Department of Materials Science and Engineering , Pohang University of Science and Technology (POSTECH) , Pohang 37673 , Korea.
Nano Lett. 2019 Mar 13;19(3):2044-2050. doi: 10.1021/acs.nanolett.9b00180. Epub 2019 Feb 6.
Neuromorphic computing is a promising alternative to conventional computing systems as it could enable parallel computation and adaptive learning process. However, the development of energy efficient neuromorphic hardware systems has been hindered by the limited performance of analog synaptic devices. Here, we demonstrate the analog conductance modulation behavior in the ferroelectric thin-film transistors (FeTFT) that have the nanoscale ferroelectric material and oxide semiconductors. Accurate control of polarization changes in the nanoscale ferroelectric layer induces conductance modulation to demonstrate linear potentiation and depression characteristics of FeTFTs. Our devices show potentiation and depression properties, including high linearity, multiple states, and small cycle-to-cycle/device-to-device variations. In simulations with measured properties, a neuromorphic system with FeTFT achieves 91.1% recognition accuracy of handwritten digits. This work may provide a way to realize the neuromorphic hardware systems that use FeTFTs as the synaptic devices.
神经形态计算是一种有前途的替代传统计算系统的方法，因为它可以实现并行计算和自适应学习过程。然而，由于模拟突触器件的性能有限，能量高效的神经形态硬件系统的发展一直受到阻碍。在这里，我们展示了具有纳米级铁电材料和氧化物半导体的铁电薄膜晶体管（FeTFT）中的模拟电导调制行为。纳米级铁电层中极化变化的精确控制诱导电导调制，以展示 FeTFT 的线性增强和抑制特性。我们的器件表现出增强和抑制特性，包括高线性度、多个状态和小的循环间/器件间变化。在使用测量特性的模拟中，具有 FeTFT 的神经形态系统对手写数字的识别准确率达到 91.1%。这项工作可能为实现使用 FeTFT 作为突触器件的神经形态硬件系统提供一种方法。
