster photoresponse speed and an abnormal transfer characteristic. The latter characteristic is induced by the gate voltage-tunable Fermi level in the heterojunction and the abundant electron trap states in the quantum dot film, which jointly results in an intense dependence of the photoresponse on the gate voltage. The dynamic trapping and de-trapping processes in the quantum dot film enable the inhibition or potentiation of the photoresponse, based on which the photopic or scotopic adaptation behavior of the human retina is successfully mimicked, respectively. By providing an irradiance-adaptable photodetector with a spectral response beyond visible light, this work should inspire future research on artificial environment-adaptable perception devices.

Multiwavelength Optoelectronic Synapse with 2D Materials for Mixed-Color Pattern Recognition

Neuromorphic visual systems emulating biological retina functionalities have enormous potential for in-sensor computing, with prospects of making artificial intelligence ubiquitous. Conventionally, visual information is captured by an image sensor, stored by memory units, and eventually processed by the machine learning algorithm. Here, we present an optoelectronic synapse device with multifunctional integration of all the processes required for real time object identification. Ultraviolet-visible wavelength-sensitive MoS₂ FET channel with infrared sensitive PtTe₂/Si gate electrode enables the device to sense, store, and process optical data for a wide range of the electromagnetic spectrum, while maintaining a low dark current. The device exhibits optical stimulation-controlled short-term and long-term potentiation, electrically driven long-term depression, synaptic weight update for multiple wavelengths of light ranging from 300 nm in ultraviolet to 2 μm in infrared. An artificial neural network developed using the extracted weight update parameters of the device can be trained to identify both single wavelength and mixed wavelength patterns. This work demonstrates a device that could potentially be used for realizing a multiwavelength neuromorphic visual system for pattern recognition and object identification.

Retina-Inspired Self-Powered Artificial Optoelectronic Synapses with Selective Detection in Organic Asymmetric Heterojunctions

The retina, the most crucial unit of the human visual perception system, combines sensing with wavelength selectivity and signal preprocessing. Incorporating energy conversion into these superior neurobiological features to generate core visual signals directly from incoming light under various conditions is essential for artificial optoelectronic synapses to emulate biological processing in the real retina. Herein, self-powered optoelectronic synapses that can selectively detect and preprocess the ultraviolet (UV) light are presented, which benefit from high-quality organic asymmetric heterojunctions with ultrathin molecular semiconducting crystalline films, intrinsic heterogeneous interfaces, and typical photovoltaic properties. These devices exhibit diverse synaptic behaviors, such as excitatory postsynaptic current, paired-pulse facilitation, and high-pass filtering characteristics, which successfully reproduce the unique connectivity among sensory neurons. These zero-power optical-sensing synaptic operations further facilitate a demonstration of image sharpening. Additionally, the charge transfer at the heterojunction interface can be modulated by tuning the gate voltage to achieve multispectral sensing ranging from the UV to near-infrared region. Therefore, this work sheds new light on more advanced retinomorphic visual systems in the post-Moore era.

Reversible Photochromism in ⟨110⟩ Oriented Layered Halide Perovskite

Extending halide perovskites' optoelectronic properties to stimuli-responsive chromism enables switchable optoelectronics, information display, and smart window applications. Here, we demonstrate a band gap tunability (chromism) via crystal structure transformation from three-dimensional FAPbBr₃ to a ⟨110⟩ oriented FA_n+2Pb_nBr_3n+2 structure using a mono-halide/cation composition (FA/Pb) tuning. Furthermore, we illustrate reversible photochromism in halide perovskite by modulating the intermediate n phase in the FA_n+2Pb_nBr_3n+2 structure, enabling greater control of the optical band gap and luminescence of a ⟨110⟩ oriented mono-halide/cation perovskite. Proton transfer reaction-mass spectroscopy carried out to precisely quantify the decomposition product reveals that the organic solvent in the film is a key contributor to the structural transformation and, therefore, the chromism in the ⟨110⟩ structure. These intermediate n phases (2 ≤ n ≤ ∞) stabilize in metastable states in the FA_n+2Pb_nBr_3n+2 system, which is accessible via strain or optical or thermal input. The structure reversibility in the ⟨110⟩ perovskite allowed us to demonstrate a class of photochromic sensors capable of self-adaptation to lighting.

