Low-Energy-Consumption and Electret-Free Photosynaptic Transistor Utilizing Poly(3-hexylthiophene)-Based Conjugated Block Copolymers

Open-shell Poly(3,4-dioxythiophene) Radical for Highly Efficient Photothermal Conversion

Open-shell organic radical semiconductor materials have received increasing attention in recent years due to their distinctive properties compared to the traditional materials with closed-shell singlet ground state. However, their poor chemical and photothermal stability in ambient conditions remains a significant challenge, primarily owing to their high reactivity with oxygen. Herein, a novel open-shell poly(3,4-dioxythiophene) radical PTTO2 is designed and readily synthesized for the first time using low-cost raw material via a straightforward BBr3-demethylation of the copolymer PTTOMe2 precursor. The open-shell character of PTTO2 is carefully studied and confirmed via the signal-silent 1H nuclear magnetic resonance spectrum, highly enhanced electron spin resonance signal compared with PTTOMe2, as well as the ultra-wide ultraviolet-visible-near nfraredUV-vis-NIR absorption and other technologies. Interestingly, the powder of PTTO2 exhibits an extraordinary absorption range spanning from 300 to 2500 nm and can reach 274 °C under the irradiation of 1.2 W cm-2, substantially higher than the 108 °C achieved by PTTOMe2. The low-cost PTTO2 stands as one of the best photothermal conversion materials among the pure organic photothermal materials and provides a new scaffold for the design of stable non-doped open-shell polymers.

Interfacial Stabilization of Organic Electrochemical Transistors Conferred Using Polythiophene-Based Conjugated Block Copolymers with a Hydrophobic Coil Design

The recent interest in developing low-cost, biocompatible, and lightweight bioelectronic devices has focused on organic electrochemical transistors (OECTs), which have the potential to fulfill these requirements. In this study, three types of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) incorporating different insulating blocks (poly(nbutyl acrylate) (PBA), polystyrene, and poly(ethylene oxide) (PEO)) were synthesized for application in OECTs. The morphological, crystallographic, and electrochemical properties of these BCPs are systematically investigated. Accordingly, P3HT-b-PBA demonstrates superior performance in the KCl-based aqueous electrolyte, with a higher product of mobility and capacitance (μC*) at 170 F s-1 cm-1 V-1 than that of the P3HT homopolymer at 58 F s-1 cm-1 V-1. P3HT-b-PBA exhibits better stability over 50 ON/OFF switching cycles than do other BCPs and P3HT homopolymers. With regard to the performance in the KPF6-based aqueous electrolyte, P3HT-b-PBA outperforms with a higher μC* of 9.2 F s-1 cm-1 V-1 than that of 8.6 F s-1 cm-1 V-1 observed from P3HT. Notably, both polymers exhibited almost no decay in device performance over 110 ON/OFF switching cycles. The strongly different performance of polymers in these two electrolytes is due to the side chain's hydrophobicity and interdigitated lamellar structures, thereby retarding the doping kinetics of the highly hydrated Cl- ions compared with the slightly hydrated PF6- ions. Concerning the improved performance of P3HT-b-PBA, this is attributed to its soft and hydrophobic backbone. Our morphological and crystallographic analyses reveal that P3HT-b-PBA experiences minimal structural disorder when swelled by the electrolyte, maintaining its original structure better than the P3HT homopolymer and the hydrophilic BCP of P3HT-b-PEO. The hydrophobic nature of P3HT-b-PBA contributes to the stability of its backbone structure, ensuring enhanced capacitance during the operation of the OECT operation. These findings provide reassurance about the stability and performance of P3HT-b-PBA in the field of OECT applications. In summary, this study represents the first exploration of P3HT-based BCPs for OECT applications and investigates their structure-performance relationships in mixed ionic-electronic conductors.

A bionic self-driven retinomorphic eye with ionogel photosynaptic retina

Bioinspired bionic eyes should be self-driving, repairable and conformal to arbitrary geometries. Such eye would enable wide-field detection and efficient visual signal processing without requiring external energy, along with retinal transplantation by replacing dysfunctional photoreceptors with healthy ones for vision restoration. A variety of artificial eyes have been constructed with hemispherical silicon, perovskite and heterostructure photoreceptors, but creating zero-powered retinomorphic system with transplantable conformal features remains elusive. By combining neuromorphic principle with retinal and ionoelastomer engineering, we demonstrate a self-driven hemispherical retinomorphic eye with elastomeric retina made of ionogel heterojunction as photoreceptors. The receptor driven by photothermoelectric effect shows photoperception with broadband light detection (365 to 970 nm), wide field-of-view (180°) and photosynaptic (paired-pulse facilitation index, 153%) behaviors for biosimilar visual learning. The retinal photoreceptors are transplantable and conformal to any complex surface, enabling visual restoration for dynamic optical imaging and motion tracking.

