Bio-Inspired Neurons Based on Novel Leaky-FeFET with Ultra-Low Hardware Cost and Advanced Functionality for All-Ferroelectric Neural Network

C. Chen, M. Yang, S. Liu, T. Liu, K. Zhu, Y. Zhao, H. Wang, Q. Huang, R. Huang
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引用次数: 30

Abstract

For the brain-inspired neuromorphic computing, various emerging memory devices, including FeFET, have been applied to develop the artificial synapses, while the artificial neurons are still mostly CMOS-implemented and suffer from high-hardware-cost issue, especially when expanding advanced functions. In this work, a novel leaky-FeFET (L-FeFET) based on partially crystallized $\text{Hf}_{05}\text{z}_{\text{r}05}\text{O}_{2}$ layer is designed to mimic biological neurons. For the first time, we propose and experimentally demonstrate a capacitor-less L-FeFET neuron for basic leaky-integrate-and-fire function with ultra-low hardware cost of only one transistor and one resistor. Furthermore, a new hybrid L-FeFET-CMOS neuron is implemented to expand advanced spike-frequency adaption with almost half of hardware cost compared with CMOS neuron. This work provides a highly-integrated and inherently-low-energy implementation for neuron and the possibility for all-ferroelectric neural networks.
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全铁电神经网络中基于新型漏场效应晶体管的超低硬件成本和先进功能的仿生神经元
对于脑启发的神经形态计算,包括场效应晶体管在内的各种新兴存储器件已被应用于人工突触的开发,而人工神经元仍然主要是cmos实现的,并且存在硬件成本高的问题,特别是在扩展高级功能时。在这项工作中,设计了一种基于部分结晶的$\text{Hf}_{05}\text{z}_{\text{r}05}\text{O}_{2}$层的新型泄漏- fefet (L-FeFET)来模拟生物神经元。我们首次提出并实验演示了一种无电容的l - ffet神经元,用于基本的漏积点火功能,其硬件成本极低,只有一个晶体管和一个电阻。此外,还实现了一种新的l - fet -CMOS混合神经元,以扩展先进的尖峰频率自适应,硬件成本几乎是CMOS神经元的一半。这项工作为神经元提供了一种高度集成和固有低能量的实现,并为全铁电神经网络提供了可能性。
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