On the optical anisotropy in 2D metal-halide perovskites

Two-dimensional metal-halide perovskites (MHPs) are versatile solution-processed organic/inorganic quantum wells where the structural anisotropy creates profound anisotropy in their electronic and excitonic properties and associated optical constants. We here employ a wholistic framework, based on semiempirical modeling (k·p/effective mass theory calculations) informed by hybrid density functional theory (DFT) and multimodal spectroscopic ellipsometry on (C₆H₅(CH₂)₂NH₃)₂PbI₄ films and crystals, that allows us to link the observed optical properties and anisotropy precisely to the underlying physical parameters that shape the electronic structure of a layered MHP. We find substantial frequency-dependent anisotropy in the optical constants and close correspondence between experiment and theory, demonstrating a high degree of in-plane alignment of the two-dimensional planes in both spin-coated thin films and cleaved single crystals made in this study. Hybrid DFT results elucidate the degree to which organic and inorganic frontier orbitals contribute to optical transitions polarized along a particular axis. The combined experimental and theoretical approach enables us to estimate the fundamental electronic bandgap of 2.65-2.68 eV in this prototypical 2D perovskite and to determine the spin-orbit coupling (Δ_SO = 1.20 eV) and effective crystal field (δ = -1.36 eV) which break the degeneracy of the frontier conduction band states and determine the exciton fine structure. The methods and results described here afford a better understanding of the connection between structure and induced optical anisotropy in quantum-confined MHPs, an important structure-property relationship for optoelectronic applications and devices.

Advances in materials and devices for mimicking sensory adaptation

Adaptive devices, which aim to adjust electrical behaviors autonomically to external stimuli, are considered to be attractive candidates for next-generation artificial perception systems. Compared with typical electronic devices with stable signal output, adaptive devices possess unique features in exhibiting dynamic fitness to varying environments. To meet this requirement, increasing efforts have been made focusing on developing new materials, functional interfaces and novel device geometry for sensory perception applications. In this review, we summarize the recent advances in materials and devices for mimicking sensory adaptation. Keeping this in mind, we first introduce the fundamentals of biological sensory adaptation. Thereafter, the recent progress in mimicking sensory adaptation, such as tactile and visual adaptive systems, is overviewed. Moreover, we suggest five strategies to construct adaptive devices. Finally, challenges and perspectives are proposed to highlight the directions that deserve focused attention in this flourishing field.

Solution-Processed Perovskite Field-Effect Transistor Artificial Synapses

Metal halide perovskites are distinctive semiconductors that exhibit both ionic and electronic transport and are promising for artificial synapses. However, developing a 3-terminal transistor artificial synapse with the perovskite channel remains elusive due to the lack of a proper technique to regulate mobile ions in a non-volatile manner. Here, a solution-processed perovskite transistor is reported for artificial synapses through the implementation of a ferroelectric gate. The ferroelectric polarization provides a non-volatile electric field on the perovskite, leading to fixation of the mobile ions and hence modulation of the electronic conductance of the channel. Multi-state channel conductance is realized by partial ferroelectric polarization. The ferroelectric-gated perovskite transistor is successfully used as an artificial synapse that emulates basic synaptic functions such as long-term plasticity with excellent linearity, short-term as well as spike-timing-dependent plasticity. The strategy to regulate ion dynamics in the perovskites using the ferroelectric gate suggests a generic route to employ perovskites for synaptic electronics.

Hybrid Thin-Film Materials Combinations for Complementary Integration Circuit Implementation

Beyond conventional silicon, emerging semiconductor materials have been actively investigated for the development of integrated circuits (ICs). Considerable effort has been put into implementing complementary circuits using non-silicon emerging materials, such as organic semiconductors, carbon nanotubes, metal oxides, transition metal dichalcogenides, and perovskites. Whereas shortcomings of each candidate semiconductor limit the development of complementary ICs, an approach of hybrid materials is considered as a new solution to the complementary integration process. This article revisits recent advances in hybrid-material combination-based complementary circuits. This review summarizes the strong and weak points of the respective candidates, focusing on their complementary circuit integrations. We also discuss the opportunities and challenges presented by the prospect of hybrid integration.

