Recent progress of low-voltage memristor for neuromorphic computing

Gong Yi-Chun; Ming Jian-Yu; Wu Si-Qi; Yi Ming-Dong; Xie Ling-Hai; Huang Wei; Ling Hai-Feng

doi:10.7498/aps.73.20241022

Abstract

Memristors stand out as the most promising candidates for non-volatile memory and neuromorphic computing due to their unique properties. A crucial strategy for optimizing memristor performance lies in voltage modulation, which is essential for achieving ultra-low power consumption in the nanowatt range and ultra-low energy operation below the femtojoule level. This capability is pivotal in overcoming the power consumption barrier and addressing the computational bottlenecks anticipated in the post-Moore era. However, for brain-inspired computing architectures utilizing high-density integrated memristor arrays, key device stability parameters must be considered, including the on/off ratio, high-speed response, retention time, and durability. Achieving efficient and stable ion/electron transport under low electric fields to develop low-voltage, high-performance memristors operating below 1 V is critical for advancing energy-efficient neuromorphic computing systems. This review provides a comprehensive overview of recent advancements in low-voltage memristors for neuromorphic computing. Firstly, it elucidates the mechanisms that control the operation of low-voltage memristor, such as electrochemical metallization and anion migration. These mechanisms play a pivotal role in determining the overall performance and reliability of memristors under low-voltage conditions. Secondly, the review then systematically examines the advantages of various material systems employed in low-voltage memristors, including transition metal oxides, two-dimensional materials, and organic materials. Each material system has distinct benefits, such as low ion activation energy, and appropriate defect density, which are critical for optimizing memristor performance at low operating voltages. Thirdly, the review consolidates the strategies for implementing low-voltage memristors through advanced materials engineering, doping engineering, and interface engineering. Moreover, the potential applications of low-voltage memristors in neuromorphic function simulation and neuromorphic computing are discussed. Finally, the current problems of low-voltage memristors are discussed, especially the stability issues and limited application scenarios. Future research directions are proposed, focusing on exploring new material systems and physical mechanisms that could be integrated into device design to achieve higher-performance low-voltage memristors.

Keywords:

Authors and contacts

State Key Laboratory of Organic Electronics and Information Displays, School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Corresponding author: Ling Hai-Feng, iamhfling@njupt.edu.cn

Funds: Project supported by the National Key Research and Development Program of China (Grant No. 2021YFA0717900), the National Natural Science Foundation of China (Grant Nos. 62288102, 22275098, 62471251), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. 46030CX21252).

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Cited By

图 1 综述框架示意图^[20–27]

Figure 1. Schematic of the overview framework^[20–27].

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图 2 两种典型的忆阻器开关机制　(a) ECM示意图; (b) VCM细丝型示意图; (c) 界面型示意图; (d)双极型开关的典型电流-电压(I-V)特性曲线; (e) 阈值型开关的I-V特性曲线; (f) 模拟类开关的I-V特性曲线

Figure 2. Two typical switching mechanisms of memristors: (a) Schematic diagram of ECM; (b) schematic diagram of filamentary-type VCM; (c) schematic diagram of interfacial-type VCM; (d) typical I-V characteristics of bipolar resistive switching; (e) typical I-V characteristics of threshold switching; (f) typical I-V characteristics of analog switching.

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图 3 (a) HfO₂晶胞示意图; (b) 钙钛矿通式晶胞示意图; (c) 闪锌矿晶胞示意图; (d) MoS₂层状结构示意图; (e) h-BN结构示意图; (f) PEDOT:PSS分子示意图; (g) MTPP分子示意图

Figure 3. (a) Schematic diagram of HfO₂ unit cell; (b) schematic diagram of perovskite unit cell; (c) schematic diagram of sphalerite; (d) schematic diagram of MoS₂ layered structure; (e) schematic diagram of h-BN structure; (f) schematic diagram of PEDOT:PSS molecule; (g) schematic diagram of MTPP molecule.

