Halide perovskite photoelectric artificial synapses: materials, devices, and applications

High-Linearity Ta2O5 Memristor and Its Application in Gaussian Convolution Image Denoising

In the image Gaussian filtering process, convolving with a Gaussian matrix is essential due to the numerous arithmetic computations involved, predominantly multiplications and additions. This can heavily tax the system's memory, particularly with frequent use. To address this issue, a W/Ta2O5/Ag memristor was employed to substantially mitigate the computational overhead associated with convolution operations. Additionally, an interlayer of ZnO was subsequently introduced into the memristor. The resulting Ta2O5/ZnO heterostructure layer exhibited improved linearity in the pulse response, which enhanced linearity facilitates easy adjustment of the conductance magnitude through a linear mapping of the number of pulses and the conductance. Subsequently, the conductance of the W/Ta2O5/ZnO/Ag bilayer memristor was employed as the weights for the convolution kernel in convolution operations. Gaussian noise removal in image processing was achieved by assembling a 5 × 5 memristor array as the kernel. When denoising was performed using memristor arrays, compared to denoising achieved through Gaussian matrix convolution, an average loss of less than 5% was observed. The provided memristors demonstrate significant potential in convolutional computations, particularly for subsequent applications in convolutional neural networks (CNNs).

Thermally and Mechanically Stable Perovskite Artificial Synapse as Tuned by Phase Engineering for Efferent Neuromuscular Control

The doping of perovskites with mixed cations and mixed halides is an effective strategy to optimize phase stability. In this study, we introduce a cubic black phase perovskite CsyFA(1-y)Pb(BrxI(1-x))3 artificial synapse, using phase engineering by adjusting the cesium-bromide content. Low-bromine mixed perovskites are suitable to improve the electric pulse excitation sensitivity and stability of the device. Specifically, the low-bromine and low-cesium mixed perovskite (x = 0.15, y = 0.22) annealed at 373 K allows the device to maintain logic response even after 1000 mechanical flex/flat cycles. The device also shows good thermal stability up to temperatures of 333 K. We have demonstrated reflex-arc behavior with MCMHP synaptic units, capable of making sensory warnings at high frequency. This compositionally engineered, dual-mixed perovskite synaptic device provides significant potential for perceptual soft neurorobotic systems and prostheses.

Monolithic 2D Perovskites Enabled Artificial Photonic Synapses for Neuromorphic Vision Sensors

Neuromorphic visual sensors (NVS) based on photonic synapses hold a significant promise to emulate the human visual system. However, current photonic synapses rely on exquisite engineering of the complex heterogeneous interface to realize learning and memory functions, resulting in high fabrication cost, reduced reliability, high energy consumption and uncompact architecture, severely limiting the up-scaled manufacture, and on-chip integration. Here a photo-memory fundamental based on ion-exciton coupling is innovated to simplify synaptic structure and minimize energy consumption. Due to the intrinsic organic/inorganic interface within the crystal, the photodetector based on monolithic 2D perovskite exhibits a persistent photocurrent lasting about 90 s, enabling versatile synaptic functions. The electrical power consumption per synaptic event is estimated to be≈1.45 × 10-16 J, one order of magnitude lower than that in a natural biological system. Proof-of-concept image preprocessing using the neuromorphic vision sensors enabled by photonic synapse demonstrates 4 times enhancement of classification accuracy. Furthermore, getting rid of the artificial neural network, an expectation-based thresholding model is put forward to mimic the human visual system for facial recognition. This conceptual device unveils a new mechanism to simplify synaptic structure, promising the transformation of the NVS and fostering the emergence of next generation neural networks.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

Spacer Engineering Enables Fine-Tuned Thin Film Microstructure and Efficient Charge Transport for Ultrasensitive 2D Perovskite-Based Heterojunction Phototransistors and Optoelectronic Synapses

2D Ruddlesden-Popper phase layered perovskites (RPLPs) hold great promise for optoelectronic applications. In this study, a series of high-performance heterojunction phototransistors (HPTs) based on RPLPs with different organic spacer cations (namely butylammonium (BA+), cyclohexylammonium (CyHA+), phenethylammonium (PEA+), p-fluorophenylethylammonium (p-F-PEA+), and 2-thiophenethylammonium (2-ThEA+)) are fabricated successfully, in which high-mobility organic semiconductor 2,7-dioctyl[1]benzothieno[3,2-b]benzothiophene is adopted to form type II heterojunction channels with RPLPs. The 2-ThEA+-RPLP-based HPTs show the highest photosensitivity of 3.18 × 107 and the best detectivity of 9.00 × 1018 Jones, while the p-F-PEA+-RPLP-based ones exhibit the highest photoresponsivity of 5.51 × 106 A W-1 and external quantum efficiency of 1.32 × 109%, all of which are among the highest reported values to date. These heterojunction systems also mimicked several optically controllable fundamental characteristics of biological synapses, including excitatory postsynaptic current, paired-pulse facilitation, and the transition from short-term memory to long-term memory states. The device based on 2-ThEA+-RPLP film shows an ultra-high PPF index of 234%. Moreover, spacer engineering brought fine-tuned thin film microstructures and efficient charge transport/transfer, which contributes to the superior photodetection performance and synaptic functions of these RPLP-based HPTs. In-depth structure-property correlations between the organic spacer cations/RPLPs and thin film microstructure/device performance are systematically investigated.

Inorganic Halide Perovskite Nanowires/Conjugated Polymer Heterojunction-Based Optoelectronic Synaptic Transistors for Dynamic Machine Vision

Inspired by the retina, artificial optoelectronic synapses have groundbreaking potential for machine vision. The field-effect transistor is a crucial platform for optoelectronic synapses that is highly sensitive to external stimuli and can modulate conductivity. On the basis of the decent optical absorption, perovskite materials have been widely employed for constructing optoelectronic synaptic transistors. However, the reported optoelectronic synaptic transistors focus on the static processing of independent stimuli at different moments, while the natural visual information consists of temporal signals. Here, we report CsPbBrI2 nanowire-based optoelectronic synaptic transistors to study the dynamic responses of artificial synaptic transistors to time-varying visual information for the first time. Moreover, on the basis of the dynamic synaptic behavior, a hardware system with an accuracy of 85% is built to the trajectory of moving objects. This work offers a new way to develop artificial optoelectronic synapses for the construction of dynamic machine vision systems.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Geometric Shape Recognition with an Ultra-High Density Perovskite Nanowire Array-Based Artificial Vision System

Artificial vision systems (AVS) have potential applications in visual prosthetics and artificially intelligent robotics, and they require a preprocessor and a processor to mimic human vision. Halide perovskite (HP) is a promising preprocessor and processor due to its excellent photoresponse, ubiquitous charge migration pathways, and innate hysteresis. However, the material instability associated with HP thin films hinders their utilization in physical AVSs. Herein, we have developed ultrahigh-density arrays of robust HP nanowires (NWs) rooted in a porous alumina membrane (PAM) as the active layer for an AVS. The NW devices exhibit gradual photocurrent change, responding to changes in light pulse duration, intensity, and number, and allow contrast enhancement of visual inputs with a device lifetime of over 5 months. The NW-based processor possesses temporally stable conductance states with retention >105 s and jitter <10%. The physical AVS demonstrated 100% accuracy in recognizing different shapes, establishing HP as a reliable material for neuromorphic vision systems.