With ability to process data in an energy-efficient way, neuromorphic computing is suggested to overcome the issues of a traditional von Neumann computing system. Neuromorphic computing is composed of two crucial features of neurons and synapses, in which neurons integrate all the charges while synapses retain these charges. In this paper, we fabricate and analyze devices for mimicking neurons and synapses in a single Si-based metal-oxide-semiconductor field-effect transistor (MOSFET) structure. We fabricate and analyze Al₂O₃/Si₃N₄ (A/N) and Al₂O₃/HfO₂/Si₃N₄/SiO₂ (A/H/N/O) devices to suggest that an A/N device could be used as a neuron device due to its fast charge emission characteristics, while the A/H/N/O device could be used as a synaptic device as added tunneling SiO₂ causes the device to retain its charges for a long-period of time. We suggest the possibility of fabricating both neurons and synapses by adopting different gate insulator stack structures in MOSFETs.

中文翻译：

---

使用高κ栅极绝缘子堆栈的场效应晶体管型器件用于神经形态应用

具有以节能方式处理数据的能力，建议使用神经形态计算来克服传统冯·诺依曼计算系统的问题。神经形态计算由神经元和突触的两个关键特征组成，其中神经元整合所有电荷，而突触保留这些电荷。在本文中，我们制造和分析了用于在单个基于硅的金属氧化物半导体场效应晶体管（MOSFET）结构中模拟神经元和突触的设备。我们制造和分析Al ₂ O ₃ / Si ₃ N ₄（A / N）和Al ₂ O ₃ / HfO ₂ / Si ₃ N ₄ / SiO ₂（A / H / N / O）设备建议A / N设备由于其快速的电荷发射特性而可以用作神经元设备，而A / H / N / O设备可以用作突触设备由于添加了隧穿SiO _2，导致该器件长时间保持电荷。我们建议通过在MOSFET中采用不同的栅极绝缘体堆叠结构来制造神经元和突触的可能性。