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

Chaperone solvent-assisted assembly of polymers at the interface of two immiscible liquids

The assembly of polymers at liquid-liquid interfaces offers a promising strategy for fabricating two-dimensional polymer films. However, a significant challenge arises when the polymers lack inherent interfacial traction. In response, we introduce an approach termed chaperone solvent-assisted assembly. This approach utilizes a target polymer, X, along with three solvents: α, β, and γ. α and β are poor solvents for X and immiscible with each other, while γ is a good solvent for X and miscible with both α and β, thus serving as the chaperone solvent. The cross-interface diffusion of γ induces the assembly of interfacially nonactive X at the α-β interface, and this mechanism is verified through systematic in situ and ex situ studies. We show that chaperone solvent-assisted assembly is versatile and reliable for the interfacial assembly of polymers, including those that are interfacially nonactive. Several practical applications based on chaperone solvent-assisted assembly are also demonstrated.

Ultralow-Energy-Consumption Photosynaptic Transistor Utilizing Conjugated Polymers/Perovskite Quantum Dots Nanocomposites With Ligand Density Optimization

The photosynaptic transistor stands as a promising contender for overcoming the von Neumann bottleneck in the realm of photo-communication. In this context, photonic synaptic transistors is developed through a straightforward solution process, employing an organic semiconducting polymer with pendant-naphthalene-containing side chains (PDPPNA) in combination with ligand-density-engineered CsPbBr3 perovskite quantum dots (PQDs). This fabrication approach allows the devices to emulate fundamental synaptic behaviors, encompassing excitatory postsynaptic current, paired-pulse facilitation, the transition from short-to-long-term memory, and the concept of "learning experience." Notably, the phototransistor, incorporating the blend of the PDPPNA and CsPbBr3 PQDs washed with ethyl acetate, achieved an exceptional memory ratio of 104. Simultaneously, the same device exhibited an impressive paired-pulse facilitation ratio of 223% at a moderate operating voltage of -4 V and an extraordinarily low energy consumption of 0.215 aJ at an ultralow operating voltage of -0.1 mV. Consequently, these low-voltage synaptic devices, constructed with a pendant side-chain engineering of organic semiconductors and a ligand density engineering of PQDs through a simple fabrication process, exhibit substantial potential for replicating the visual memory capabilities of the human brain.

Gate-Tunable Positive and Negative Photoconductance in Near-Infrared Organic Heterostructures for In-Sensor Computing

The rapid growth of sensor data in the artificial intelligence often causes significant reductions in processing speed and power efficiency. Addressing this challenge, in-sensor computing is introduced as an advanced sensor architecture that simultaneously senses, memorizes, and processes images at the sensor level. However, this is rarely reported for organic semiconductors that possess inherent flexibility and tunable bandgap. Herein, an organic heterostructure that exhibits a robust photoresponse to near-infrared (NIR) light is introduced, making it ideal for in-sensor computing applications. This heterostructure, consisting of partially overlapping p-type and n-type organic thin films, is compatible with conventional photolithography techniques, allowing for high integration density of up to 520 devices cm-2 with a 5 µm channel length. Importantly, by modulating gate voltage, both positive and negative photoresponses to NIR light (1050 nm) are attained, which establishes a linear correlation between responsivity and gate voltage and consequently enables real-time matrix multiplication within the sensor. As a result, this organic heterostructure facilitates efficient and precise NIR in-sensor computing, including image processing and nondestructive reading and classification, achieving a recognition accuracy of 97.06%. This work serves as a foundation for the development of reconfigurable and multifunctional NIR neuromorphic vision systems.

