Al-based memristor applied to habituation sensory nervous system

Zhu Wei; Guo Tian-Tian; Liu Lan; Zhou Rong-Rong

doi:10.7498/aps.70.20201961

Abstract

Sensory nervous system (SNS) can build the connections between organism and outside environment. Both of synapse and neuron are cornerstones of human biological system, which can transmit information to human brain and receive the feedback from central nervous system. Finally, the corresponding responses to the external information are performed. However, the information from outside environment should be received by SNS all the time. It is important for organism to distinguish between the stimuli that required attention and those that are irrelevant and no need to response. Habituation is one of fundamental properties of SNS to form such discrimination. It plays an important role for organism to adapt the environment and filter out irrelevantly repetitive information. In this study, an nc-Al/AlN structured based memristor with a thickness of 40 nm is produced by the sputtering method. The top and bottom electrode are of Ag and Al respectively, forming a sandwiched structure device. Habituation is found in the nc-Al/AlN thin film based memristor which has been rarely reported before. Both of current-voltage (I-V ) and pulse voltage measurement are executed on this device at room temperature. In the I-V measurement, the memristor shows unipolar switching properties which may be caused by conductive filament connecting or breaking. In the voltage pulse measurement, pulse interval is an important factor to affect memristor conduction. If the pulse interval is quite large, that is, the pulse frequency is low, the memristor will get maximized conduction very slow or in infinity time. If choosing an appropriate pulse voltage and interval value, the habituation will be observed after several stimulus pulses. The larger pulse interval needs more pulse numbers to cause memristor to be habituated, but which results in higher device conduction finally. A habituation memristor can act as synapse and connect with neuron to build the whole leaky integrate-and-fire (LIF) model which is quite often used in circuit design to mimic a real organism neuron behavior. In this model, neuron could be fired only when it gets enough stimuli from previous neuron. If the stimulus pulse frequency is low, there is observed no firing phenomenon in this case. In this study, the input signal of LIF model is a continuous voltage pulse with an amplitude of 1.2 V and interval of 5 ms. Such an input signal will be transmitted by habituation memristor to a neuron electronic element. The output signal is the pulse generated by neuron when it is fired. According to the results, the frequency of output signal is smaller than input information which complies with the basic characteristics of habituation. It is supposed that organisms should not response to this repetitive pulse any more and it will make neuron have more capabilities to handle following information.

Keywords:

Authors and contacts

School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China

Corresponding author: Zhu Wei, wzhu@chd.edu.cn

Funds: Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61704010) and the Natural Science Foundation of Shaanxi Province, China (Grant No. 2020JQ-341)