Perovskite photodetectors and their application in artificial photonic synapses

Organic-inorganic hybrid perovskites exhibit superior optoelectrical properties and have been widely used in photodetectors. Perovskite photodetectors with excellent detectivity have great potential for developing artificial photonic synapses which can merge data transmission and storage. They are highly desired for next generation neuromorphic computing. The recent progress of perovskite photodetectors and their application in artificial photonic synapses are summarized in this review. Firstly, the key performance parameters of photodetectors are briefly introduced. Secondly, the recent research progress of photodetectors including photoconductors, photodiodes, and phototransistors is summarized. Finally, the applications of perovskite photodetectors in artificial photonic synapses in recent years are highlighted. All these demonstrate the great potential of perovskite photonic synapses for the development of artificial intelligence.

Mixed-Halide Perovskite Film-Based Neuromorphic Phototransistors for Mimicking Experience-History-Dependent Sensory Adaptation

Sensory adaptation is an essential function for humans to live on the earth. Herein, a hybrid synaptic phototransistor based on the mixed-halide perovskite/organic semiconductor film is reported. This hybrid phototransistor achieves photosensitive performance including a high photoresponsivity over 4 × 10³ A/W and an excellent specific detectivity of 2.8 × 10¹⁶ Jones. Due to the photoinduced halide-ion segregation of the mixed-halide perovskites and their slow recovery properties, the experience-history-dependent sensory adaptation behavior can be mimicked. Moreover, the light pulse width, intensity, light wavelength, and gate bias can be used to regulate the adaptation processes to improve its adaptability and perceptibility in different environments. The CsPbBr_xI_3-x/organic semiconductor hybrid films produced by spin coating are beneficial to large-scale fabrication. This study fabricates a novel solution-processable light-stimulated synapse based on inorganic perovskites for mimicking the human sensory adaptation that makes it possible to approach artificial neural sensory systems.

Neuromorphic Active Pixel Image Sensor Array for Visual 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.

Artificial Synapses Based on Lead-Free Perovskite Floating-Gate Organic Field-Effect Transistors for Supervised and Unsupervised Learning

Synaptic devices are expected to overcome von Neumann's bottleneck and served as one of the foundations for future neuromorphic computing. Lead halide perovskites are considered as promising photoactive materials but limited by the toxicity of lead. Herein, lead-free perovskite CsBi₃I₁₀ is utilized as a photoactive material to fabricate organic synaptic transistors with a floating-gate structure for the first time. The devices can maintain the I_light/I_dark ratio of 10³ for 4 h and have excellent stability within the 30 days test even without encapsulation. Synaptic functions are successfully simulated. Notably, by combining the decent charge transport property of the organic semiconductor and the excellent photoelectronic property of CsBi₃I₁₀, synaptic performance can be realized even with an operating voltage as low as -0.01 V, which is rare among floating-gate synaptic transistors. Furthermore, artificial neural networks are constructed. We propose a new method that can simulate the synaptic weight value in multiple digit form to achieve complete gradient descent. The image recognition test exhibits thrilling recognition accuracy for both supervised (91%) and unsupervised (81%) classifications. These results demonstrate the great potential of floating-gate organic synaptic transistors in neuromorphic computing.

Conception of a Smart Artificial Retina Based on a Dual-Mode Organic Sensing Inverter

The human visual system enables perceiving, learning, remembering, and recognizing elementary visual information (light, colors, and images), which has inspired the development of biomimicry visual system-based electronic devices. Photosensing and synaptic devices are integrated into these systems to realize elementary information storage and recognition to imitate image processing. However, the severe restrictions of the monotonic light response and complicated circuitry design remain challenges for the development of artificial visual devices. Here, the concept of a smart artificial retina based on an organic optical sensing inverter device that can be operated as a multiwavelength photodetector and recorder is reported first. The device exhibits a light-triggered broadband (red/green/blue) response, a low energy consumption as low as ±5 V, and an ultrafast response speed (<300 ms). Moreover, the multifunctional component is also combined within a single cell for health monitoring of the artificial retina during light surveillance to avoid retinopathy. Proof-of-concept devices, by simplifying the circuitry and providing dual-mode functions, can contribute significantly to the development of bionics design and broaden the horizon for smart artificial retinas in the human visual system.

Neuromorphic Devices for Bionic Sensing and Perception

Neuromorphic devices that can emulate the bionic sensory and perceptual functions of neural systems have great applications in personal healthcare monitoring, neuro-prosthetics, and human-machine interfaces. In order to realize bionic sensing and perception, it's crucial to prepare neuromorphic devices with the function of perceiving environment in real-time. Up to now, lots of efforts have been made in the incorporation of the bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. In this review, we first introduce neuromorphic devices based on diverse materials and mechanisms. Then we summarize the progress made in the emulation of biological sensing and perception systems. Finally, the challenges and opportunities in these fields are also discussed.

