Xiang Wan's research while affiliated with Nanjing University of Posts and Telecommunications and other places

Organic–inorganic hybrid perovskites are widely used in photodetection owing to their high optical absorption coefficients. A variety of research has been conducted on perovskite phototransistors and their optoelectronic properties, but the exploration of the influence of contact resistance remains limited. In this work, we employed the transmission-line method to separate the contact resistance Rc × W (ranging from 4.81 × 104 to 4.77 × 103 Ω cm) and the channel resistance Rch × W (ranging from 1.93 × 104 to 1.16 × 104 Ω cm) of (PEA)2SnI4 perovskite phototransistors at different light intensities (520 nm, ranging from 0 to 2550 μW/cm2). Further analysis reveals that illumination-induced accumulation of charge carriers at the metal/semiconductor interface reduces the Schottky barrier. Approximately 90% of the observed increase in photocurrent can be attributed to the reduction in the contact resistance. Our finding underscores the crucial role of charge injection in influencing perovskite-based phototransistors.

Organic electrochemical transistors (OECTs) are now widely investigated for their potential use in brain-inspired neuromorphic computation. In this paper, chitosan-gated OECTs are proposed as the neuromorphic devices. The proton conductive chitosan employed as the dielectric can provide low-voltage operation and synapse-like functions for such device via its electric-double-layer (EDL) capacitive effect. Two wiring schemes are utilized for such devices to emulate two different types of short-term synaptic plasticity: potentiation (STP) and depression (STD). These schemes are further integrated into a novel neuromorphic circuit to implement direction selectivity. The achieved direction selectivity is subsequently employed as a temporal-coding method for the recognition of dynamic handwriting. This study demonstrates the tremendous potential of chitosan-gated OECT in neuromorphic application, and it is expected to accelerate the development of next-generation artificial intelligence systems.

Spiking neural networks (SNNs) employ discrete spikes that mimic the firing of neurons in biological systems to process and transmit information. This characteristic enables SNNs to effectively capture temporal dynamics and capitalize on the time information inherent in time‐varying inputs, such as motion, audio/video streams, and other sequential data. Currently, most hardware implementations of SNNs are designed to use rate‐coding, where information is encoded in the rate of spikes. However, it still remains challenging for the hardware implementation of temporal coding in SNNs, which allows for higher input sparsity and exploits additional dimensions such as precise spike timing and relative spike timings. This study presents hardware implementations of SNNs constructed by organic electrochemical transistors (OECTs), processing temporal‐coded information. The protic dynamics in response to electrical stimuli enable the emulation of temporal integration, reset, and leaking of membrane potential in a simple leaky integrate‐and‐fire (LIF) neuron circuit. By utilizing these features, the emulated LIF neuron can be employed to construct SNNs capable of processing temporal‐coded information in complex tasks including coincidence detection and dynamic handwriting recognition, exhibiting high performance and good tolerance even when dealing with noisy datasets.

Donor-Acceptor (D-A) polymer field-effect transistors (pFETs) are at the cutting edge of organic electronics. However, some fundamental cognition of interface states remains unquantified, particularly at different dynamic scales. In this study, the interface states of D-A polymer transistors are quantified using the dynamic pumping method. The experimental results indicate that the interface state density of the transistor is insensitive to the measurement pulse condition and stays within the range of 1012 1013 cm-2. The experiments described in this paper provide a quantitative analysis of interface states. This analysis can serve as effective guidance for optimizing future devices.