Bio-inspired organic small-molecule memristor synergistically modulated by electric-thermal field

LI Wen; KONG Lingjie; CHEN Ye; ZHOU Jia; SHI Wei; YI Mingdong

doi:10.7498/aps.74.20250626

Abstract

Memristor-driven neuromorphic computing offers a promising path for brain-inspired intelligence by emulating the multidimensional plasticity of biological synapses, thereby achieving energy-efficient parallel computation. However, in the context of dynamically modulating synaptic plasticity, achieving strong environmental adaptability, especially in response to temperature fluctuation, remains a major challenge for organic memristors. In order to solve this problem, a bio-inspired cobalt phthalocyanine (CoPc)-based memristor is developed specifically for synergistic electric-thermal field modulation. The device utilizes the stable planar π-conjugated system of CoPc molecules and leverages dynamic oxygen vacancy (O_V) migration at the CoPc/AlO_x interface. A comprehensive electrical characterisation is conducted, incorporating X-ray photoelectron spectroscopy (XPS), in-situ Raman spectroscopy, and temperature-dependent electrical measurements across a wide range (293–473 K). This is supported by physical modelling (SCLC, FNT, Arrhenius) to elucidate the underlying mechanisms. Evidence indicates that the device can effectively replicate key aspects of synaptic plasiticy, including short-term potentiation/depression (STP/STD), and pairedpulse facilitation/depression (PPF/PPD), through the regulation of an electric field. The index increases to 151%, indicating a significant increase. Spike-amplitude-dependent plasticity (SADP, 45% weight increase), spike-timing-dependent plasticity (STDP, ΔW = ±90%), and learning-forgetting-relearning dynamics are revealed, unveiling cumulative memory effects linked to O_V transport. The device exhibits excellent temperature resilience over the range of 293–473 K, characterised by a linear adaptive shift in its critical voltage (V_Critical) from 8.7 V at 293 K to 4.5 V, with dV_Critical/dT = 0.023 V/K. Physical analysis attributes this adaptive threshold and stable operation to a dual-field synergistic mechanism based on trap-assisted carrier transport. Elevated temperature thermally activates carriers, reducing the effective barrier for trap escape and O_V migration activation energy (E_a = 0.073–0.312 eV), which facilitates conduction through Fowler-Nordheim tunneling (FNT) at lower electric fields. Conversely, lower temperatures require higher electric fields to enhance trap ionization efficiency through the Poole-Frenkel effect, compensating for reduced thermal energy. The validation of the linear V_Critical-T relationship as a sensitive temperature transduction mechanism is achieved by developing an intelligent fire warning system. This study involves a 6 × 6 CoPc memristor array integrated into household heaters, combined with a deep learning model consisting of a fully connected network with 20 × 16 + 16 × 8 + 8 × 1 neurons. The resulting model achieves an accuracy of 96.54% in identifying high abnormal temperature. This work establishes a novel paradigm for environmentally adaptive neuromorphic devices through molecular/ interface design and synergistic multi-field modulation, providing a physical realization of temperature-elastic synaptic operation and demonstrating its practical feasibility for powerful next-generation brain-inspired computing platforms.

Keywords:

Authors and contacts

1.
State Key Laboratory of Flexible Electronics, Institute of Advanced Materials, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
School of Flexible Electronics (Future Technologies), Institute of Advanced Materials, Nanjing Tech University, Nanjing 211816, China

Corresponding author: CHEN Ye, ichenye@outlook.com ; YI Mingdong, iammdyi@njupt.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62174089, 62275130, 62375125), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20240138), and the Postgraduate Research&Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX21_0774).

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References

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Cited By

图 1 CoPc忆阻器性能　(a) 器件结构示意图; (b) 酞菁钴 (CoPc)的分子结构; (c) 器件横截面SEM图像; (d) CoPc薄膜AFM图像; (e) ITO/CoPc/Al忆阻器在0→–9.5→0 V(左)和0→9.5→0 V(右)的20个电压扫描周期的I-V曲线; (f) 从(e)中提取的增强(9.5 V)和抑制(–9.5 V)特性的电导

Figure 1. Performance of CoPc memristor: (a) Schematic diagram of the device structure; (b) molecular structure of cobalt phthalocyanine (CoPc); (c) cross-sectional SEM image of the device; (d) AFM image of the CoPc film; (e) I-V curves of the CoPc memristor during 20 voltage-sweep cycles of 0→–9.5→0 V (left) and 0→9.5→0 V (right), respectively; (f) conductance of the potentiation (9.5 V) and depression (–9.5 V) characteristics extracted from (e).