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References

[1]	Yamins D L, DiCarlo J J 2016 Nat. Neurosci. 19 356 Google Scholar
[2]	Wan Q, Jiang X Y, Andreea M N, Lu S G, Kimberly S M, Thomas W A 2012 Nat. Neurosci. 15 1144 Google Scholar
[3]	Thompson R F, Spencer W A 1966 Psychol. Rev. 73 16 Google Scholar
[4]	Rankin C H, Abrams T, Barry R J, Bhatnagar S, Clayton D F, Colombo J, Coppola G, Geyer M A, Glanzman D L, Marsland S, McSweeney F K, Wilson D A, Wu C F, Thompson R F 2009 Neurobiol. Learn. Mem. 92 135 Google Scholar
[5]	Fu T D, Liu X M, Gao H Y, Ward J E, Liu X R, Yin B, Wang Z R, Zhuo Y, David J F Walker, Joshua Yang J, Chen J H, Derek R L, Yao J 2020 Nat. Commun. 11 1861 Google Scholar
[6]	Zhong Y N, Wang T, Gao X, Xu J L, Wang S D 2018 Adv. Funct. Mater. 28 1800854 Google Scholar
[7]	Wan C J, He Y L, Jiang S S, Li J F, Wan Q 2020 Adv. Electron. Mater. 26 389 Google Scholar
[8]	刘益春, 林亚, 王中强, 徐海阳 2019 物理学报 68 168504 Google Scholar Liu Y C, Lin Y, Wang Z Q, Xu H Y 2019 Acta Phys. Sin. 68 168504 Google Scholar
[9]	邵楠, 张盛兵, 邵舒渊 2016 物理学报 65 128503 Google Scholar Shao N, Zhang S B, Shao S Y 2016 Acta Phys. Sin. 65 128503 Google Scholar
[10]	Zhu X J, Wang Q W, Wei D L 2020 Nat. Commun. 11 2439 Google Scholar
[11]	Dang B J, Wu Q T, Song F, Sun J, Yang M, Ma X H, Wang H, Hao Y 2018 Nanoscale 10 20089 Google Scholar
[12]	Yang X, Fang Y C, Yu Z Z, Wang Z W, Zhang T, Yin M H, Lin M, Yang Y C, Cai Y M, Huang R 2016 Nanoscale 8 18897 Google Scholar
[13]	Shi Y Y, Liang X H, Yuan B, Victoria C, Li H T, Hui F, Yu Z C W, Yuan F, Eric P, Philip W, Mario L 2018 Nat. Eletron. 1 458 Google Scholar
[14]	Yao P, Wu H Q, Gao B, Tang J S, Zhang Q T, Zhang W Q, Yang J J, He Q 2020 Nature 577 641 Google Scholar
[15]	Writam B, Liu Q, Lv H B, Long S B, Liu M 2017 Nanoscale 9 14442 Google Scholar
[16]	Wu Q T, Wang H, Luo Q, Writam B, Cao J C, Zhang X M, Wu F C, Liu Q, Li L, Liu M 2018 Nanoscale 10 5875 Google Scholar
[17]	Pan F, Gao S, Chen C, Song C, Zeng F 2014 Mater. Sci. Eng. 83 1 Google Scholar
[18]	Gao S, Yi X H, Shang J 2019 Chem. Soc. Rev. 48 1531 Google Scholar
[19]	Yang X, Wang C, Shang J, Zhang C C, Tan H W, Yi X H, Pan L, Zhang W B, Fan F, Liu Y Q, Chen Y, Liu G, Li R W 2016 RSC Adv. 6 25179 Google Scholar
[20]	Ting Y, Chen J Y, Huang C W, Huang T K, Hsieh C Y, Wu W W 2018 Small 14 1703153 Google Scholar
[21]	Ella G 2014 Semicond. Sci. Technol. 29 104004 Google Scholar
[22]	Li C, Hu M, Li Y N, Jiang H, Ge N, Eric M, Zhang J M, Song W H, Noraica D, Catherin E G, Li Z Y, Strachan J P, Lin P, Wang Z R, Barnell M, Wu Q, Stanley Williams R, Joshua Yang J, Xia Q F 2018 Nat. Electron. 1 52 Google Scholar
[23]	Chen Y R, Song H, Jiang H, Li Z M, Zhang Z W, Sun X J, Li D B, Miao G Q 2014 Appl. Phys. Lett. 105 193502 Google Scholar
[24]	Kim H D, An H M, SeI Y, Kim T G 2011 IEEE Electron Device Lett. 32 1125 Google Scholar
[25]	Chen C, Gao S, Tang G S, Fu H D, Wang G Y, Song C, Zheng F, Pan F 2013 ACS Appl. Mater. Interfaces 5 1793 Google Scholar
[26]	Lin C C, Liou H Y, Chu S Y, Huang C Y, Hong C S 2018 Cryst. Eng. Commun. 20 6230 Google Scholar
[27]	Zhu W, Chen T P, Liu Y, Fung S 2012 J. Appl. Phys. 112 063706 Google Scholar
[28]	Zhu W, Chen T P, Yang M, Liu Y, Fung S 2012 IEEE Trans. Electron Devices 59 2363 Google Scholar
[29]	Gilmer D C, Veksler D, Kirsch P, Vandelli L, Padovani A, Larcher L, McKenna K, Shluger A, Iglesias Porti V M, Nafría M 2011 J. Appl. Phys. 110 124518 Google Scholar
[30]	Umberto C, Ludovic G, Attilio, Karl O, Alexis F, Andreas S, Christophe D, Olivier R, Hugo B, Malgorzata J, Wilfried V 2014 Nano Lett. 14 2401 Google Scholar
[31]	Pablo S, Julien T, Benoit C, Etienne J, Marie Paule B, Federico T, Marcelo R, Laurent C 2017 Adv. Funct. Mater. 27 1604740 Google Scholar
[32]	Yang J Q, Wang R P, Ren Y, Mao J Y, Wang Z P, Zhou Y, Han S T 2020 Adv. Mater. 2020 2003610 Google Scholar
[33]	Gao S, Zeng F, Chen C, Tang G S, Lin Y S, Zheng Z F, Song C, Pan F 2013 Nanotechnology 24 335201 Google Scholar
[34]	Gao S, Chen C, Zhai Z, Liu Y, Lin S, Li S, Lu S, Wang G, Song C, Zeng F, Pan F 2014 Appl. Phys. Lett. 6 063504 Google Scholar
[35]	Corinne T, Ramakrishnan I, Vilas M, Nathan G, David F, Jim B, Aaron S, Nicholas C, Zeng H K, Michael H, Christof K, Stefan M 2018 Nat. Commun. 9 709 Google Scholar
[36]	Chatterjee D, Kottantharayil A 2019 IEEE Electron Device Lett. 40 1301 Google Scholar