Reversible Molecular Conformation Transitions of Smectic Liquid Crystals for Light/Bias-Gated Transistor Memory

In recent years, organic photonic field-effect transistors have made remarkable progress with the rapid development of conjugated polycrystalline materials. Liquid crystals, with their smooth surface, defined layer thickness, and crystalline structures, are commonly used for these advantages. In this work, a series of smectic liquid crystalline molecules, 2,9-didecyl-dinaphtho-thienothiophene (C10-DNTT), 2,7-didecyl-benzothieno-benzothiopene (C10-BTBT), 3,9-didecyl-dinaphtho-thiophene (C10-DNT), and didecyl-sexithiophene (C10-6T), have been used in photonic transistor memory, functioning as both hole-transport channels and electron traps to investigate systematically the reasons and mechanisms behind the memory behavior of smectic liquid crystals. After thermal annealing, C10-BTBT and C10-6T/C10-DNTT are homeotropically aligned from the smectic A and smectic X phases, respectively. The 3D-ordered structure of these smectic-aligned crystals contributed to efficient photowriting and electrical erasing processes. Among them, the device performance of C10-BTBT was particularly significant, with a memory window of 21 V. The memory ratio could reach 1.5 × 106 and maintain a memory ratio of over 3 orders after 10,000 s, contributing to its smectic A structure. Through the research, we confirmed the memory and light/bias-gated behaviors of these smectic liquid crystalline molecules, attributing them to reversible molecular conformation transitions and the inherent structural inhomogeneity inside the polycrystalline channel layer.

Nanoparticles of Push-Pull Triphenylamine-Based Molecules for Light-Controlled Stimulation of Neuronal Activity

Organic semiconductor materials with a unique set of properties are very attractive for interfacing biological objects and can be used for noninvasive therapy or detection of biological signals. Here, we describe the synthesis and investigation of a novel series of organic push-pull conjugated molecules with the star-shaped architecture, consisting of triphenylamine as a branching electron donor core linked through the thiophene π-spacer to electron-withdrawing alkyl-dicyanovinyl groups. The molecules could form stable aqueous dispersions of nanoparticles (NPs) without the addition of any surfactants or amphiphilic polymer matrixes with the average size distribution varying from 40 to 120 nm and absorption spectra very similar to those of human eye retina pigments such as rods and green cones. Variation of the terminal alkyl chain length of the molecules forming NPs from 1 to 12 carbon atoms was found to be an efficient tool to modulate their lipophilic and biological properties. Possibilities of using the NPs as light nanoactuators in biological systems or as artificial pigments for therapy of degenerative retinal diseases were studied both on the model planar bilayer lipid membranes and on the rat cortical neurons. In the planar bilayer system, the photodynamic activity of these NPs led to photoinactivation of ion channels formed by pentadecapeptide gramicidin A. Treatment of rat cortical neurons with the NPs caused depolarization of cell membranes upon light irradiation, which could also be due to the photodynamic activity of the NPs. The results of the work gave more insight into the mechanisms of light-controlled stimulation of neuronal activity and for the first time showed that fine-tuning of the lipophilic affinity of NPs based on organic conjugated molecules is of high importance for creating a bioelectronic interface for biomedical applications.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

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.

Artificial Visual Systems Fabricated with Ferroelectric van der Waals Heterostructure for In-Memory Computing Applications

Rapid developments in artificial neural network techniques and retina-inspired artificial visual systems are required to realize the sensing, processing, and memorization of an optical signal in a single device. Herein, a ferroelectric field-effect transistor fabricated with CuInP2S6 and α-In2Se3 van der Waals heterostructures is proposed and demonstrated for the development of an artificial visual system. The dipole polarizations are coupled and bidirectionally locked inside the ferroelectric α-In2Se3 along the in-plane and out-of-plane directions and are controlled by the gate voltages. Furthermore, light-induced polarization can change the order of polarization of the dipoles inside α-In2Se3. We demonstrate that using the combined control of these electrical and optical signals, the device may function like a retina-inspired vision system. The device can operate across a wide wavelength range (405-850 nm) and detect very low incident light (0.03 mW/cm2). Color recognition, high paired-pulse facilitation (∼170%), and short- to long-term memory transitions through quick learning are observed using this device. Additionally, this device demonstrates different complex processing abilities, including pattern recognition, light adaptation, optical logic operation, and event learning. The proposed ferroelectric heterostructure-based artificial visual system can serve as an essential bridge for fulfilling the future requirements of all-in-one sensing and memory-processing devices.

