Kim Yijoon, Kim Hyangwoo, Oh Kyounghwan, Park Ju Hong, Kong Byoung Don, Baek Chang-Ki
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
Future IT Innovation Laboratory, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
Discov Nano. 2024 Aug 23;19(1):132. doi: 10.1186/s11671-024-04079-5.
We have proposed leaky integrate-and-fire (LIF) neuron having low-energy consumption and tunable functionality without external circuit components. Our LIF neuron has a simple configuration consisting of only three components: one bandgap-engineered resistive switching transistor (BE-RST), one capacitor, and one resistor. Here, the crucial point is that BE-RST with a silicon-germanium heterojunction possesses an amplified hysteric current switching with a low latch-up voltage due to improved hole storage capability and impact ionization coefficient. Therefore, the proposed neuron utilizing BE-RST requires an energy consumption of 0.36 pJ/spike, which is approximately six times lower than 2.08 pJ/spike of pure silicon-RST based neuron. In addition, the spiking properties can be tuned by modulating the leakage rate and threshold through gate bias, which contributes to energy-efficient sparse-activity and high learning accuracy. As a result, our proposed neuron can be a promising candidate for executing various spiking neural network applications.
我们提出了一种无需外部电路元件即可实现低能耗和功能可调的漏电积分发放(LIF)神经元。我们的LIF神经元具有简单的结构,仅由三个组件组成:一个带隙工程化电阻开关晶体管(BE-RST)、一个电容器和一个电阻器。在此,关键在于具有硅锗异质结的BE-RST由于空穴存储能力和碰撞电离系数的提高,具有放大的滞后电流开关特性和低闩锁电压。因此,所提出的利用BE-RST的神经元的能量消耗为0.36 pJ/尖峰,比基于纯硅-RST的神经元的2.08 pJ/尖峰低约六倍。此外,通过栅极偏置调制泄漏率和阈值可以调整尖峰特性,这有助于实现节能稀疏活动和高学习精度。结果,我们提出的神经元有望成为执行各种脉冲神经网络应用的候选者。