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图 1 铝基薄膜的TEM和XPS测试　(a) XPS测试(BE, 结合能); (b) TEM测试

Figure 1. The TEM and XPS results of nc-Al embedded AlN thin film: (a) XPS result (BE, binding energy); (b) TEM result.

DownLoad: Full-Size Img PowerPoint

图 2 忆阻器的电学特性和阻变原理　(a) 铝基薄膜忆阻器I-V测试显示单极(unipolar)特性; (b) 忆阻器的I-V测试在低电压时的幂律拟合; (c) HRS下器件内导电丝处于断开状态, LRS下器件内导电丝处于连接状态; (d) 器件HRS下的电荷传导机制

Figure 2. Electrical and resistive switching theory of memristor: (a) The I-V characteristic of memeristor shows unipolar property; (b) the power-law fitting of memeristor I-V measurement at low voltage region; (c) conductive filaments broken at HRS and connected at LRS; (d) conduction mechanisms of memristor at HRS.

DownLoad: Full-Size Img PowerPoint

图 3 忆阻器的脉冲电压测试显示了习惯化特性　(a)间隔越大导电率到达最大值所需的脉冲数量越多; 脉冲电压越大导电率到达最大值所需的脉冲数量越少, 如插图所示; (b)脉冲电压1.2 V, 间隔2 ms时器件感知和习惯化测试; (c) 脉冲电压1.2 V, 间隔5 ms时器件感知和习惯化测试; (d) 脉冲电压1.2 V, 间隔20 ms时器件感知和习惯化测试

Figure 3. Memristor exhibit habituation property in voltage pulse measurement: (a) The longer pulse interval need more pulse number to cause memristor conduction maximized and the larger pulse amplitude need less pulse number to cause memristor conduction maximized, as shown in inset figure; (b) learning and habituation measurement of memristor with pulse voltage 1.2 V and pulse interval 2 ms; (c) learning and habituation measurement of memristor with pulse voltage 1.2 V and pulse interval 5 ms; (d) learning and habituation measurement of memristor with pulse voltage 1.2 V and pulse interval 20 ms.

DownLoad: Full-Size Img PowerPoint

图 4 器件引起习惯化所需脉冲数量与脉冲间隔和脉冲电压幅值的关系　(a) 固定脉冲幅值和宽度, 脉冲间隔越大引起器件习惯化特性所需的脉冲数量越多, 负向脉冲比正向脉冲需要更多的脉冲数量引起器件习惯化; (b) 固定脉冲间隔和宽度, 脉冲电压越大引起器件习惯化特性所需的脉冲数量越少, 负向脉冲比正向脉冲需要更多的脉冲数量引起器件习惯化

Figure 4. Relationship of the number of pulses required for device habituation to the pulse interval and pulse voltage: (a) The relationship between the number of pulses required for device habituation and pulse interval with fixed pulse voltage and width; (b) the relationship between the number of pulses required for device habituation and pulse voltage with fixed pulse interval and width.

