CN106653850A - 基于石墨烯/碳纳米管复合吸收层的人工神经突触晶体管 - Google Patents

基于石墨烯/碳纳米管复合吸收层的人工神经突触晶体管 Download PDF

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CN106653850A
CN106653850A CN201610848037.9A CN201610848037A CN106653850A CN 106653850 A CN106653850 A CN 106653850A CN 201610848037 A CN201610848037 A CN 201610848037A CN 106653850 A CN106653850 A CN 106653850A
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王枫秋
秦书超
刘玉杰
徐永兵
张�荣
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Abstract

本发明公开了一种基于石墨烯/碳纳米管复合吸收层的人工突触晶体管,该晶体管包括自下而上依次设置的衬底、栅极介质层、石墨烯/碳纳米管复合吸收层;所述石墨烯/碳纳米管复合吸收层包括至少一层石墨烯层和至少一层碳纳米管层,并且,至少一层石墨烯层与所述栅极介质层接触,所述石墨烯层的两端分别设有源极、漏极,且所述碳纳米管层不与所述源极、漏极接触;将所述的晶体管作为光学神经元系统的基本单元,将多个晶体管进行阵列集成,通过引线与外部测量设备相互连接形成智能光学神经元系统。利用该神经系统分布式存储、并行运算和自适应学习等优势,能够快速有效地进行图像识别。本发明还提供了通过该系统实现信息存储和图像识别的具体方案。

