Hydrogel-Based Artificial Synapses for Sustainable Neuromorphic Electronics

Hydrogels find widespread applications in biomedicine because of their outstanding biocompatibility, biodegradability, and tunable material properties. Hydrogels can be chemically functionalized or reinforced to respond to physical or chemical stimulation, which opens up new possibilities in the emerging field of intelligent bioelectronics. Here, the state-of-the-art in functional hydrogel-based transistors and memristors is reviewed as potential artificial synapses. Within these systems, hydrogels can serve as semisolid dielectric electrolytes in transistors and as switching layers in memristors. These synaptic devices with volatile and non-volatile resistive switching show good adaptability to external stimuli for short-term and long-term synaptic memory effects, some of which are integrated into synaptic arrays as artificial neurons; although, there are discrepancies in switching performance and efficacy. By comparing different hydrogels and their respective properties, an outlook is provided on a new range of biocompatible, environment-friendly, and sustainable neuromorphic hardware. How potential energy-efficient information storage and processing can be achieved using artificial neural networks with brain-inspired architecture for neuromorphic computing is described. The development of hydrogel-based artificial synapses can significantly impact the fields of neuromorphic bionics, biometrics, and biosensing.

Organic Mixed Ionic-Electronic Conductors for Bioelectronic Sensors: Materials and Operation Mechanisms

The field of organic mixed ionic-electronic conductors (OMIECs) has gained significant attention due to their ability to transport both electrons and ions, making them promising candidates for various applications. Initially focused on inorganic materials, the exploration of mixed conduction has expanded to organic materials, especially polymers, owing to their advantages such as solution processability, flexibility, and property tunability. OMIECs, particularly in the form of polymers, possess both electronic and ionic transport functionalities. This review provides an overview of OMIECs in various aspects covering mechanisms of charge transport including electronic transport, ionic transport, and ionic-electronic coupling, as well as conducting/semiconducting conjugated polymers and their applications in organic bioelectronics, including (multi)sensors, neuromorphic devices, and electrochromic devices. OMIECs show promise in organic bioelectronics due to their compatibility with biological systems and the ability to modulate electronic conduction and ionic transport, resembling the principles of biological systems. Organic electrochemical transistors (OECTs) based on OMIECs offer significant potential for bioelectronic applications, responding to external stimuli through modulation of ionic transport. An in-depth review of recent research achievements in organic bioelectronic applications using OMIECs, categorized based on physical and chemical stimuli as well as neuromorphic devices and circuit applications, is presented.

An Ultrasensitive Biomimetic Optic Afferent Nervous System with Circadian Learnability

The optic afferent nervous system (OANS) plays a significant role in generating vision and circadian behaviors based on light detection and signals from the endocrine system. However, the bionic simulation of this photochemically mediated behavior is still a challenge for neuromorphic devices. Herein, stimuli of neurotransmitters at ultralow concentrations and illumination are coupled to artificial synapses with the aid of biofunctionalized heterojunction and tunneling to successfully simulate a circadian neural response. Furthermore, the mechanisms underlying the photosensitive synaptic current in response to stimuli are described. Interestingly, this OANS is demonstrated to be capable of mimicking normal and abnormal circadian learnability by combining the measured synaptic current with a three-layer spike neural network. Strong theoretical and experimental evidence, as well as applications, are provided for the proposed biomimetic OANS to demonstrate that it can reproduce biological circadian behavior, thus establishing it as a promising candidate for future neuromorphic intelligent robots.

Bio-inspired artificial synapse for neuromorphic computing based on NiO nanoparticle thin film

The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location are an exciting aspect of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics.

Enhanced Organic Electrochemical Transistor Performance of Donor-Acceptor Conjugated Polymers Modified with Hybrid Glycol/Ionic Side Chains by Postpolymerization Modification

Emergent bioelectronic technologies are underpinned by the organic electrochemical transistor (OECT), which employs an electrolyte medium to modulate the conductivity of its organic semiconductor channel. Here we utilize postpolymerization modification (PPM) on a conjugated polymer backbone to directly introduce glycolated or anionic side chains via fluoride displacement. The resulting polymers demonstrated increased volumetric capacitances, with subdued swelling, compared to their parent polymer in p-type enhancement mode OECTs. This increase in capacitance was attributed to their modified side chain configurations enabling cationic charge compensation for thin film electrochemical oxidation, as deduced from electrochemical quartz crystal microbalance measurements. An overall improvement in OECT performance was recorded for the hybrid glycol/ionic polymer compared to the parent, owing to its low swelling and bimodal crystalline orientation as imaged by grazing-incidence wide-angle X-ray scattering, enabling its high charge mobility at 1.02 cm²·V^-1·s^-1. Compromised device performance was recorded for the fully glycolated derivative compared to the parent, which was linked to its limited face-on stacking, which hindered OECT charge mobility at 0.26 cm²·V^-1·s^-1, despite its high capacitance. These results highlight the effectiveness of anionic side chain attachment by PPM as a means of increasing the volumetric capacitance of p-type conjugated polymers for OECTs, while retaining solid-state macromolecular properties that facilitate hole transport.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Wavelength and Polarization Sensitive Synaptic Phototransistor Based on Organic n-type Semiconductor/Supramolecular J-Aggregate Heterostructure

Human retina- and brain-inspired optoelectronic synapses, which integrate light detection and signal memory functions for data processing, have significant interest because of their potential applications for artificial vision technology. In nature, many animals such as mantis shrimp use polarized light information as well as scalar information including wavelength and intensity; however, a spectropolarimetric organic optoelectronic synapse has been seldom investigated. Herein, we report an organic synaptic phototransistor, consisting of a charge trapping liquid-crystalline perylene bisimide J-aggregate and a charge transporting crystalline dichlorinated naphthalene diimide, that can detect both wavelength and polarization information. The device shows persistent positive and negative photocurrents under low and high voltage conditions, respectively. Furthermore, the aligned organic heterostructure in the thin-film enables linearly polarized light to be absorbed with a dichroic ratio of 1.4 and 3.7 under transverse polarized blue and red light illumination, respectively. These features allow polarized light sensitive postsynaptic functions in the device. Consequently, a simple polarization imaging sensor array is successfully demonstrated using photonic synapses, which suggests that a supramolecular material is an important candidate for the development of spectropolarimetric neuromorphic vision systems.