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图 4 (a) 基于材料本征缺陷优化离子传输示意图; (b) SnS的晶体结构示意图; (c) 不同限流下的Ag/SnS/Pt器件的RS特性; (d) SnS薄膜的扫描隧穿电镜图像; (e) dI/dV与SnS样品偏压关系图^[73]; (f) Ag/CsPbI₃/Ag忆阻器的结构示意图和SEM图像; (g) Ag/CsPbI₃/Ag忆阻器I-V特性曲线; (h) 碘元素X射线特征峰的能量色散光谱^[111]; (i), (j) Pt/MoS₂/Ti忆阻器物理机制示意图; (k) MoS₂纳米片尺寸为0.48 μm时的I-V特性曲线; (l) 硫空位扩散势垒随纳米片尺寸变化曲线^[117]

Figure 4. (a) Schematic diagram of ion transport optimization based on intrinsic material defects; (b) schematic diagram of SnS crystal structure; (c) resistive switching of Ag/SnS/Pt device with different compliance current; (d) scanning tunneling microscopy image of SnS thin film; (e) relationship of dI/dV versus bias voltage of SnS sample^[73]; (f) schematic diagram and SEM image of Ag/CsPbI₃/Ag memristor; (g) I-V characteristic curve of Ag/CsPbI₃/Ag memristor; (h) energy-dispersive X-ray spectrum of iodine elements core level^[111]; (i), (j) schematic diagram of the physical mechanism of Pt/MoS₂/Ti memristor; (k) I-V characteristic curve when the size of MoS₂ nanosheets is 0.48 μm; (l) curve of the change in sulfur vacancy diffusion barrier with nanosheet size^[117].

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图 5 基于本征微纳结构优化离子传输　(a) 基于纳米线结构优化离子传输示意图; (b) Ag/c-YY NW/Ag器件结构示意图; (c) c-YY的化学结构式和开尔文探针力显微镜(KPFM)表面电势分布; (d) 不同限流下Ag/c-YY NW/Ag的阈值开关行为^[124]; (e) Ag-SiO₂-Pt器件结构下嵌入图案化蛋白质纳米线薄膜的垂直忆阻器结构; (f) 蛋白质纳米线的TEM图像(标尺为100 nm); (g) 在忆阻器中引入蛋白质纳米线促进Ag⁺的阴极还原过程示意图; (h) 蛋白质纳米线器件在800次I-V连续扫描下的阈值开关行为^[126]; (i) 基于高度有序晶体排布的本征微纳结构优化离子传输示意图; (j), (k) PBFCL₁₀薄膜中有序排列的纳米晶体和均匀的离子迁移路径的示意图; (l) C-AFM表征下PBFCL₁₀薄膜表面的电流分布; (m) Ag/PBFCL₁₀/Au典型I-V特性曲线^[131]

Figure 5. Optimization of ion transport-based on intrinsic nanostructure: (a) Schematic diagram of ion transport optimization based on intrinsic nanostructure; (b) schematic diagram of Ag/c-YY NW/Ag device structure; (c) chemical structure and KPFM surface potential distribution of c-YY; (d) threshold switching behavior of Ag/c-YY NW/Ag under different compliance current^[124]; (e) vertical memristor structure with patterned protein nanowire thin film embedded in Ag-SiO₂-Pt device structure; (f) TEM image of protein nanowires (scale bar: 100 nm); (g) schematic of the cathodic reduction process of Ag⁺ facilitated by protein nanowires; (h) threshold switching behavior of protein nanowire devices under 800 consecutive I-V scanning^[126]; (i) schematic of ion transport optimization based on highly-ordered crystal arrangement of intrinsic nanostructures; (j), (k) schematic of ordered nanocrystals and uniform ion migration pathways in PBFCL₁₀ thin film; (l) current mapping on the surface of PBFCL₁₀ film characterized by C-AFM; (m) typical I-V characteristics of Ag/PBFCL₁₀/Au^[131].

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图 6 (a) 掺杂离子策略——增大离子总量与优化离子传输路径示意图; (b) Ag/Ag掺杂的CM:κ-car/Pt的原理图; (c) Ag/Ag掺杂的CM:κ-car /Pt器件的典型I-V曲线^[136]; (d) Ag-iPS的差分电荷密度分布; (e) Ag/Ag-iPS(~40 nm)/Au忆阻器100次循环内的I-V曲线^[138]; (f) MAPbI₃:Ag忆阻器在0 V→1 V→0 V→–1 V→0 V下的双向阈值开关行为; (g) MAPbI₃:Ag扩散忆阻器中执行神经模拟活动的阈值激发和自发弛豫的工作机制示意图^[59]; (h) Au/YSZ:Ag/Au/Ti忆阻器连续50个直流电压扫描循环(0→1.1 V→0 V→–1 V→0 V), 展示出可重复的双向阈值开关行为; (i) TEM观察下的YSZ:Ag忆阻器阈值切换过程, 初始器件具有约25 nm的大银簇; (j) 直流扫描将银簇分解为约10 nm的小银纳米颗粒, 形成锥形渗透通道; (k), (l) YSZ:Ag忆阻器中Ag团簇演化的初始状态和加正偏压后的形态^[139]