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.

2D Semiconductor Based Flexible Photoresponsive Ring Oscillators for Artificial Vision Pixels

Artificial retina implantation provides an effective and feasible attempt for vision recovery in addition to retinal transplantation. The most advanced artificial retinas ever developed based on silicon technology are rigid and thus less compatible with the biosystem. Here we demonstrate flexible photoresponsive ring oscillators (PROs) based on the 2D semiconductor MoS2 for artificial retinas. Under natural light illuminations, arrayed PROs on flexible substrates serving as vision pixels can efficiently output light-intensity-dependent electrical pulses that are processable and transmittable in the human visual nerve system. Such PROs can work under low supply voltages below 1 V with a record-low power consumption, e.g. only 12.4 nW at a light intensity of 10 mW/cm2, decreased by ∼500 times compared with that of the state-of-the-art silicon devices. Such flexible artificial retinas with a simple device structure, high light-to-signal conversion efficiency, ultralow power consumption, and high tunability provide an alternative prosthesis for further clinical trials.

Fast Photoresponsive Phototransistor Memory Using Star-Shaped Conjugated Rod-Coil Molecules as a Floating Gate

With the explosive growth in data generation, photomemory capable of multibit data storage is highly desired to enhance the capacity of storage media. To improve the performance of phototransistor memory, an organic-molecule-based electret with an elaborate nanostructure is of great importance because it can enable multibit data storage in a memory device with high stability. In this study, a series of star-shaped rod-coil molecules consisting of perylenediimide (PDI) and biobased solanesol were synthesized in two-armed (PDI-Sol2), four-armed (PDI-Sol4), and six-armed (PDI-Sol6) architectures. Their molecular architecture-morphology relationships were investigated, and phototransistor memory was fabricated and characterized to evaluate the structure-performance relationship of these rod-coil molecules. Accordingly, the memory devices were enabled by photowriting with panchromatic light (405-650 nm) and electrical erasing using a gate bias. The PDI-Sol4-based memory device showed high memory ratios of 10 000 over 10 000 s and a rapid multilevel photoresponse of 50 ms. This achievement is related to the favorable energy-level alignment, isolated nanostructure, and face-on orientation of PDI-Sol4, which eliminated the charge tunneling barrier. The results of this study provide a new strategy for tailoring nanostructures in organic-molecule-based electrets by using a star-shaped rod-coil architecture for high-performance phototransistor memory.

Retina-Inspired Color-Cognitive Learning via Chromatically Controllable Mixed Quantum Dot Synaptic Transistor Arrays

Artificial photonic synapses are emerging as a promising implementation to emulate the human visual cognitive system by consolidating a series of processes for sensing and memorizing visual information into one system. In particular, mimicking retinal functions such as multispectral color perception and controllable nonvolatility is important for realizing artificial visual systems. However, many studies to date have focused on monochromatic-light-based photonic synapses, and thus, the emulation of color discrimination capability remains an important challenge for visual intelligence. Here, an artificial multispectral color recognition system by employing heterojunction photosynaptic transistors consisting of ratio-controllable mixed quantum dot (M-QD) photoabsorbers and metal-oxide semiconducting channels is proposed. The biological photoreceptor inspires M-QD photoabsorbers with a precisely designed red (R), green (G), and blue (B)-QD ratio, enabling full-range visible color recognition with high photo-to-electric conversion efficiency. In addition, adjustable synaptic plasticity by modulating gate bias allows multiple nonvolatile-to-volatile memory conversion, leading to chromatic control in the artificial photonic synapse. To ensure the viability of the developed proof of concept, a 7 × 7 pixelated photonic synapse array capable of performing outstanding color image recognition based on adjustable wavelength-dependent volatility conversion is demonstrated.