铁电半导体中用于储层计算的光生电流和短期记忆
Photo-induced currents and short-term memory for reservoir computing in a ferroelectric semiconductor
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中文总结 AI 辅助
研究以ErMnO₃为模型，探索利用光生电流识别时变光脉冲用于储层计算的可能。该材料在白光照射下有非线性光生电流和可控弛豫动力学，经储层变换后，对“过去”输入脉冲识别精度从约33%提至约93%，展现铁电六方锰酸盐用于储层计算的潜力。
中文摘要 AI 辅助
物理储层计算是一种通过利用物理系统的固有非线性动力学和衰退记忆来处理时间信号的节能方法。近来，铁电半导体因其对外部刺激的多种电子响应而成为储层材料的焦点。本文以小带隙p型半导体ErMnO₃为模型系统，探索通过光生电流识别时变光脉冲的基本可能性。在白光照射下，ErMnO₃表现出非线性演化的光生电流和可控的弛豫动力学，自然实现了储层计算所需的高维投影和衰退记忆能力。ErMnO₃的储层能力通过对“过去”输入脉冲识别精度的提高得以体现，对输入信号进行储层变换后，识别精度从约33%提高到约93%。结果表明铁电六方锰酸盐是基于光生电流的储层计算的有前途的平台，并突出了光驱动氧化物半导体用于时间信息处理的潜力。
英文摘要
Physical reservoir computing represents an energy efficient approach for processing temporal signals by exploiting the intrinsic nonlinear dynamics and fading memory of a physical system. Recently, ferroelectric semiconductors moved into focus as reservoir materials motivated by their versatile electronic responses to external stimuli. Here, we explore the fundamental possibility to recognize time-varying light pulses via photo-induced currents, using the small-band-gap p-type semiconductor ErMnO$_3$ as a model system. Under white light illumination, ErMnO$_3$ exhibits non-linearly evolving photo-induced currents and controllable relaxation dynamics that naturally realize the high-dimensional projection and fading memory capabilities required for reservoir computing. The reservoir capability of ErMnO$_3$ is reflected by the improved recognition accuracy of "Past" input pulses, which increases from ~33% to ~93% after applying reservoir transformation to the input signal. The results present ferroelectric hexagonal manganites as a promising platform for photo-induced current-based reservoir computing and highlight the potential of light-driven oxide semiconductors for temporal information processing.
