Non-von Neumann multi-input spike signal processing enabled by an artificial synaptic multiplexer

Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles

Artificial neuromorphic devices can emulate dendric integration, axonal parallel transmission, along with superior energy efficiency in facilitating efficient information processing, offering enormous potential for wearable electronics. However, integrating such circuits into textiles to achieve biomimetic information perception, processing, and control motion feedback remains a formidable challenge. Here, we engineer a quasi-solid-state iontronic synapse fiber (ISF) comprising photoresponsive TiO2, ion storage Co-MoS2, and an ion transport layer. The resulting ISF achieves inherent short-term synaptic plasticity, femtojoule-range energy consumption, and the ability to transduce chemical/optical signals. Multiple ISFs are interwoven into a synthetic neural fabric, allowing the simultaneous propagation of distinct optical signals for transmitting parallel information. Importantly, IFSs with multiple input electrodes exhibit spatiotemporal information integration. As a proof of concept, a textile-based multiplexing neuromorphic sensorimotor system is constructed to connect synaptic fibers with artificial fiber muscles, enabling preneuronal sensing information integration, parallel transmission, and postneuronal information output to control the coordinated motor of fiber muscles. The proposed fiber system holds enormous promise in wearable electronics, soft robotics, and biomedical engineering.

Toward human-like adaptability in robotics through a retention-engineered synaptic control system

Although advanced robots can adeptly mimic human movement and aesthetics, they are still unable to adapt or evolve in response to external experiences. To address this limitation, we propose an innovative approach that uses parallel-processable retention-engineered synaptic devices in the control system. This approach aims to simulate a human-like learning system without necessitating complex computational systems. The retention properties of the synaptic devices were modulated by adjusting the amount of Ag/AgCl ink sprayed. This changed the voltage drop across the interface between the gate electrode and the electrolyte. Furthermore, the unrestricted movement of ions in the electrolyte enhanced the signal multiplexing capability of the ion gel, enabling device-level parallel processing. By integrating the unique characteristics of the synaptic devices with actuators, we successfully emulated a human-like workout process that includes feedback between acute and chronic responses. The proposed control system offers an innovative approach to reducing system complexity and achieving a human-like learning system in the field of biomimicry.

Tuning Gate Potential Profiles and Current-Voltage Characteristics of Polymer Electrolyte-Gated Transistors by Capacitance Engineering

We demonstrate that the transfer characteristics of electrolyte-gated transistors (EGTs) with polythiophene semiconductor channels are a strong function of gate/electrolyte interfacial contact area, i.e., gate size. Polythiophene EGTs with gate/electrolyte areas much larger than the channel/electrolyte areas show a clear peak in the drain current vs gate voltage (ID-VG) behavior, as well as peak voltage hysteresis between the forward and reverse VG sweeps. Polythiophene EGTs with small gate/electrolyte areas, on the other hand, exhibit current plateaus in the ID-VG behavior and a gate-size-dependent hysteresis loop between turn on and off. The qualitatively different transport behaviors are attributed to the relative sizes of the gate/electrolyte and channel/electrolyte interface capacitances, which are proportional to interfacial area. These interfacial capacitances are in series with each other such that the total capacitance of the full gate/electrolyte/channel stack is dominated by the interface with the smallest capacitance or area. For EGTs with large gates, most of the applied VG is dropped at the channel/electrolyte interface, leading to very high charge accumulations, up to ∼0.3 holes per ring (hpr) in the case of polythiophene semiconductors. The large charge density results in sub-band-filling and a marked decrease in hole mobility, giving rise to the peak in ID-VG. For EGTs with small gates, hole accumulation saturates near 0.15 hpr, band-filling does not occur, and hole mobility is maintained at a fixed value, which leads to the ID plateau. Potential drops at the interfaces are confirmed by in situ potential measurements inside a gate/electrolyte/polymer semiconductor stack. Hole accumulations are measured with gate current-gate voltage (IG-VG) measurements acquired simultaneously with the ID-VG characteristics. Overall, our measurements demonstrate that remarkably different ID behavior can be obtained for polythiophene EGTs by controlling the magnitude of the gate-electrolyte interfacial capacitance.

Electrochemical Analysis of Ion Effects on Electrolyte-Gated Synaptic Transistor Characteristics

Electrolyte-gated transistors (EGTs) are promising candidates as artificial synapses owing to their precise conductance controllability, quick response times, and especially their low operating voltages resulting from ion-assisted signal transmission. However, it is still vague how ion-related physiochemical elements and working mechanisms impact synaptic performance. Here, to address the unclear correlations, we suggest a methodical approach based on electrochemical analysis using poly(ethylene oxide) EGTs with three alkali ions: Li+, Na+, and K+. Cyclic voltammetry is employed to identify the kind of electrochemical reactions taking place at the channel/electrolyte interface, which determines the nonvolatile memory functionality of the EGTs. Additionally, using electrochemical impedance spectroscopy and qualitative analysis of electrolytes, we confirm that the intrinsic properties of electrolytes (such as crystallinity, solubility, and ion conductivity) and ion dynamics ultimately define the linearity/symmetricity of conductance modulation. Through simple but systematic electrochemical analysis, these results offer useful insights for the selection of components for high-performing artificial synapses.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

Single-Line Multi-Channel Flexible Stress Sensor Arrays

Flexible stress sensor arrays, comprising multiple flexible stress sensor units, enable accurate quantification and analysis of spatial stress distribution. Nevertheless, the current implementation of flexible stress sensor arrays faces the challenge of excessive signal wires, resulting in reduced deformability, stability, reliability, and increased costs. The primary obstacle lies in the electric amplitude modulation nature of the sensor unit's signal (e.g., resistance and capacitance), allowing only one signal per wire. To overcome this challenge, the single-line multi-channel signal (SLMC) measurement has been developed, enabling simultaneous detection of multiple sensor signals through one or two signal wires, which effectively reduces the number of signal wires, thereby enhancing stability, deformability, and reliability. This review offers a general knowledge of SLMC measurement beginning with flexible stress sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on different arraying methods and their corresponding advantages and disadvantages. Finally, this review categorizes existing SLMC measurement methods into RLC series resonant sensing, transmission line sensing, ionic conductor sensing, triboelectric sensing, piezoresistive sensing, and distributed fiber optic sensing based on their mechanisms, describes the mechanisms and characteristics of each method and summarizes the research status of SLMC measurement.

Multiplexed Complementary Signal Transmission for a Self-Regulating Artificial Nervous System

Neuromorphic engineering has emerged as a promising research field that can enable efficient and sophisticated signal transmission by mimicking the biological nervous system. This paper presents an artificial nervous system capable of facile self-regulation via multiplexed complementary signals. Based on the tunable nature of the Schottky barrier of a complementary signal integration circuit, a pair of complementary signals is successfully integrated to realize efficient signal transmission. As a proof of concept, a feedback-based blood glucose level control system is constructed by incorporating a glucose/insulin sensor, a complementary signal integration circuit, an artificial synapse, and an artificial neuron circuit. Certain amounts of glucose and insulin in the initial state are detected by each sensor and reflected as positive and negative amplitudes of the multiplexed presynaptic pulses, respectively. Subsequently, the pulses are converted to postsynaptic current, which triggered the injection of glucose or insulin in a way that confined the glucose level to a desirable range. The proposed artificial nervous system demonstrates the notable potential of practical advances in complementary control engineering.