Figure 6. (a) Doped ion strategy—schematic diagram illustrating increased total ion amount and optimized ion transport pathways; (b) schematic diagram of Ag/Ag-doped CM:κ-car/Pt; (c) typical I-V curve of Ag/Ag-doped CM:κ-car/Pt^[136]; (d) differential charge density distribution of Ag-iPS; (e) I-V curves within 100 cycles for Ag/Ag-iPS (~40 nm)/Au memristor^[138]; (f) bidirectional threshold switching behavior of MAPbI₃:Ag memristor under 0 V → 1 V → 0 V → –1 V → 0 V; (g) schematic diagram illustrating threshold excitation for simulation neural activity and spontaneous relaxation in MAPbI₃:Ag diffusion memristor^[59]; (h) continuous 50 cycles of DC voltage scans (0 → 1.1 V → 0 V → –1 V → 0 V) in Au/YSZ:Ag/Au/Ti memristor, demonstrating repeatable bidirectional threshold switching behavior; (i) TEM observation of YSZ:Ag memristor threshold switching process, with initial device featuring large silver clusters (~25 nm); (j) DC scan decomposing silver clusters into smaller silver nanoparticles (~10 nm), forming conical permeation channels; (k), (l) initial state and morphology after applying positive bias of silver cluster evolution in YSZ:Ag memristor^[139].

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图 7 掺杂离子策略——改善电荷存储/释放过程　(a) Ni原子周围电荷缺乏区域的差分电荷密度分布表现出超快开关速度和原子尺寸效应引起的库仑阻塞, 以实现长数据保留能力; (b) Au/NiSAs/N-C/PVP/ITO忆阻器100次循环的I-V特性曲线和(c) 保留时间^[99]; (d)—(g) ITO/Silk:AgNO₃/Ag的工作机制模型^[146]

Figure 7. Doped ion strategy—improving charge storage/release processes: (a) Differential charge density distribution around Ni atoms showing ultrafast switching speed and Coulomb blockade for long retention due to atomic size effects; (b) the I-V characteristic curves and (c) retention time of 100 cycles of memristor^[99]; (d)–(g) mechanism schematic of ITO/Silk:AgNO₃/Ag^[146].

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图 8 (a) 界面优化策略——钝化界面缺陷示意图; (b) 经过/未经过PEAI钝化的MAPbI₃薄膜的时间分辨光致发光光谱; (c) 经过/未经过PEAI钝化的PET-ITO/MAPbI₃/PEAI/Au忆阻器的双对数I-V特性; (d) 在不同频率(4.17, 2.94, 1.85, 1.06, 0.87 MHz)下的电流响应^[152]; (e) Ag/CsI/MoO_x/Ag忆阻器示意图; (f) Ag/CsI/MoO_x/Ag器件I-V特性; (g) Ag/CsI/MoO_x/Ag忆阻器在136000次循环耐久性测试中的表现^[62]

Figure 8. (a) Interface optimization strategy—passivating interface defect schematic; (b) TRPL spectra of MAPbI₃ with/without PEAI passivation; (c) double logarithmic I-V characteristics of PET-ITO/MAPbI₃/PEAI/Au memristors with/without PEAI passivation; (d) current response at different frequencies (4.17, 2.94, 1.85, 1.06, 0.87 MHz)^[152]; (e) schematic diagram of Ag/CsI/MoO_x/Ag memristor; (f) the I-V characteristics of Ag/CsI/MoO_x/Ag device; (g) endurance test of Ag/CsI/MoO_x/Ag memristors under 136000 cycles^[62].

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图 9 界面优化策略——保持范德瓦耳斯接触　(a), (b) 将Ag电极转移到SnSe片的另一侧完成Ag/SnO_x/SnSe器件; (c) Ag/SnO_x/SnSe界面的横截面TEM图像; (d) Ag/SnO_x/SnSe忆阻器I-V 特性曲线; (e) 通过分别施加1 V, –1 V的编程电压脉冲和0.1 V的读取电压(蓝色曲线), 并仅在读取操作期间测量电流(红色曲线), 演示写入、擦除和读取操作^[157]; (f) 在SiO₂/Si基板上制造的GeTe/MoTe₂忆阻器示意图; (g) 器件横截面高分辨率TEM图像; (h) 面积在10 μm×10 μm时的I-V特性曲线^[162]

Figure 9. Interface optimization strategy—maintaining van der Waals contact: (a), (b) Ag electrode transferred to the other side of SnSe flake to complete Ag/SnO_x/SnSe device; (c) cross-sectional TEM image of Ag/SnO_x/SnSe interface; (d) I-V characteristic curve of Ag/SnO_x/SnSe memristor; (e) demonstration of write, erase, and read operations by applying programming voltage pulses of 1 V and –1 V, a reading voltage of 0.1 V (blue curve), and measuring current only during the reading operation (red curve) ^[157]; (f) schematic diagram of GeTe/MoTe₂ memristors fabricated on SiO₂/Si substrate; (g) high-resolution cross-sectional TEM image of device; (h) the I-V characteristic of 10 μm×10 μm memristive device^[162].