DownLoad: Full-Size Img PowerPoint

图 5 感觉神经系统的工作原理^[10]　(a) 神经系统结构图; (b) 神经系统的LIF模型; (c)频率较低的输入信号不能引起神经元的激发; (d) 频率较高的输入信号引起神经元激发

Figure 5. Working principle diagram of sensory nervous system (SNS)^[10]: (a) Structure of SNS; (b) LIF model of SNS; (c) no firing observed with low frequency input pulse signal; (d) firing of neuron observed with high frequency input pulse signal.

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图 6 神经系统LIF模型习惯化特性测试　(a) 含有习惯化特性忆阻器的LIF模型图; (b) 连续脉冲测试, 展示了习惯化特性对完整神经系统的影响

Figure 6. Habituation measurement of LIF model: (a) LIF model structure with habituation memristor; (b) the effect of habituation memristor in LIF model with continuous pulse stimuli.

DownLoad: Full-Size Img PowerPoint

[1]	Yamins D L, DiCarlo J J 2016 Nat. Neurosci. 19 356 Google Scholar
[2]	Wan Q, Jiang X Y, Andreea M N, Lu S G, Kimberly S M, Thomas W A 2012 Nat. Neurosci. 15 1144 Google Scholar
[3]	Thompson R F, Spencer W A 1966 Psychol. Rev. 73 16 Google Scholar
[4]	Rankin C H, Abrams T, Barry R J, Bhatnagar S, Clayton D F, Colombo J, Coppola G, Geyer M A, Glanzman D L, Marsland S, McSweeney F K, Wilson D A, Wu C F, Thompson R F 2009 Neurobiol. Learn. Mem. 92 135 Google Scholar
[5]	Fu T D, Liu X M, Gao H Y, Ward J E, Liu X R, Yin B, Wang Z R, Zhuo Y, David J F Walker, Joshua Yang J, Chen J H, Derek R L, Yao J 2020 Nat. Commun. 11 1861 Google Scholar
[6]	Zhong Y N, Wang T, Gao X, Xu J L, Wang S D 2018 Adv. Funct. Mater. 28 1800854 Google Scholar
[7]	Wan C J, He Y L, Jiang S S, Li J F, Wan Q 2020 Adv. Electron. Mater. 26 389 Google Scholar
[8]	刘益春, 林亚, 王中强, 徐海阳 2019 物理学报 68 168504 Google Scholar Liu Y C, Lin Y, Wang Z Q, Xu H Y 2019 Acta Phys. Sin. 68 168504 Google Scholar
[9]	邵楠, 张盛兵, 邵舒渊 2016 物理学报 65 128503 Google Scholar Shao N, Zhang S B, Shao S Y 2016 Acta Phys. Sin. 65 128503 Google Scholar
[10]	Zhu X J, Wang Q W, Wei D L 2020 Nat. Commun. 11 2439 Google Scholar
[11]	Dang B J, Wu Q T, Song F, Sun J, Yang M, Ma X H, Wang H, Hao Y 2018 Nanoscale 10 20089 Google Scholar
[12]	Yang X, Fang Y C, Yu Z Z, Wang Z W, Zhang T, Yin M H, Lin M, Yang Y C, Cai Y M, Huang R 2016 Nanoscale 8 18897 Google Scholar
[13]	Shi Y Y, Liang X H, Yuan B, Victoria C, Li H T, Hui F, Yu Z C W, Yuan F, Eric P, Philip W, Mario L 2018 Nat. Eletron. 1 458 Google Scholar
[14]	Yao P, Wu H Q, Gao B, Tang J S, Zhang Q T, Zhang W Q, Yang J J, He Q 2020 Nature 577 641 Google Scholar
[15]	Writam B, Liu Q, Lv H B, Long S B, Liu M 2017 Nanoscale 9 14442 Google Scholar
[16]	Wu Q T, Wang H, Luo Q, Writam B, Cao J C, Zhang X M, Wu F C, Liu Q, Li L, Liu M 2018 Nanoscale 10 5875 Google Scholar
