One-Step Preparation of Semiconductor/Dielectric Bilayer Structures for the Simulation of Flexible Bionic Photonic Synapses

Emerging Artificial Synaptic Devices Based on Organic Semiconductors: Molecular Design, Structure and Applications

In modern computing, the Von Neumann architecture faces challenges such as the memory bottleneck, hindering efficient processing of large datasets and concurrent programs. Neuromorphic computing, inspired by the brain's architecture, emerges as a promising alternative, offering unparalleled computational power while consuming less energy. Artificial synaptic devices play a crucial role in this paradigm shift. Various material systems, from organic to inorganic, have been explored for neuromorphic devices, with organic materials attracting attention for their excellent photoelectric properties, diverse material choices, and versatile preparation methods. Organic semiconductors, in particular, offer advantages over transition-metal dichalcogenides, including ease of preparation and flexibility, making them suitable for large-area organic films. This review focuses on emerging artificial synaptic devices based on organic semiconductors, discussing different branches within the organic semiconductor material system, various fabrication methods, device structure designs, and applications of organic artificial synapse. Critical considerations and challenges for achieving truly human-like dynamic perception in artificial systems based on organic semiconductors are also outlined, reflecting the ongoing evolution of neuromorphic computing.

Catalyst-Free Polymorphic β-Ga2O3 Nanomaterials for Solar-Blind Optoelectronic Devices: Applications in Imaging and Neural Communication

The continuous advancements in ultraviolet-C (UV-C) optoelectronics are poised to meet the growing demand for efficient and innovative optoelectronic devices, particularly in image sensing and neural communication. This study proposes a low-cost tube sealing and muffle calcination process for the catalyst-free synthesis of polymorphic β-Ga2O3 nanomaterials. These nanomaterials are synthesized via a vapor-solid (VS) growth mechanism, enabling the formation of high-quality nanowires (NWs), nanobelts (NBs), and nanosheets (NSs). UV-C photodetectors (PDs) fabricated with β-Ga2O3 nanobelts demonstrated exceptional performance, exhibiting a responsivity of 4.62 × 105 A W-1 and a specific detectivity of 4.78 × 1012 Jones under 254 nm light. This PD enabled high-sensitivity and high-contrast UV-C imaging, effectively capturing the letters "CNU" and a "Panda" pattern. Additionally, the β-Ga2O3 nanowire-based optoelectronic synapse (OES) device displayed efficient light sensing and significant persistent photoconductivity, accurately mimicking synaptic behaviors such as short-term to long-term memory transitions and memory reinforcement. The OES device is successfully integrated into a wireless optical communication system, effectively simulating neural signal transmission by outputting the current waveform signal of "CNU 1954" and exhibiting notable UV-C light sensing and learning abilities. This work not only introduces a method for synthesizing polymorphic β-Ga2O3 nanomaterials but also underscores their potential in advanced UV-C optoelectronic applications, including image sensing and neural communication.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Highly Sensitive, Low-Energy-Consumption Biomimetic Olfactory Synaptic Transistors Based on the Aggregation of the Semiconductor Films

Artificial olfactory synaptic devices with low energy consumption and low detection limits are important for the further development of neuromorphic computing and intelligent robotics. In this work, an ultralow energy consumption and low detection limit imitation olfactory synaptic device based on organic field-effect transistors (OFETs) was prepared. The aggregation state of poly(diketopyrrolopyrrole-selenophene) (PTDPP) semiconductor films is modulated by adding unfavorable solvents and annealing treatments to obtain excellent charge transfer and gas synaptic properties. The regulated OFET device can execute basic biological synaptic functions, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term to long-term plasticity, at an ultralow operating voltage of -0.0005 V. The ultralow energy consumption during the biomimetic simulation is in the range of 8.94-88 fJ per spike. Noteworthily, the gas detection limit of the device is as low as 50 ppb, well below normal human NO2 gas perception limits (100-1000 ppb). Additionally, high-pass filtering, Pavlovian conditioned reflexes, and decoding of "Morse code" were simulated. Finally, a grid-free conformal device with outstanding flexibility and stability was fabricated. In conclusion, the control of semiconductor thin-film aggregation provides effective guidance for preparing low-energy-consumption, highly sensitive olfactory nerve-mimicking devices and promoting the development of wearable electronics.

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.