Description

基于石墨烯/碳纳米管复合吸收层的人工神经突触晶体管
技术领域
本发明有关于一种基于石墨烯/碳纳米管复合吸收层的人工神经突触晶体管,属于人工智能领域。
背景技术
神经元芯片是模拟生物大脑结构和功能进行运算的构架,相比传统冯·诺依曼计算机,具有经验学习、强容错性和自适应性等能力,在模式识别、感知和在复杂环境中作决策等方面展现出独有的优势,在探寻新的信息表示、存储、并行运算和模式识别等领域具有极高的研究应用价值。
神经突触(Synapses)是神经元间进行信息传递的唯一结点,其可塑性指神经元之间的连接效率,根据其记忆时间的长短可分为短时可塑性(Short-term plasticity,STP)和长时可塑性(Long-term plasticity,LTP),在心理学上分别对应短时记忆和长时记忆,这正是神经元系统进行运算、学习和记忆的基础。
目前,关于人工神经突触和神经网络系统的构建和模拟,大多在晶体管(transistors)或忆阻器(memorisistors)结构中实现。但对于电激励模拟形式的晶体管或忆阻器,其输入和输出电信号间的耦合系数是固定的,不利于实现复杂的运算功能。为了解决这类问题,有研究组采用性能可调的材料进行器件结构设计,如:利用扭转双层石墨烯构建场效应晶体管(Tian, H. et al. Graphene dynamic synapse with modulatableplasticity. Nano Lett. 15, 8013-8019 (2015).),在同一个器件中实现了神经元的兴奋和抑制两种功能,并且通过调节栅压控制了突触可塑性。但真正的神经元系统,是集数据采集和信息处理于一体的,目前人工突触中数据感知模块的缺失将导致大量沉冗电路和非必要功耗产生,因此也限制了人工神经元系统的构建。
发明内容
为了解决以上问题,本发明的目的在于提供一种基于石墨烯/碳纳米管复合吸收层的人工神经突触晶体管。本发明还具体的提供了关于石墨烯/碳纳米管复合吸收层的人工神经突触晶体管在光信息存储或图像识别上的应用。
为达到上述目的,本发明首先提供了一种基于石墨烯碳纳米管复合吸收层的人工突触晶体管,其中,该晶体管包括衬底、栅极介质层、石墨烯/碳纳米管复合吸收层、源极和漏极。
根据本发明的具体实施方案,在上述人工突触晶体管中,衬底的材料可以是本领域中制备衬底所常用的材料,优选地,所述衬底的材料为半导体材料。衬底可选用但不限于硅片、蓝宝石等硬质或柔性衬底,例如采用高掺杂p型Si衬底,用于支撑石墨烯/碳纳米管复合吸收层光探测晶体管结构。
根据本发明的具体实施方案,在上述人工突触晶体管中,所述石墨烯/碳纳米管复合吸收层中的碳纳米管包括单壁碳纳米管、双壁碳纳米管、多壁碳纳米管、金属性碳纳米管、半导体性碳纳米管中的一种或几种的组合,其中,碳纳米管层厚度为1-10nm。
根据本发明的具体实施方案,在上述人工突触晶体管中,所述石墨烯/碳纳米管复合吸收层中的石墨烯为单层石墨烯、双层石墨烯或少层石墨烯;优选地,所述少层石墨烯的层数为单层或小于(含)10层。石墨烯层所采用的石墨烯可以是掺杂的或未掺杂的,可以通过包括机械剥离、化学气相沉积等工艺来制备。
根据本发明的具体实施方案,在上述人工突触晶体管中,所述源极与漏极分别包括两层金属,其下层金属与所述石墨烯层接触;优选地,所述源极与漏极的下层金属不同,可以分别选用钛、钯、铬、和镍中的两种组合;更优选地,所述源极和漏极的总厚度为20-50nm,单层金属层的厚度至少为3nm。
根据本发明的具体实施方案,具体制备方法可采用常规方式:栅极介质层可通过热氧化法形成在衬底表面;碳纳米管层可以制成悬浊液并通过旋涂技术沉积在栅极介质层表面;石墨烯可以是常规方法制备并通过PMMA辅助的方法转移到碳纳米管层的表面;源极和漏极可以通过光刻法、lifi-off工艺、电子蒸发法形成于石墨烯的表面;在石墨烯的表面可以通过光刻制备出石墨烯沟道,并采用氧等离子体技术除去边缘石墨烯。当具有多层石墨烯、碳纳米管时,重复进行相关的制备步骤即可。
采用激光作为激励源,碳纳米管层为主要光吸收层,石墨烯层为辅助光吸收层并作为光生载流子的输运沟道。
碳纳米管和石墨烯是碳材料的同素异形体,它们可通过π-π键合的方式形成界面,将有助于载流子的输运。碳纳米管和石墨烯响应波长范围覆盖红外波段,能很好地分离光生电子-空穴对。碳纳米管和石墨烯都有优良的电学性质,载流子迁移率较高,器件的响应速度快。而且,由于碳纳米管和石墨烯的大规模制备技术趋于成熟,本发明的器件可进行规模化制备,成本也将更低。
根据本发明的具体实施方案,可通过控制栅极电压精确调节石墨烯沟道中载流子的浓度,进而调节器件对光的响应度。亦可采用金属叉指(interdigitated fingers)电极,增强有效光探测面积增加响应度。
本发明还提供了一种智能光学神经元系统,将上述人工突触晶体管构建成阵列,将所述阵列作为所述智能光学神经元系统的子单元。
在上述智能光学神经元系统,优选地,所述衬底下表面、所述源极和漏极的上表面分别设置有引出电极,所述引出电极通过电流测量器相互连接。
本发明还提供了上述基于石墨烯/碳纳米管复合吸收层的人工突触晶体管在光信息存储设备的应用。
本发明还提供了上述基于石墨烯/碳纳米管复合吸收层的人工突触晶体管在图像识别中的应用。
与现有技术相比,本发明的优点至少在于:将数据采集和信息处理有效结合,能够有效降低功耗;利用石墨烯线性能带色散关系,通过控制栅压获得可调突触塑性,达到响应可调。
附图说明
图1为实施例1提供的基于石墨烯/碳纳米管复合吸收层的人工突触晶体管的纵向剖面示意图。
图2为实施例1提供的基于石墨烯/碳纳米管复合吸收层的人工突触晶体管的FET(FET, Field Effect Transistor)示意图。
图3为基于石墨烯/碳纳米管复合吸收层的人工突触晶体管在光照下的转移曲线。
图4为实施例2提供的基于石墨烯/碳纳米管复合吸收层的智能光学神经元系统的光存储设备示意图。
图5为实施例3提供的基于石墨烯/碳纳米管复合吸收层的智能光学神经元系统的模式识别流程示意图。
具体实施方式
下面结合附图对本发明作进一步详细描述。
实施例1:本实施例提供了一种基于石墨烯/碳纳米管复合吸收层的人工突触晶体管的设计方案。结合图1所示,具体步骤如下:
A、在衬底101(p型重掺杂的硅片)上面采用热氧化方法制备300nm厚二氧化硅层作为栅极介质层102,采用丙酮、异丙醇、去离子水分别超声清洗10分钟;
B、碳纳米管悬浊液旋涂在介质层上,室温晾干形成碳纳米管层103;
C、在碳纳米管层上采用PMMA辅助的方法,转移在铜箔上CVD法生长的单层石墨烯(104),在丙酮中室温浸泡至少12小时除去PMMA层,形成复合吸收层;
D、采用光刻方法,lift-off工艺,电子束蒸发制备源极的第一导电金属层105和第二导电金属层106(Cr/Au=10nm/40nm或Ti/Au=10nm/40nm或Pt/Au=10nm/40nm)和漏极的第一导电金属层107和第二导电金属层108(Cr/Au=10nm/40nm或Ti/Au=10nm/40nm或Pt/Au=10nm/40nm);
E、第二次光刻制备出石墨烯沟道,并采用氧等离子体技术除去边缘石墨烯;
在上述晶体管的源极、漏极和栅极引线,得到人工突触晶体管(FET, Field EffectTransistor),其结构如图2所示。
实施例二:本实施例提供了一种基于石墨烯/碳纳米管复合吸收层的智能光学神经元系统的光信息存储设备的设计方案。如图4所示,上述基于石墨烯/碳纳米管复合吸收层的人工突触晶体管203栅极通过保护电阻204连接位线201,漏极通过保护电阻204连接字线202,沟道电阻值可以通过测量仪表设备205结合信号处理电路读出。该光存储设备包括阵列分布的多个人工突触晶体管203及相关部件,相应位线201和字线202也可以适宜布置多个,通过信号处理电路读出阵列内所有晶体管中电阻值,进行信息的存储。
实施例三:本实施例提供了一种基于石墨烯/碳纳米管复合吸收层的智能光学神经元系统的图像识别设计方案。如图5,场景图像通过智能光学神经元系统进行采集、处理和特征参数存储组成图像数据库,再次进行图像采集处理后进行特征参数比对,进行图像识别。