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图 10 基于低电压忆阻器的神经元功能模拟及计算应用　(a) 生物神经元和细胞膜结构; (b), (c) TS器件模拟神经元基本动作电位发放过程^[109]; (d) 将触觉传感单元中输出电压传至人工神经元的电路图; (e), (f) 通过按压压力传感器的电压输入, 人工神经元中的膜电位(V_m)和电流(I)发生变化^[127]; (g) 不同健康个体的呼吸模式; (h) 湿度感知神经元电路示意图; (i) 人工湿度传感神经元在不同湿度脉冲刺激下的响应; (j) 不同湿度脉冲刺激下的异步ST1和ST2结果; (k) 用于肺部疾病分类的3层神经网络示意图; (l) 分类结果的混淆矩阵^[124]

Figure 10. Neural function simulation and computational applications based on low-voltage memristors: (a) Biological neurons and cell membrane structure; (b), (c) TS device simulating the basic action potential firing process of neurons^[109]; (d) circuit diagram for transmitting output voltage from tactile sensing unit to artificial neurons; (e), (f) changes in membrane potential (V_m) and current (I) in artificial neurons due to voltage input from pressure sensor^[127]; (g) breathing patterns of different healthy individuals; (h) circuit diagram of humidity-sensing neurons; (i) response of artificial humidity-sensing neurons to different humidity pulse stimuli; (j) asynchronous ST1 and ST2 results under different humidity pulse stimuli; (k) schematic diagram of a three-layer neural network used for lung disease classification; (l) confusion matrix of classification results^[124].

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图 11 基于低电压忆阻器的突触功能模拟及计算应用 (a)—(c) 生物突触与忆阻器结构相似性示意图; (d) PET-ITO/MAPbI₃/PEAI/Au忆阻器在具有不同的持续时间的单个脉冲下的SDDP行为(d₁ = 68 ms, d₂= 136 ms, d₃ = 545 ms, d₄ = 615 ms), 电压保持为–0.5 V; (e) 在具有不同频率的连续脉冲下的SRDP行为(f₁ = 1.06 Hz, f₂ = 6.39 Hz), 每组脉冲为10个, 电压保持为–0.5 V, 脉宽为68 ms; (f) 增强和抑制的循环^[152]; (g) Au/PBFCL₁₀/Ag忆阻器电导连续调制过程; (h) 具有自反馈的HNN结合了基于线性、指数和忆阻器的CSA算法; (i) 在420次迭代后, 使用HNN网络预测的突触权重矩阵、输出矩阵和行进路线的结果^[131]

Figure 11. Based on the low-voltage memristor synaptic function simulation and computational applications: (a)–(c) Schematic diagram of the similarity between biological synapses and memristor structures; (d) SDDP behavior of PET-ITO/MAPbI₃/PEAI/Au memristor under a single pulse with different durations (d₁ = 68 ms, d₂ = 136 ms, d₃ = 545 ms, d₄ = 615 ms) while maintaining a voltage of –0.5 V; (e) SRDP behavior under continuous pulses with different frequencies (f₁ = 1.06 Hz, f₂ = 6.39 Hz), each consisting of 10 pulses, while maintaining a voltage of –0.5 V and a pulse width of 68 ms; (f) enhancement and inhibition cycles^[152]; (g) conductive modulation process of Au/PBFCL₁₀/Ag memristor; (h) HNN with self-feedback combining linear, exponential, and memristor-based CSA algorithms; (i) results of synaptic weight matrix, output matrix, and path prediction using the HNN network after 420 iterations^[131].

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表 1 低电压忆阻器性能总结

Table 1. Summary of low-voltage memristor performance.

