Sharply Increased Current in Asymmetrically Aligned Polycrystalline Polymer Transistors With Sub-Domain-Size Channels

Zhang, Dakuan; Yin, Yao; Lv, Wei; Gao, Fan; Pan, Danfeng; Tu, Xuecou; Ju, Shihao; Su, Xin; Ma, Xuezhi; Sun, Huabin; Xu, Yong; Li, Song Lin; Shi, Yi

doi:10.1109/led.2020.2976653

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…Figure 2b,c shows the transfer and output characteristics of the DPPT-TT transistor, indicating its typical p-type behavior. The operating voltage is relatively low compared to that of the previous research, [38,39] due to the electric-double-layer (EDL) capacitive behavior of the chitosan-based protic electrolyte dielectric. [40] The EDL is formed at the interface between the electrolyte and the channel, whereupon gate-to-channel voltages the depletion/accumulation of interfacial protons induce/reduce holes in the p-type DPPT-TT channel, turning on/off the device, as illustrated in Figure S2 (Supporting Information).…”

Section: Resultsmentioning

confidence: 76%

Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding

Zhu,

Wan,

Yan

et al. 2024

Adv Elect Materials

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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.

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Section: Resultsmentioning

confidence: 76%