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图 2 CoPc忆阻器机理分析　(a) 器件面积-电流依赖特性; (b) 扫描速率-回滞面积依赖特性; 忆阻器高阻态的I-V曲线进行SCLC (c)和FNT (d)拟合; 忆阻器低阻态的I-V曲线进行SCLC (e)和FNT (f)拟合; 在HRS (g)和LRS (h)时从ITO电极收集的O 1s XPS能谱; 在不同深度采集的 Co 2p (i)和 O 1s (j)的XPS能谱; (k) 9.5 V电压刺激下的原位拉曼光谱; (l) 在不同温度(293—473 K)下连续施加9.5 V尖峰电压获得的电流曲线

Figure 2. Mechanism analysis of CoPc memristor: (a) Cell size-current dependent characteristics; (b) scan speed-hysteresis area dependent characteristics; I-V curves of the HRS of the memristor fitted by SCLC (c) and FNT (d); I-V curves of the LRS of the memristor fitted by SCLC (e) and FNT (f); O 1s XPS spectra collected from the ITO electrode at HRS (g) and LRS (h); XPS energy spectra of Co 2p (i) and O 1s (j) collected at different depths; (k) in situ Raman spectra under 9.5 V voltage stimuli; (l) current obtained by continuously applying spike voltages of 9.5 V at different temperatures (293–473 K).

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图 3 CoPc忆阻器的突触可塑性　(a) 短时程增强和(b) 短时程抑制曲线(50个电压脉冲刺激, V_Pos = 9.5 V, V_Neg = –9.5 V, V_Read = 1 V, Δt = 430 ms); (c) 配对脉冲异化PPF和(d) 配对脉冲抑制 PPD指数曲线, 插图为对应的电流曲线; (e) 脉冲幅值 (7, 8, 9.5 V) 依赖可塑性曲线; (f) 脉冲频率(2.30, 1.15, 0.769, 0.577, 0.462 Hz)依赖可塑性曲线; (g) 尖峰时序依赖可塑性曲线; (h) 归一化的增强(V_Pos = 9.5 V)和抑制(V_Neg = 5.5 V)曲线; (i) 学习-遗忘-再学习曲线

Figure 3. Synaptic plasticity of CoPc memristor. STP (a) and STD (b) curves under 50 voltage pulses (V_Pos = 9.5 V, V_Neg = –9.5 V, V_Read = 1 V, Δt = 430 ms); PPF (c) and PPD (d) index curves, with insets showing the corresponding current curves; (e) spike amplitude (7, 8, and 9.5 V) dependent plasticity curves; (f) spike frequency (2.30, 1.15, 0.769, 0.577, 0.462 Hz) dependent plasticity curves; (g) STDP curves; (h) normalized potentiation (V_Pos = 9.5 V) and depression (V_Neg = 5.5 V) curves; (i) learning-forgetting-relearning curves.

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图 4 CoPc忆阻器的温度梯度调控特性　CoPc忆阻器依次在(a) 温度为293 K、刺激电压为9 V; (b) 333 K, 8 V; (c) 373 K, 7 V; (d) 423 K, 6 V; (e) 473 K, 5 V下的短时程可塑性; (f) 不同电压(6, 7, 8和9 V)下的LRS电阻随1000/T变化的Arrhenius模型; 在不同温度(g) 293 K, (h) 333 K, (i) 373 K, (j) 423, (k) 473 K的测试环境下器件50个临界电压的概率分布; (l) 在5个温度(293, 333, 373, 423和473 K)与5个电压(5, 6, 7, 8和9 V)下, 总计25种工作状态下的100 s刺激后的稳态电流; (m) 家用电热器存在的火灾隐患示意图; (n) 基于CoPc忆阻器的智能预警装置示意图; (o) 在100次训练后对异常状态识别正确率

Figure 4. Temperature gradient control characteristics of CoPc memristor: STP of the CoPc memristor at (a) a temperature of 293 K and an amplitude of 9 V; (b) 333 K and 8 V; (c) 373 K and 7 V; (d) 423 K and 6 V; (e) 473 K and 5 V in sequence; (f) Arrhenius model of LRS resistance varying with 1000/T at different voltages (6, 7, 8, and 9 V); the probability distribution of 50 critical voltages of the device under the different temperatures of (g) 293 K, (h) 333 K, (i) 373 K, (j) 423 (k) and 473 K; (1) steady-state currents after 100 s of stimulation at five temperatures (293, 333, 373, 423, and 473 K) with five voltages (5, 6, 7, 8, 9 V) for a total of 25 operating states; (m) schematic diagram of fire hazards existing in household electric heaters; (n) schematic diagram of the intelligent early warning device based on CoPc memristor; (o) the accuracy rate in identifying abnormal states after 100 training epoch.

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表 1 文献中报告的温度弹性忆阻器工作温度范围、动态可调性、功耗、识别准确率总结

Table 1. Summary of the operating temperature range, dynamic adjustability, power consumption and recognition accuracy of temperature-elastic memristors reported in the literature.

