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二氧化硅/氟化镁基超低能耗相变集成光子器件

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相变集成光子器件具有带宽大、延迟低、多路复用和抗干扰性好等性能优势,因此被广泛视作传统电子器件的有力竞争者.然而,当前相变光子器件编程所需的能耗较高,从而对其商业应用前景造成了严重的损害.为了解决这一问题,本文创造性地提出了一种非常有前景的二氧化硅(SiO2)/氟化镁(MgF2)基光子架构以取代当前主流的硅基器件.该器件采用目前已得到广泛应用的相变材料Ge2Sb2Te5(GST)和氧化铟锡(ITO)合金分别作为功能层和片上加热器材料,并通过自主开发的电热和相场耦合模型实现其数据编程和读取过程的模拟.仿真结果表明该器件在晶化和非晶化过程中所产生的能耗分别为78 aj/nm3和90 aj/nm3,远低于其他大多数硅基器件.同时其在近红外波段(如1 550 nm)也保持了良好的光学传输特性,并展现出超过5个中间态的多值存储特性和50 ns的短脉宽编程时间.除此之外,进一步研究表明使用该器件所搭建的光学神经网络可用于鸢尾花数据集识别,其准确率高达90%,接近于传统人工神经网络的识别准确率(约为94.7%).上述工作为具有低功耗、存内计算和神经形态计算功能的新兴相变光子器件开发提供了新的研究思路,对于实现一种兼具电子和全光信息器件性能优势的通用非冯诺依曼计算体系有着重大的意义.
Ultralow Energy Phase-Change Integrated Photonics Devices with the Silicon Dioxide/Magnesium Fluoride Platform
Phase-change integrated photonic devices are widely considered as a strong competitor to conventional electronic devices due to their large bandwidth,short delay,multiplexing and great anti-interference.However,current phase-change integrated photonic devices require high energy consumption,thus severely exacerbating its commercial appli-cation prospect.To address this issue,this paper innovatively proposed a promising silicon dioxide(SiO2)/magnesium fluo-ride(MgF2)based photonic architecture to replace the mainstream silicon based devices.Such device made use of the Ge2Sb2Te5(GST)and indium tin oxide(ITO)as the functional and microheater materials,respectively,which have received widespread applications today,and simulated its programming and readout process according to an independently devel-oped model that coupled electro-thermal and phase-change field processes.Results indicated that the energy consumption for crystallization and amorphization were 78 aj/nm3 and 90 aj/nm3,much lower than majority of other silicon-based devic-es.It also exhibited good light propagation trait at near-infrared band(1 550 nm),as well as multilevel characteristic with more than 5 intermediate states and short pulse width with 50 ns.Additionally,further research suggested that the photonic neural networks constructed from the proposed device can be used to recognize the iris dataset,and its accuracy can reach 90%,close to that of conventional neural networks(~94.7%).Aforementioned work provided for the new strategy for devel-oping emerging phase-change photonic devices with low power,in-memory computing and neuromorphic computing func-tionalities,and exhibited its extremely important significance to the general non von-Neumann regime that has both elec-tronic and photonic performance superiorities.

PCMmicroheaterphotonicenergy consumption

连晓娟、蒋纪元、万相、肖宛昂、王磊

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南京邮电大学集成电路科学与工程学院(产教融合学院),江苏 南京 210023

中国科学院半导体研究所,北京 100083

中国科学院大学材料与光电研究中心&集成电路学院,北京 100083

江苏集萃智能集成电路设计技术研究所有限公司,江苏 无锡 214028

南京邮电大学南通研究院,江苏 南通 226021

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相变材料 电加热 光学 能耗

2024

电子学报
中国电子学会

电子学报

CSTPCD北大核心
影响因子:1.237
ISSN:0372-2112
年,卷(期):2024.52(11)