Components needed in Artificial Intelligence with a higher information capacity are critically needed and have garnered significant attention at the forefront of information technology. This study utilizes solution-processed zinc–tin oxide (ZTO) thin-film phototransistors and modulates the values of V_G, which allows for the regulation of electron trapping/detrapping at the ZTO/SiO₂ interface. By coupling the excited photonic carrier and electronic trapping, logic gates such as “AND,” “OR,” “NAND,” and “NOR” can be achieved. With the exponential growth in data generation, efficient processing and storage solutions are imperative. However, extensive data transfer between computing units and storage limits the level of artificial neural networks (ANNs). Consequently, quantized neural networks (QNNs) have gained interest for their reduced computational resource requirements and lower consumption. In this context, we introduce an optimized ternary logic circuit based on ZTO devices. By utilizing optical modulation to adjust the turn-on voltage of the single device, we demonstrate the achievement of ternary current states, thereby providing three distinct discrete states. This configuration can be extended to QNN computing, demonstrating multilevel quantized current values for in-memory computation. We achieved a handwriting digit recognition rate of 91.6%, thereby demonstrating reliable QNN hardware performance. This robust QNN performance indicates that the metal oxide phototransistor shows significant potential for future ternary computing systems.

中文翻译：

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溶液处理锌-氧化锡薄膜晶体管中的界面电子电荷捕获和光子载流子激发耦合应用于逻辑门设计和量子化神经网络


人工智能需要具有更高信息容量的组件是迫切需要的，并且在信息技术的最前沿引起了极大的关注。本研究利用溶液处理的锌-锡氧化物 （ZTO） 薄膜光电晶体管并调制 VG_{的值，从而}允许调节 ZTO/SiO₂ 界面的电子捕获/去捕获。通过耦合激发的光子载流子和电子俘获，可以实现“AND”、“OR”、“NAND”和“NOR”等逻辑门。随着数据生成的指数级增长，高效的处理和存储解决方案势在必行。但是，计算单元和存储之间的大量数据传输限制了人工神经网络 （ANN） 的级别。因此，量化神经网络 （QNN） 因其减少计算资源需求和降低消耗而受到关注。在此背景下，我们介绍了一种基于 ZTO 器件的优化三元逻辑电路。通过利用光调制来调整单个器件的导通电压，我们展示了三元电流状态的实现，从而提供了三种不同的离散状态。此配置可以扩展到 QNN 计算，演示用于内存计算的多级量化电流值。我们实现了 91.6% 的手写数字识别率，从而展示了可靠的 QNN 硬件性能。这种稳健的 QNN 性能表明，金属氧化物光电晶体管在未来的三元计算系统中显示出巨大的潜力。