[17]	Pan F, Gao S, Chen C, Song C, Zeng F 2014 Mater. Sci. Eng. 83 1 Google Scholar
[18]	Gao S, Yi X H, Shang J 2019 Chem. Soc. Rev. 48 1531 Google Scholar
[19]	Yang X, Wang C, Shang J, Zhang C C, Tan H W, Yi X H, Pan L, Zhang W B, Fan F, Liu Y Q, Chen Y, Liu G, Li R W 2016 RSC Adv. 6 25179 Google Scholar
[20]	Ting Y, Chen J Y, Huang C W, Huang T K, Hsieh C Y, Wu W W 2018 Small 14 1703153 Google Scholar
[21]	Ella G 2014 Semicond. Sci. Technol. 29 104004 Google Scholar
[22]	Li C, Hu M, Li Y N, Jiang H, Ge N, Eric M, Zhang J M, Song W H, Noraica D, Catherin E G, Li Z Y, Strachan J P, Lin P, Wang Z R, Barnell M, Wu Q, Stanley Williams R, Joshua Yang J, Xia Q F 2018 Nat. Electron. 1 52 Google Scholar
[23]	Chen Y R, Song H, Jiang H, Li Z M, Zhang Z W, Sun X J, Li D B, Miao G Q 2014 Appl. Phys. Lett. 105 193502 Google Scholar
[24]	Kim H D, An H M, SeI Y, Kim T G 2011 IEEE Electron Device Lett. 32 1125 Google Scholar
[25]	Chen C, Gao S, Tang G S, Fu H D, Wang G Y, Song C, Zheng F, Pan F 2013 ACS Appl. Mater. Interfaces 5 1793 Google Scholar
[26]	Lin C C, Liou H Y, Chu S Y, Huang C Y, Hong C S 2018 Cryst. Eng. Commun. 20 6230 Google Scholar
[27]	Zhu W, Chen T P, Liu Y, Fung S 2012 J. Appl. Phys. 112 063706 Google Scholar
[28]	Zhu W, Chen T P, Yang M, Liu Y, Fung S 2012 IEEE Trans. Electron Devices 59 2363 Google Scholar
[29]	Gilmer D C, Veksler D, Kirsch P, Vandelli L, Padovani A, Larcher L, McKenna K, Shluger A, Iglesias Porti V M, Nafría M 2011 J. Appl. Phys. 110 124518 Google Scholar
[30]	Umberto C, Ludovic G, Attilio, Karl O, Alexis F, Andreas S, Christophe D, Olivier R, Hugo B, Malgorzata J, Wilfried V 2014 Nano Lett. 14 2401 Google Scholar
[31]	Pablo S, Julien T, Benoit C, Etienne J, Marie Paule B, Federico T, Marcelo R, Laurent C 2017 Adv. Funct. Mater. 27 1604740 Google Scholar
[32]	Yang J Q, Wang R P, Ren Y, Mao J Y, Wang Z P, Zhou Y, Han S T 2020 Adv. Mater. 2020 2003610 Google Scholar
[33]	Gao S, Zeng F, Chen C, Tang G S, Lin Y S, Zheng Z F, Song C, Pan F 2013 Nanotechnology 24 335201 Google Scholar
[34]	Gao S, Chen C, Zhai Z, Liu Y, Lin S, Li S, Lu S, Wang G, Song C, Zeng F, Pan F 2014 Appl. Phys. Lett. 6 063504 Google Scholar
[35]	Corinne T, Ramakrishnan I, Vilas M, Nathan G, David F, Jim B, Aaron S, Nicholas C, Zeng H K, Michael H, Christof K, Stefan M 2018 Nat. Commun. 9 709 Google Scholar
[36]	Chatterjee D, Kottantharayil A 2019 IEEE Electron Device Lett. 40 1301 Google Scholar

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