Abstract
Synaptic devices that merge memory and processing functions into one unit have broad application potentials in neuromorphic computing, soft robots, and human-machine interfaces. However, most previously reported synaptic devices exhibit fixed performance once been fabricated, which limits their application in diverse scenarios. Here, we report floating-gate photosensitive synaptic transistors with charge-trapping perovskite quantum dots (PQDs) and atomic layer deposited (ALD) Al2O3 tunneling layers, which exhibit typical synaptic behaviors including excitatory postsynaptic current (EPSC), pair-pulse facilitation and dynamic filtering characteristics under both electrical or optical signal stimulation. Further, the combination of the high-quality Al2O3 tuning layer and highly photosensitive PQDs charge-trapping layer provides the devices with extensively tunable synaptic performance under optical and electrical co-modulation. Applying light during electrical modulation can significantly improve both the synaptic weight changes and the non-linearity of weight updates, while the memory effect under light modulation can be obviously adjusted by the gate voltage. The pattern learning and forgetting processes for “0” and “1” with different synaptic weights and memory times are further demonstrated in the device array. Overall, this work provides synaptic devices with tunable functions for building complex and robust artificial neural networks.
摘要
将记忆和处理功能整合为一个单元的突触器件在神经形态 计算、软机器人和人机交互等方面具有广泛的应用潜力. 然而, 先 前报道的大多数突触器件一旦制造出来就表现出固定的性能, 这 限制了它们在不同场景中的应用. 在这里, 我们报道了一种以钙钛 矿量子点为电荷俘获层、以原子层沉积的Al2O3为隧穿层的浮栅光 敏突触晶体管. 在电或者光信号的刺激下, 该器件都能展示出典型 的突触行为, 包括兴奋性突触后电流、双脉冲异化和动态滤波特 性. 进一步地, 器件中高质量Al2O3隧穿层和高光敏的钙钛矿量子 点电荷俘获层使得其突触可塑性可以在光和电信号的共同调制下 实现大范围的调节. 在电调制过程中施加光信号可以显著改善突 触权重的变化和权值更新的非线性, 而光调制下的记忆效应可以 明显地受到栅极电压的调节. 该器件的阵列进一步展示了对图案 “0”和“1”的不同突触权重或记忆时间的学习和遗忘过程. 综上, 这 项工作为构建复杂而稳固的人工神经网络提供了具有可调功能的 突触器件.
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References
Mennel L, Symonowicz J, Wachter S, et al. Ultrafast machine vision with 2D material neural network image sensors. Nature, 2020, 579: 62–66
Shi W, Guo Y, Liu Y. When flexible organic field-effect transistors meet biomimetics: A prospective view of the internet of things. Adv Mater, 2020, 32: 1901493
Roy K, Jaiswal A, Panda P. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575: 607–617
Zidan MA, Strachan JP, Lu WD. The future of electronics based on memristive systems. Nat Electron, 2018, 1: 22–29
Kuzum D, Yu S, Philip Wong HS. Synaptic electronics: Materials, devices and applications. Nanotechnology, 2013, 24: 382001
Zhu LQ, Wan CJ, Guo LQ, et al. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat Commun, 2014, 5: 3158
van de Burgt Y, Melianas A, Keene ST, et al. Organic electronics for neuromorphic computing. Nat Electron, 2018, 1: 386–397
Wang Y, Lv Z, Chen J, et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv Mater, 2018, 30: 1802883
Sanchez Esqueda I, Yan X, Rutherglen C, et al. Aligned carbon nanotube synaptic transistors for large-scale neuromorphic computing. ACS Nano, 2018, 12: 7352–7361
Tian H, Cao X, Xie Y, et al. Emulating bilingual synaptic response using a junction-based artificial synaptic device. ACS Nano, 2017, 11: 7156–7163
Wang H, Zhao Q, Ni Z, et al. A ferroelectric/electrochemical modulated organic synapse for ultraflexible, artificial visual-perception system. Adv Mater, 2018, 30: 1803961
John RA, Liu F, Chien NA, et al. Synergistic gating of electro-iono-photoactive 2D chalcogenide neuristors: Coexistence of Hebbian and homeostatic synaptic metaplasticity. Adv Mater, 2018, 30: 1800220
Dai S, Zhao Y, Wang Y, et al. Recent advances in transistor-based artificial synapses. Adv Funct Mater, 2019, 29: 1903700
Yu JJ, Liang LY, Hu LX, et al. Optoelectronic neuromorphic thin-film transistors capable of selective attention and with ultra-low power dissipation. Nano Energy, 2019, 62: 772–780
Zhu J, Zhang T, Yang Y, et al. A comprehensive review on emerging artificial neuromorphic devices. Appl Phys Rev, 2020, 7: 011312
Park HL, Lee Y, Kim N, et al. Flexible neuromorphic electronics for computing, soft robotics, and neuroprosthetics. Adv Mater, 2020, 32: 1903558
Tang J, Yuan F, Shen X, et al. Bridging biological and artificial neural networks with emerging neuromorphic devices: Fundamentals, progress, and challenges. Adv Mater, 2019, 31: 1902761
Fuller EJ, Keene ST, Melianas A, et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science, 2019, 364: 570–574
Dai S, Wu X, Liu D, et al. Light-stimulated synaptic devices utilizing interfacial effect of organic field-effect transistors. ACS Appl Mater Interfaces, 2018, 10: 21472–21480
Sun Z, Pedretti G, Bricalli A, et al. One-step regression and classification with cross-point resistive memory arrays. Sci Adv, 2020, 6: eaay2378
Wang CY, Liang SJ, Wang S, et al. Gate-tunable van der Waals heterostructure for reconfigurable neural network vision sensor. Sci Adv, 2020, 6: eaba6173
Kim Y, Chortos A, Xu W, et al. A bioinspired flexible organic artificial afferent nerve. Science, 2018, 360: 998–1003
Shim H, Sim K, Ershad F, et al. Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems. Sci Adv, 2019, 5: eaax4961
Zhu X, Wang Q, Lu WD. Memristor networks for real-time neural activity analysis. Nat Commun, 2020, 11: 2439