Highly sensitive active pixel image sensor array driven by large-area bilayer MoS2 transistor circuitry

Various large-area growth methods for two-dimensional transition metal dichalcogenides have been developed recently for future electronic and photonic applications. However, they have not yet been employed for synthesizing active pixel image sensors. Here, we report on an active pixel image sensor array with a bilayer MoS2 film prepared via a two-step large-area growth method. The active pixel of image sensor is composed of 2D MoS2 switching transistors and 2D MoS2 phototransistors. The maximum photoresponsivity (Rph) of the bilayer MoS2 phototransistors in an 8 × 8 active pixel image sensor array is statistically measured as high as 119.16 A W-1. With the aid of computational modeling, we find that the main mechanism for the high Rph of the bilayer MoS2 phototransistor is a photo-gating effect by the holes trapped at subgap states. The image-sensing characteristics of the bilayer MoS2 active pixel image sensor array are successfully investigated using light stencil projection.

Metal Halide Perovskite/2D Material Heterostructures: Syntheses and Applications

The past decade has witnessed the great success achieved by metal halide perovskites (MHPs) in photovoltaic and related fields. However, challenges still remain in further improving their performance, as well as, settling the stability issue for future commercialization. Recently, MHP/2D material heterostructures that combining MHPs with the low-cost and solution-processable 2D materials have demonstrated unprecedented improvement in both performance and stability due to the distinctive features at hetero-interface. The diverse fabrication techniques of MHPs and 2D materials allow them to be assembled as heterostructures with different configurations in a variety of ways. Moreover, the large families of MHPs and 2D materials provide the opportunity for the rational design and modification on compositions and functionalities of MHP/2D materials heterostructures. Herein, a comprehensive review of MHP/2D material heterostructures from syntheses to applications is presented. First, various fabrication techniques for MHP/2D material heterostructures are introduced by classifying them into solid-state methods and solution-processed methods. Then the applications of MHP/2D heterostructures in various fields including photodetectors, solar cells, and photocatalysis are summarized in detail. Finally, current challenges for the development of MHP/2D material heterostructures are highlighted, and future opportunities for the advancements in this research field are also provided.

Recent Advances in Electrical Doping of 2D Semiconductor Materials: Methods, Analyses, and Applications

Two-dimensional materials have garnered interest from the perspectives of physics, materials, and applied electronics owing to their outstanding physical and chemical properties. Advances in exfoliation and synthesis technologies have enabled preparation and electrical characterization of various atomically thin films of semiconductor transition metal dichalcogenides (TMDs). Their two-dimensional structures and electromagnetic spectra coupled to bandgaps in the visible region indicate their suitability for digital electronics and optoelectronics. To further expand the potential applications of these two-dimensional semiconductor materials, technologies capable of precisely controlling the electrical properties of the material are essential. Doping has been traditionally used to effectively change the electrical and electronic properties of materials through relatively simple processes. To change the electrical properties, substances that can donate or remove electrons are added. Doping of atomically thin two-dimensional semiconductor materials is similar to that used for silicon but has a slightly different mechanism. Three main methods with different characteristics and slightly different principles are generally used. This review presents an overview of various advanced doping techniques based on the substitutional, chemical, and charge transfer molecular doping strategies of graphene and TMDs, which are the representative 2D semiconductor materials.

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

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.

Bioinspired Photoresponsive Single Transistor Neuron for a Neuromorphic Visual System

Realizing a neuromorphic-based artificial visual system with low-cost hardware requires a neuromorphic device that can react to light stimuli. This study introduces a photoresponsive neuron device composed of a single transistor, developed by engineering an artificial neuron that responds to light, just like retinal neurons. Neuron firing is activated primarily by electrical stimuli such as current via a well-known single transistor latch phenomenon. Its firing characteristics, represented by spiking frequency and amplitude, are additionally modulated by optical stimuli such as photons. When light is illuminated onto the neuron transistor, electron-hole pairs are generated, and they allow the neuron transistor to fire at lower firing threshold voltage. Different photoresponsive properties can be modulated by the intensity and wavelength of the light, analogous to the behavior of retinal neurons. The artificial visual system can be miniaturized because a photoresponsive neuronal function is realized without bulky components such as image sensors and extra circuits.