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The primary objective of neuromorphic electronic devices is the implementation of neural networks that replicate the memory and learning functions of biological synapses. To exploit the advantages of electrolyte gate synaptic transistors operating like biological synapses, we engineered electric double-layer transistors (EDLTs) using phosphorus-doped silicate glass (PSG). To investigate the effects of phosphorus on the EDL and synaptic behavior, undoped silicate spin-on-glass-based transistors were fabricated as a control group. Initially, we measured the frequency-dependent capacitance and double-sweep transfer curves for the metal-oxide-semiconductor (MOS) capacitors and MOS field-effect transistors. Subsequently, we analyzed the excitatory post-synaptic currents (EPSCs), including pre-synaptic single spikes, double spikes, and frequency variations. The capacitance and hysteresis window characteristics of the PSG for synaptic operations were verified. To assess the specific synaptic operational characteristics of PSG-EDLTs, we examined EPSCs based on the spike number and established synaptic weights in potentiation and depression (P/D) in relation to pre-synaptic variables. Normalizing the P/D results, we extracted the parameter values for the nonlinearity factor, asymmetric ratio, and dynamic range based on the pre-synaptic variables, revealing the trade-off relationships among them. Finally, based on artificial neural network simulations, we verified the high-recognition rate of PSG-EDLTs for handwritten digits. These results suggest that phosphorus-based EDLTs are beneficial for implementing high-performance artificial synaptic hardware. [ABSTRACT FROM AUTHOR]

Stretchable displays, capable of freely transforming their shapes, have received significant attention as alternatives to conventional rigid displays, and they are anticipated to provide new opportunities in various human-friendly electronics applications. As a core component of stretchable displays, high-performance stretchable light-emitting diodes (LEDs) have recently emerged. The approaches to fabricate stretchable LEDs are broadly categorized into two groups, namely "structural" and "material-based" approaches, based on the mechanisms to tolerate strain. While structural approaches rely on specially designed geometries to dissipate applied strain, material-based approaches mainly focus on replacing conventional rigid components of LEDs to soft and stretchable materials. Here, we review the latest studies on the fabrication of stretchable LEDs, which is accomplished through these distinctive strategies. First, we introduce representative device designs for efficient strain distribution, encompassing island-bridge structures, wavy buckling, and kirigami-/origami-based structures. For the material-based approaches, we discuss the latest studies for intrinsically stretchable (is-) electronic/optoelectronic materials, including the formation of conductive nanocomposite and polymeric blending with various additives. The review also provides examples of is-LEDs, focusing on their luminous performance and stretchability. We conclude this review with a brief outlook on future technologies. [ABSTRACT FROM AUTHOR]

In this study, a high-performance bio-organic memristor with a crossbar array structure using milk as a resistive switching layer (RSL) is proposed. To ensure compatibility with the complementary metal oxide semiconductor process of milk RSL, a high-k Ta2O5 layer was deposited as a capping layer; this layer enables high-density, integration-capable, photolithography processes. The fabricated crossbar array memristors contain milk–Ta2O5 hybrid membranes, and they exhibit bipolar resistance switching behavior and uniform resistance distribution across hundreds of repeated test cycles. In terms of the artificial synaptic behavior and synaptic weight changes, milk–Ta2O5 hybrid crossbar array memristors have a stable analog RESET process, and the memristors are highly responsive to presynaptic stimulation via paired-pulse facilitation excitatory post-synaptic current. Moreover, spike-timing-dependent plasticity and potentiation and depression behaviors, which closely emulate long-term plasticity and modulate synaptic weights, were evaluated. Finally, an artificial neural network was designed and trained to recognize the pattern of the Modified National Institute of Standards and Technology (MNIST) digits to evaluate the capability of the neuromorphic computing system. Consequently, a high recognition rate of over 88% was achieved. Thus, the milk–Ta2O5 hybrid crossbar array memristor is a promising electronic platform for in-memory computing systems. [ABSTRACT FROM AUTHOR]