Functional emulation of biological synapses using electronic devices is regarded as the first step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte-gated transistors (EGTs) are mixed ionic–electronic conductivity devices capable of efficient gate-channel capacitance coupling, biocompatibility, and flexible architectures. Electrolyte gating offers significant advantages for the realization of neuromorphic devices/architectures, including ultralow-voltage operation and the ability to form parallel-interconnected networks with minimal hardwired connectivity. In this review, the most recent developments in EGT-based electronics are introduced with their synaptic behaviors and detailed mechanisms, including short-/long-term plasticity, global regulation phenomena, lateral coupling between device terminals, and spatiotemporal correlated functions. Analog memory phenomena allow for the implementation of perceptron-based ANNs. Due to their mixed-conductivity phenomena, neuromorphic circuits based on EGTs allow for facile interfacing with biological environments. We also discuss the future challenges in implementing low power, high speed, and reliable neuromorphic computing for large-scale ANNs with these neuromorphic devices. The advancement of neuromorphic devices that rely on EGTs highlights the importance of this field for neuromorphic computing and for novel healthcare technologies in the form of adaptable or trainable biointerfacing.

Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing

Triboelectric nanogenerator based self-powered sensor for artificial intelligence

Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS2 heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.

Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure.

Resistive switching memory integrated with amorphous carbon-based nanogenerators for self-powered device

To construct an artificial intelligence system with high efficient information integration and computing capability like the human brain, it is necessary to realize the biological neurotransmission and information processing in artificial neural network (ANN), rather than a single electronic synapse as most reports. Because the power consumption of single synaptic event is ∼10 fJ in biology, designing an intelligent memristors-based 3D ANN with energy consumption lower than femtojoule-level (e.g., attojoule-level) and faster operating speed than millisecond-level makes it possible for constructing a higher energy efficient and higher speed computing system than the human brain. In this paper, a flexible 3D crossbar memristor array is presented, exhibiting the multilevel information transmission functionality with the power consumption of 4.28 aJ and the response speed of 50 ns per synaptic event. This work is a significant step toward the development of an ultrahigh efficient and ultrahigh-speed wearable 3D neuromorphic computing system.

Three-Dimensional Nanoscale Flexible Memristor Networks with Ultralow Power for Information Transmission and Processing Application

Self-powered artificial synapses actuated by triboelectric nanogenerator

Stretchable synaptic transistors with tunable synaptic behavior

Gelatin-hydrogel based organic synaptic transistor

The artificial neural system has attracted tremendous attention in the field of artificial intelligence due to operate mode of parallel computation which is superior to traditional Von Neumann computers in processing complex sensory data and real-time situations with extremely low power dissipation. Remarkable progress has been made in the hardware-based electric-double-layer synaptic transistors as its modulation by ion movement is similar to biological synapse for the past few years. Unfortunately, long-term potentiation (LTP) timescale is still a big challenge in hardware-based electric-double-layer synaptic transistors which is essential to processing capacity and memory formation. Meanwhile, the effect of ion concentration on the synaptic plasticity has rarely been reported. Here, a solid state electrolyte-gated transistor using Ta2O5 as dielectric layer with unique ionic composition was demonstrated and the regulation of synaptic weight was realized by changing ion concentration. Both the potentiation and depression of synaptic weight such as excitatory post-synaptic current, inhibitory response (IPSC), paired pulse facilitation as well as LTP were successfully simulated. More importantly, oxygen vacancy content was tuned for the first time to modulate synaptic plasticity by varying film thickness and gas ratio, through which the intensity and duration of memory were enhanced with appropriate vacancy concentration. It indicated that appropriate vacancy concentration avoided the effects of internal electric field induced by ion excess, leading to a long-term memory. These results reveal a promising path to improve memory capacity of artificial synapse via ion modulation.

https://iopscience.iop.org/article/10.1088/1361-6528/ab5080/pdf

All-metal oxide synaptic transistor with modulatable plasticity.

Organic phototransistors with high sensitivity and responsivity to light irradiance have great potential applications in national defense, meteorology, industrial manufacturing, and medical security. However, undesired dark current and photoresponsivity limit their practical applications. Here, a novel vertical organic phototransistor combined with ferroelectric materials is developed. The device structure has nanometer channel length, which can effectively separate photogenerated carriers and reduce the probability of carrier recombination and defect scattering, thus improving the device performance of phototransistors. Moreover, by inserting the poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) ferroelectric layer, the Schottky barrier at the interface between the semiconductor and source can be adjusted by the polarization of the external electric field, which can effectively reduce the dark current of the phototransistor to further improve the device performance. Therefore, our phototransistors exhibit a high photoresponsivity of more than 5.7 × 105A/W, an outstanding detectivity of 1.15 × 1018 Jones, and an excellent photosensitivity of 5 × 107 under 760 nm light illumination, which are better than those of conventional lateral organic phototransistors. This work provides a new approach for the development of high-performance phototransistors, which opens a new pathway for organic phototransistors in practical application.

Dengxiao Lai

Papers

Self-powered artificial synapses actuated by triboelectric nanogenerator

Stretchable synaptic transistors with tunable synaptic behavior

Gelatin-hydrogel based organic synaptic transistor

All-metal oxide synaptic transistor with modulatable plasticity.

High-Performance Vertical Organic Phototransistors Enhanced by Ferroelectrics.