Han H, Xu Z, Guo K, et al. Tunable synaptic plasticity in crystallized conjugated polymer nanowire artificial synapses. Adv Intelligent Syst, 2020, 2: 1900176
Kim S, Lee Y, Park M, et al. Dimensionality dependent plasticity in halide perovskite artificial synapses for neuromorphic computing. Adv Electron Mater, 2019, 5: 1900008
Li E, Lin W, Yan Y, et al. Synaptic transistor capable of accelerated learning induced by temperature-facilitated modulation of synaptic plasticity. ACS Appl Mater Interfaces, 2019, 11: 46008–46016
Li X, Wu Y, Zhang S, et al. CsPbX3 quantum dots for lighting and displays: Room-temperature synthesis, photoluminescence superiorities, underlying origins and white light-emitting diodes. Adv Funct Mater, 2016, 26: 2435–2445
Lv Z, Chen M, Qian F, et al. Mimicking neuroplasticity in a hybrid biopolymer transistor by dual modes modulation. Adv Funct Mater, 2019, 29: 1902374
Liu Y, Tang S, Banerjee SK. Tunnel oxide thickness dependence of activation energy for retention time in SiGe quantum dot flash memory. Appl Phys Lett, 2006, 88: 213504
Yan D, Shi T, Zang Z, et al. Ultrastable CsPbBr3 perovskite quantum dot and their enhanced amplified spontaneous emission by surface ligand modification. Small, 2019, 15: 1901173
Wang K, Dai S, Zhao Y, et al. Light-stimulated synaptic transistors fabricated by a facile solution process based on inorganic perovskite quantum dots and organic semiconductors. Small, 2019, 15: 1900010
Shen K, Xu H, Li X, et al. Flexible and self-powered photodetector arrays based on all-inorganic CsPbBr3 quantum dots. Adv Mater, 2020, 32: 2000004
Chen JY, Chiu YC, Li YT, et al. Nonvolatile perovskite-based photomemory with a multilevel memory behavior. Adv Mater, 2017, 29: 1702217
Abbott LF, Regehr WG. Synaptic computation. Nature, 2004, 431: 796–803
Sun J, Oh S, Choi Y, et al. Optoelectronic synapse based on IGZO-alkylated graphene oxide hybrid structure. Adv Funct Mater, 2018, 28: 1804397
Fu YM, Wan CJ, Yu F, et al. Electrolyte gated oxide pseudodiode for inhibitory synapse applications. Adv Electron Mater, 2018, 4: 1800371
Shi J, Ha SD, Zhou Y, et al. A correlated nickelate synaptic transistor. Nat Commun, 2013, 4: 2676
Ryu JJ, Jeon K, Kim G, et al. Highly linear and symmetric weight modification in HfO2-based memristive devices for high-precision weight entries. Adv Electron Mater, 2020, 6: 2000434
Chen X, Pan J, Fu J, et al. Polyoxometalates-modulated reduced graphene oxide flash memory with ambipolar trapping as bidirectional artificial synapse. Adv Electron Mater, 2018, 4: 1800444
Kim S, Choi B, Lim M, et al. Pattern recognition using carbon nanotube synaptic transistors with an adjustable weight update protocol. ACS Nano, 2017, 11: 2814–2822
Ren Y, Yang J, Zhou L, et al. Gate-tunable synaptic plasticity through controlled polarity of charge trapping in fullerene composites. Adv Funct Mater, 2018, 28: 1805599
Yu F, Zhu LQ, Gao WT, et al. Chitosan-based polysaccharide-gated flexible indium tin oxide synaptic transistor with learning abilities. ACS Appl Mater Interfaces, 2018, 10: 16881–16886
Acknowledgements
This work was supported by the National Natural Science Foundation of China (61874029).
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Wu X and Ding SJ designed the devices and experiments; Li L performed the experiments; Li L and Wu X wrote the paper; all the authors discussed the results.
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The authors declare that they have no conflict of interest.
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Experimental details and supporting data are available in the online version of the paper.
Lingkai Li received his BE degree in microelectronics from Xidian University, Xi’an, China, in 2017, and MSc degree from the School of Microelectronics, Fudan University, Shanghai, China, in 2020. His research interest is thin-film synaptic transistors. He is currently an integrated circuit (IC) verification engineer in Huawei company.
Xiaohan Wu is now an associate professor at the School of Microelectronics, Fudan University, China. He received his PhD degree from the University of Montpellier I, France in 2013, and was employed as an Assistant Professor in the School of Materials Science and Engineering, Tongji University till 2018. His research interest refers to optoelectronics, flexible devices and atomic layer technology.
Shi-Jin Ding received his PhD degree from Fudan University in China in 2001. He worked at Kiel University in Germany from 2001 to 2002, and at the National University of Singapore from 2003 to 2004. He joined Fudan University in 2005, and became a full professor in 2008. His current research interests include metal oxide semiconductor thin-film transistor and memory devices, solid-state ultrahigh density capacitors, and atomic layer deposition of novel materials for advanced integration circuits.
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Li, L., Wang, XL., Pei, J. et al. Floating-gate photosensitive synaptic transistors with tunable functions for neuromorphic computing. Sci. China Mater. 64, 1219–1229 (2021). https://doi.org/10.1007/s40843-020-1534-2
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DOI: https://doi.org/10.1007/s40843-020-1534-2