应变双极电荷等离子体晶体管作为高速LIF神经元

IF 3 Q2 PHYSICS, CONDENSED MATTER Micro and Nanostructures Pub Date : 2025-07-01 Epub Date: 2025-03-09 DOI:10.1016/j.micrna.2025.208127
Priyanka , Sangeeta Singh , Meena Panchore
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引用次数: 0

摘要

对于神经形态计算和生物逼真动力学,不同的CMOS器件已经被采用。但是,大多数用于开发人工突触和模拟LIF神经元动力学的设备都存在高功耗、低运行速度和高硬件实现成本的问题。在本研究中,漏电集成和火焰神经网络是利用一种基于浮体机制的应变双极电荷等离子体晶体管实现的。这是第一次,紧张的BCPT神经元被证明可以模拟生物行为,这提供了一个固有的低能量,成本效益和易于实现的神经元。与相变CMOS、SOI CMOS、PCMO CMOS、Biristor、Bulk FinFET、基于BTBT、FBFET、LBIMOS、DGJLFET和硅纳米线的PD SOI CMOS相比,该神经元的峰值能量分别为1.53 × 105、1.79 × 105、5.1 × 104、2.9 × 104、32.1、16.4、1.28 × 103、918、5820和14.8倍。本文还研究了温度、碱基宽度、锗摩尔分数和金属电极功函数的影响。0.30 V的集电极电位足以产生8 μ a /μm的阈值峰值电流,比SOI CMOS、Bulk FinFET、Si NIPIN、PD SOI MOS、LBIMOS和DGJLFET分别小9.33、10、2.66、5、6.66和1.33倍。在这里,受应变的BCPT浮体负责撞击电离产生的空穴积累(II)。所提出的神经元装置使用SILVACO 2D TCAD仿真工具演示了12ghz频率下LIF动力学的基本功能。因此,这项工作提供了在高运行速度和低能耗下容易制造高度集成SNN的可能性。
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Strained bipolar charge plasma transistor as a high speed LIF neuron
For Neuromorphic computing and bio realistic dynamics, distinct CMOS devices have been carried out. But most devices used for developing artificial synapses and mimicking the LIF neuronal dynamics suffer from high power dissipation, low operating speed, and high cost of hardware implementation. In this work, leaky integrate and fire neural are implemented using a strained bipolar charge plasma transistor based on a floating body mechanism. For the first time, strained BCPT neuron is demonstrated to mimic biological behavior which provides an inherently low-energy, cost-effective, and easy implementation of the neuron. The strained BCPT based neuron exhibits maximum spiking energy of 196 aJ which is 1.53 × 105, 1.79 × 105, 5.1 × 104, 2.9 × 104, 32.1, 16.4, 1.28 × 103, 918, 5820, 14.8 times less as compared to phase change CMOS, SOI CMOS, PCMO CMOS, Biristor, Bulk FinFET, PD SOI CMOS based on BTBT, FBFET, LBIMOS, DGJLFET, and Silicon nanowire. This work also investigates the effect of temperature, base width, germanium mole fraction, and metal electrode work function. The collector potential of 0.30 V is enough to produce a threshold spike current of 8 rmμA/μm which is 9.33, 10, 2.66, 5, 6.66, and 1.33 times less as compared to SOI CMOS, Bulk FinFET, Si NIPIN, PD SOI MOS, LBIMOS, and DGJLFET, respectively. Here, the strained BCPT floating body is responsible for the accumulation of holes generated by impact ionization (II). The proposed neuron device demonstrates the basic function of LIF dynamics at 12 GHz frequency using the SILVACO 2D TCAD simulation tool. Hence, this work provides the possibility of easy fabrication of highly-integrated SNN at high operating speed and low-energy consumption.
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