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.

A bi-functional three-terminal memristor applicable as an artificial synapse and neuron

Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN.

Tailoring Wavelength-Adaptive Visual Neuroplasticity Transitions of Synaptic Transistors Comprising Rod-Coil Block Copolymers for Dual-Mode Photoswitchable Learning/Forgetting Neural Functions

The vision-inspired artificial neural network based on optical synapses has drawn a tremendous amount of attention for emulating biological senses. Although photoexcitation-induced synaptic functionalities have been widely studied, optical habituation via the photoinhibitory pathway is yet to be demonstrated for sophisticated biomimetic visual adaptive systems. Here, the first optical neuromorphic block copolymer (BCP) phototransistor is demonstrated as an all-optical operation responding to various wavelengths, fulfilling photoassisted dynamic learning/forgetting cycles via optical potentiation without gate bias. The polyfluorene BCPs were precisely designed to enable wavelength-adaptive responses, benefiting from interfacial semiconductor/electret morphology and the crystallinity/electron affinity of the BCPs. Notably, this is the first work to simultaneously exhibit fully light-controlled short- and long-term memory based on organic material systems. The device presents a high current contrast above 100-fold and long-term retention over 104 s. As a proof-of-concept for neural networks, a 6 × 6 array of photosynapses performed outstanding visual pattern learning/forgetting with high accuracy. This study exploits the design strategy of a conjugated BCP electret to unleash the full potential of wavelength-adaptive visual neuroplasticity transitions. It provides an effective architecture for designing high-performance and high-storage capacity required applications in next-generation neuromorphic systems.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Molecular template growth of organic heterojunctions to tailor visual neuroplasticity for high performance phototransistors with ultralow energy consumption

The optical and charge transport properties of organic semiconductors are strongly influenced by their morphology and molecular structures. Here we report the influence of a molecular template strategy on anisotropic control via weak epitaxial growth of a semiconducting channel for a dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT)/para-sexiphenyl (p-6P) heterojunction. The aim is to improve charge transport and trapping, to enable tailoring of visual neuroplasticity. The proposed phototransistor devices, comprising a molecular heterojunction with optimized molecular template thickness, exhibited an excellent memory ratio (I_ON/I_OFF) and retention characteristics in response to light stimulation, owing to the enhanced orientation/packing of DNTT molecules and a favorable match between the LUMO/HOMO levels of p-6P and DNTT. The best performing heterojunction exhibits visual synaptic functionalities, including an extremely high pair-pulse facilitation index of ∼206%, ultralow energy consumption of 0.54 fJ, and zero-gate operation, under ultrashort pulse light stimulation to mimic human-like sensing, computing, and memory functions. An array of heterojunction photosynapses possess a high degree of visual pattern recognition and learning, to mimic the neuroplasticity of human brain activities through a rehearsal learning process. This study provides a guide to the design of molecular heterojunctions for tailoring high-performance photonic memory and synapses for neuromorphic computing and artificial intelligence systems.

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

Flexible synaptic devices with information sensing, processing, and storage functions are indispensable in the development of wearable artificial intelligence electronic systems. Here, a semiconductor/dielectric bilayer structure was prepared by a one-step deposition method and used for the first time in a flexible biomimetic photonic synaptic transistor device. Specifically, poly(3-hexylthiophene)-block-poly(phenyl isocyanide) with pentafluorophenyl ester (P3HT-b-PPI(5F)) was prepared as the device active layer, where the P3HT segment served as a carrier transport channel and optical gate and the PPI(5F) segment was used for charge trapping. Various biomimetic synaptic behaviors, such as excitatory postsynaptic currents, paired-pulse facilitation, and short-term/long-term memory, were successfully simulated under green light stimulation. An ultra-low energy consumption of 1.82 fJ was achieved with a greatly reduced operating voltage. Further, the "Morse-code" optical decoding was simulated using the excellent synaptic plasticity of the device. In addition, flexible synaptic devices were prepared by a one-step deposition method and can be well-affixed to arbitrary substrates. This has promising applications in the field of wearable bionic electronics.

Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning

In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.

Artificial Tactile Recognition Enabled by Flexible Low-Voltage Organic Transistors and Low-Power Synaptic Electronics

The advancement of self-powered intelligent strain systems for human-computer interaction is crucial toward wearable and energy-saving applications. Simultaneously, lowering operating voltage and thus reducing power consumption are of particular interests. A brain-like smart synaptic hardware system is considered as a promising candidate for low-power, parallel computing and learning processes. However, the combination of low-voltage organic transistors and energy efficient smart synapse hardware systems driven by a tactile signal has been hindered by the limited materials and technology. Here, by employing an elastomeric copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with a high HFP content of 25 mol %, flexible, low-voltage transistors (|V_G| ≤ 3 V) and a low energy consumption synapse ≤ 9.2 × 10^-17 J are devised simultaneously, along with the lowest quality factor (R = P_w × V_G, 2.76 × 10^-16 J V). Furthermore, based on the low voltage and low power consumption characteristics, flexible artificial tactile recognition system and Morse code recognition are established without any computing supporting. Mechanical flexibility, cycling stability, image contrast enhancement functions, and simulated pattern recognition accuracy of the multilayer perceptron neural network are also simulated. This work recommends a route of exploiting low voltage, low power consumption synaptic systems and smart human-machine interfaces with low energy loss based on flexible organic synaptic transistors.

Tuning Ambipolarity of the Conjugated Polymer Channel Layers of Floating-Gate Free Transistors: From Volatile Memories to Artificial Synapses

Three-terminal synaptic transistor has drawn significant research interests for neuromorphic computation due to its advantage of facile device integrability. Lately, bulk-heterojunction-based synaptic transistors with bipolar modulation are proposed to exempt the use of an additional floating gate. However, the actual correlation between the channel's ambipolarity, memory characteristic, and synaptic behavior for a floating-gate free transistor has not been investigated yet. Herein, by studying five diketopyrrolopyrrole-benzotriazole dual-acceptor random conjugated polymers, a clear correlation among the hole/electron ratio, the memory retention characteristic, and the synaptic behavior for the polymer channel layer in a floating-gate free transistor is described. It reveals that the polymers with balanced ambipolarity possess better charge trapping capabilities and larger memory windows; however, the high ambipolarity results in higher volatility of the memory characteristics, namely poor memory retention capability. In contrast, the polymer with a reduced ambipolarity possesses an enhanced memory retention capability despite showing a reduced memory window. It is further manifested that this enhanced charge retention capability enables the device to present artificial synaptic characteristics. The results highlight the importance of the channel's ambipolarity of floating-gate free transistors on the resultant volatile memory characteristics and synaptic behaviors.

Amphiphilic PTB7-Based Rod-Coil Block Copolymer for Water-Processable Nanoparticles as an Active Layer for Sustainable Organic Photovoltaic: A Case Study

We synthetized a new rod-coil block copolymer (BCP) based on the semiconducting polymerpoly({4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl}{3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl}) (PTB7) and poly-4-vinylpyridine (P4VP), tailored to produce water-processable nanoparticles (WPNPs) in blend with phenyl-C71-butyric acid methyl ester (PC₇₁BM). The copolymer PTB7-b-P4VP was completely characterized by means of two-dimensional nuclear magnetic resonance (2D-NMR), matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (MS), size-exclusion chromatography (SEC), and differential scanning calorimetry (DSC) to confirm the molecular structure. The WPNPs were prepared through an adapted miniemulsion approach without any surfactants. Transmission electron microscopy (TEM) images reveal the nano-segregation of two active materials inside the WPNPs. The nanostructures appear spherical with a Janus-like inner morphology. PTB7 segregated to one side of the nanoparticle, while PC₇₁BM segregated to the other side. This morphology was consistent with the value of the surface energy obtained for the two active materials PTB7-b-P4VP and PC₇₁BM. The WPNPs obtained were deposited as an active layer of organic solar cells (OSCs). The films obtained were characterized by UV-Visible Spectroscopy (UV-vis), atomic force microscopy (AFM), and grazing incidence X-ray diffraction (GIXRD). J-V characteristics of the WPNP-based devices were measured by obtaining a power conversion efficiency of 0.85%. Noticeably, the efficiency of the WPNP-based devices was higher than that achieved for the devices fabricated with the PTB7-based BCP dissolved in chlorinated organic solvent.