Active layer	Working temperature/K	Material	Dynamic modulation	Recognition accuracy rate/%	Power consumption	Ref.
CoPc	293–473	Organic	Yes	96.54	1.95 nJ	This work
SF:Ca²⁺	300	Organic	No	—	2.25 μJ	[59]
TaO_x	298–418	Inorganic	No	100	—	[60]
CIGSe	623	Inorganic	No	90	—	[61]
TaO_1.8/TaO_2.7/TaO_1.8	300–343	Inorganic	No	—	—	[62]
AlFeO₃	298–413	Inorganic	No	—	—	[63]
Chitosan/PNIPAM	290–410	Organic	Yes	—	0.23 mJ	[64]
Co-TCPP	288–313	Organic	No	93.95	—	[65]
CsPbBr₂I	300–513	Inorganic	No	—	—	[66]
VO₂	294–315	Inorganic	No	98.1	3.9 nJ	[8]
VOPc	303–373	Organic	Yes	—	—	[67]

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[1]	Carlos E, Branquinho R, Martins R, Kiazadeh A, Fortunato E 2020 Adv. Mater. 33 2004328
[2]	Hong X, Loy D J, Dananjaya P A, Tan F, Ng C, Lew W 2018 J. Mater. Sci. 53 8720 Google Scholar
[3]	Liao K H, Lei P X, Tu M L, Luo S W, Jiang T, Jie W J, Hao J H 2021 ACS Appl. Mater. Interfaces 13 32606 Google Scholar
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[5]	Campbell K A, Drake K T, Barney Smith E H 2016 Front. Bioeng. Biotechnol. 4 97
[6]	Daddinounou S, Vatajelu E I 2024 Front. Neurosci. 18 1387339 Google Scholar
[7]	Go S X, Lim K G, Lee T H, Loke D K 2024 Small Sci. 4 2300139 Google Scholar
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[10]	Zhang J L, Li X J, Xiao P D, Wei Z M, Hong Q H 2024 IEEE Trans. Circuits Syst. I Regular Papers 71 3228 Google Scholar
[11]	Huang Y F, Hopkins R, Janosky D, Chen Y C, Chang Y F, Lee J C 2022 IEEE Trans. Electron Devices 69 6102 Google Scholar
[12]	Li J Y, Qian Y Z, Li W, Yu S C, Ke Y X, Qian H W, Lin Y H, Hou C H, Shyue J J, Zhou J, Chen Y, Xu J P, Zhu J T, Yi M D, Huang W 2023 Adv. Mater. 35 2209728 Google Scholar
[13]	Naqi M, Yu Y, Cho Y, Kang S, Khine M T, Lee M, Kim S 2024 Mater. Today Nano 27 100491 Google Scholar
[14]	Fan Z Y, Tang Z H, Fang J L, Jiang Y P, Liu Q X, Tang X G, Zhou Y C, Gao J 2024 Nanomaterials 14 583 Google Scholar
[15]	He Y, Farmakidis N, Aggarwal S, Dong B, Lee J S, Wang M, Zhang Y, Parmigiani F, Bhaskaran H 2024 Nano Lett. 24 16325 Google Scholar
[16]	Zhu Y B, Wu C X, Xu Z W, Liu Y, Hu H L, Guo T L, Kim T W, Chai Y, Li F S 2021 Nano Lett. 21 6087 Google Scholar
[17]	Haghshenas Gorgabi F, Morant-Miñana M C, Zafarkish H, Abbaszadeh D, Asadi K 2023 J. Mater. Chem. C 11 1690 Google Scholar
[18]	Hajtó D, Rák Á, Cserey G 2019 Materials 12 3573 Google Scholar
[19]	Li H Z, Gao Q, Gao J, Huang J S, Geng X L, Wang G X, Liang B, Li X H, Wang M, Xiao Z S, Chu P K, Huang A P 2023 ACS Appl. Mater. Interfaces 15 46449 Google Scholar
[20]	Sun Y M, Li B X, Liu M, Zhang Z K 2024 Mater. Today Adv. 23 100515 Google Scholar
[21]	Tian L, Wang Y Y, Shi L P, Zhao R 2020 ACS Appl. Electron. Mater. 2 3633 Google Scholar
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[23]	Baranowski M, Sachser R, Marinković B P, Ivanović S D, Huth M 2022 Nanomaterials 12 4145 Google Scholar
[24]	Mayer S F, Mitsioni M, van den Heuvel L, Robin P, Ronceray N, Marcaida M J, Abriata L A, Krapp L F, Anton J S, Soussou S, Jeanneret-Grosjean J, Fulciniti A, Moller A, Vacle S, Feletti L, Brinkerhoff H, Laszlo A H, Gundlach J H, Emmerich T, Dal Peraro M, Radenovic A 2025 BioRxiv 26 615172
[25]	Rodriguez N, Maldonado D, Romero F J, Alonso F J, Aguilera A M, Godoy A, Jimenez-Molinos F, Ruiz F G, Roldan J B 2019 Materials 12 3734 Google Scholar
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