Implementing in-memory computation, an artificial neural network (ANN) consisting of thin-film transistors (TFTs) monolithically integrated in each unit of an array of capacitors is constructed. Both single-gate and parallel, dual-gate (DG) TFTs are deployed. The capacitors and the DG TFTs serve as the respective memory and computational elements. The DG TFT offers the capability of amplifying a weak but relevant input signal and suppressing a strong but irrelevant input signal across a synaptic gap, and the storage of charge on the capacitor is pseudostatic because of the exceptionally low OFF-state leakage current of the accompanying address TFT built on a metal–oxide semiconductor. The feasibility of such an ANN is demonstrated using a $4\times6$ array for classifying a specific set of Tetris patterns.

中文翻译：

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使用并行双栅极薄膜晶体管实现的人工神经网络

为了实现内存计算，构建了一个人工神经网络 (ANN)，该网络由单片集成在电容器阵列的每个单元中的薄膜晶体管 (TFT) 组成。部署了单栅极和并行双栅极 (DG) TFT。电容器和 DG TFT 用作各自的存储器和计算元件。DG TFT 提供了放大微弱但相关的输入信号并抑制跨突触间隙的强但无关的输入信号的能力，并且电容器上的电荷存储是伪静态的，因为伴随的极低的关闭状态泄漏电流寻址建立在金属氧化物半导体上的TFT。这种人工神经网络的可行性通过使用 $4\times6$用于对一组特定的俄罗斯方块模式进行分类的数组。