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具有铝掺杂铪氧化物铁电薄膜的模拟突触晶体管。

Analog Synaptic Transistor with Al-Doped HfO Ferroelectric Thin Film.

作者信息

Kim Duho, Jeon Yu-Rim, Ku Boncheol, Chung Chulwon, Kim Tae Heun, Yang Sanghyeok, Won Uiyeon, Jeong Taeho, Choi Changhwan

机构信息

Division of Materials Science & Engineering, Hanyang University, Seoul 04763, Republic of Korea.

Department of Energy Engineering, Hanyang University, Seoul 04763, Republic of Korea.

出版信息

ACS Appl Mater Interfaces. 2021 Nov 10;13(44):52743-52753. doi: 10.1021/acsami.1c12735. Epub 2021 Nov 1.

Abstract

Neuromorphic computing has garnered significant attention because it can overcome the limitations of the current von-Neumann computing system. Analog synaptic devices are essential for realizing hardware-based artificial neuromorphic devices; however, only a few systematic studies in terms of both synaptic materials and device structures have been conducted so far, and thus, further research is required in this direction. In this study, we demonstrate the synaptic characteristics of a ferroelectric material-based thin-film transistor (FeTFT) that uses partial switching of ferroelectric polarization to implement analog conductance modulation. For a ferroelectric material, an aluminum-doped hafnium oxide (Al-doped HfO) thin film was prepared by atomic layer deposition. As an analog synaptic device, our FeTFT successfully emulated short-term plasticity and long-term plasticity characteristics, such as paired-pulse facilitation and spike timing-dependent plasticity. In addition, we obtained potentiation/depression weight updates with high linearity, an on/off ratio, and low cycle-to-cycle variation by adjusting the amplitude and number of input pulses. In the simulation trained with optimized potentiation/depression conditions, we achieved a pattern recognition accuracy of approximately 90% for the Modified National Institute of Standard and Technology (MNIST) handwritten data set. Our results indicated that ferroelectric transistors can be used as an alternative artificial synapse.

摘要

神经形态计算因其能够克服当前冯·诺依曼计算系统的局限性而备受关注。模拟突触器件对于实现基于硬件的人工神经形态器件至关重要;然而,到目前为止,在突触材料和器件结构方面仅进行了少数系统研究,因此,需要在这个方向上进一步开展研究。在本研究中,我们展示了一种基于铁电材料的薄膜晶体管(FeTFT)的突触特性,该晶体管利用铁电极化的部分切换来实现模拟电导调制。对于铁电材料,通过原子层沉积制备了掺铝氧化铪(Al掺杂的HfO)薄膜。作为一种模拟突触器件,我们的FeTFT成功地模拟了短期可塑性和长期可塑性特性,如双脉冲易化和脉冲时间依赖可塑性。此外,通过调整输入脉冲的幅度和数量,我们获得了具有高线性度、开/关比和低周期到周期变化的增强/抑制权重更新。在用优化的增强/抑制条件进行训练的模拟中,对于修改后的美国国家标准与技术研究院(MNIST)手写数据集,我们实现了约90%的模式识别准确率。我们的结果表明,铁电晶体管可以用作替代人工突触。

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