Claims (10)

1.一种基于石墨烯碳纳米管复合吸收层的人工突触晶体管,其特征在于,该晶体管包括自下而上依次设置的衬底、栅极介质层、石墨烯/碳纳米管复合吸收层;
所述石墨烯/ 碳纳米管复合吸收层包括至少一层石墨烯层和至少一层碳纳米管层,
并且,至少一层石墨烯层与所述栅极介质层接触,所述石墨烯层的两端分别设有源极、漏极,且所述碳纳米管层不与所述源极、漏极接触。
2.如权利要求1所述的晶体管,其特征在于,所述衬底的材料为半导体材料。
3.如权利要求1所述的晶体管,其特征在于,所述石墨烯/碳纳米管复合吸收层中的碳纳米管包括单壁碳纳米管、双壁碳纳米管、多壁碳纳米管、金属性碳纳米管、半导体性碳纳米管中的一种或几种的组合,其中,碳纳米管层厚度为1-10nm。
4.如权利要求1所述的晶体管,其特征在于,所述石墨烯/碳纳米管复合吸收层中的石墨烯为单层石墨烯、双层石墨烯或少层石墨烯。
5.如权利要求1所述的晶体管,其特征在于,所述源极与漏极均包括两层材质不同的导电金属层,其中一层导电金属层与石墨烯层接触,所述的与石墨烯层相接触的源极与漏极的导电金属层其材质不同,所述源极和漏极的总厚度为20-50nm,一层导电金属层的厚度至少为3nm。
6.如权利要求5所述的晶体管,其特征在于,导电金属层的材质为钛、钯、铬、和镍中的任意一种。
7.一种智能光学神经元系统,其特征在于,将权利要求1-5任一所述的晶体管构建成阵列,将所述阵列作为所述智能光学神经元系统的子单元。
8.如权利要求7所述的智能光学神经元系统,其特征在于,衬底下表面、源极和漏极的上表面分别设置有引出电极,所述引出电极通过电流测量器相互连接。
9.如权利要求1-5任一所述的晶体管在光信息存储设备中的应用。
10.如权利要求1-5任一所述的晶体管在在图像识别中的应用。
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