器件结构	工作机制	开关电压	开关比	开关速度/ns	保留时间	耐久性 (循环)	功耗/ 能耗	应用	文献
Pt/HfAlO_x/TaN	VCM	BRS: +1/–1 V	—	50	—	—	4.28 aJ	手写数字识别	[171]
Ta/Ta₂O₅:Ag/Ru	ECM	BRS: +0.7 V/–0.7 V	—	100	≈5×10⁴ s	5×10⁷	—	—	[42]
Pt/YSZ/Zr	VCM	BRS: +0.7 V/–0.7 V	—	2	10⁴ s	10⁸	—	—	[172]
Ag/SnO_x/SnSe	ECM	BRS: +0.4/–0.1 V	>10³	—	10⁵ s	4000	—	—	[157]
EGaIn/MACsPbI/ PEDOT: PSS/ITO	VCM	BRS: +0.6/–0.41 V	>10⁵	—	10⁵ s	10⁴	3.8 mW	—	[114]
ITO/FA_1–yMA_yPbI_3–xCl_x/ (PEA)₂PbI₄/Au	VCM	BRS: +1.0/–0.5 V	—	—	—	200	1 fJ	突触功能模拟	[49]
Ag/PMMA/MAPbI₃: Ag/Au	ECM	TS:±0.22 V	—	40	—	2500	10 μW	伤害传感器	[59]
Ag/CsPbI₃/Ag	ECM	TS:100 mV	—	—	100 ms	—	2 nW	储备池计算	[111]
PET-ITO/MAPbI₃/ PEAI/Au	VCM	BRS: +1/–1 V	—	—	—	50	13.5 aJ	神经元积分- 发放功能	[152]
Ag/MoO_x/ CsI (CsBr)/Ag	ECM	BRS: –0.16/+0.07 V	>10¹⁰	<200	>10⁶ s	>10⁵	<3.31 pW	模拟手写数字分类	[62]
Pt/CuI/Cu	ECM	BRS: +0.64/–0.19 V	10³	—	17 h	125	8.73 µW	图像硬件加密和解密	[10]
Ag/PMMA/ Cs₂AgBiBr₆/ITO	ECM	BRS: +0.6/–0.6 V	>10	—	—	—	188 pJ	手写数字识别	[58]
Pt/MoS₂/Ti	VCM	BRS: +0.65/–0.90 V	160	—	10 years	1×10⁷	—	—	[117]
Au/HfSe₂/Au	VCM	BRS: +0.742/–0.817 V	10²	—	—	500	0.82 pJ	矩阵计算	[80]
Ag/BNO_x/Graphene	ECM	BRS: 0.6/0.1 V	100-1000	—	—	100	—	—	[75]
Ag/Protein nanowires/Ag	ECM	TS:60 ± 4 mV	—	—	—	10⁴	—	神经元-突触联立积分发放	[126]
Au/PBFCL₁₀/Ag	ECM	BRS: +0.2/–0.2 V	—	21	>10⁶ s	—	2.35 μW	HNN	[131]
ITO/PEDOT:PSS/ pTPD/CsPbBr₃NCs/Ag	ECM	TS:<1 V	10³	—	10⁵ s	TS:2×10⁶BRS:5.6×10³	—	储备池计算	[154]
Au/MSFP/Au	VCM	BRS: +1.0/–1.0 V	—	—	10⁴ s	100	—	图像处理	[106]
ITO/PVK:TCNQ/Ag	ECM	BRS: +0.69/–0.52 V TS:0.21 V	≈10³	—	10⁴ s	10⁴	15.2 μW	突触、神经元功能模拟	[109]
Au/TPPS/Au	VCM	BRS: –0.1/+0.3 V	—	—	—	—	16.25 pW— 2.06 nW	突触模拟	[105]
W(Ag)/PI/Pt/Ti	ECM	TS:0.56 V	≈10³	—	0.44 ms	300	80 nW	图像处理	[107]
Pt/CuZnS/Ag	ECM	V_set=0.089 V	≈10⁶	—	>1000 s	100	0.1 nW	模式识别	[132]
Pt/DDP-CuNPs/Au	VCM	TS:4 mV	—	—	—	100	—	SNN	[145]
Ag/c-YY NW/Ag	ECM	TS:≤0.1 V	10⁶	—	—	—	750 fJ	SNN	[124]
Ag/Ag-IPS/Au	ECM	BRS: +0.43/–0.21 V	10⁸	100	10⁵ s	900	18.5 fJ	图像处理	[138]
Ag/PMMA/MAPbI₃: Ag/Au	ECM	TS:≈0.2 V	—	40	—	2500	—	伤害感受器	[59]
Al/Ti₃C₂:Ag/Pt	VCM	BRS: +2.0/–2.0 V	—	—	—	10⁶	0.35 pJ	突触模拟	[137]
Ag/TiO₂:Ag/Pt	ECM	BRS: +0.1/–0.1 V	—	—	—	—	26.0 pJ	突触模拟	[140]
ITO/NiSAs/ N-C/PVP/Au	VCM	BRS: +0.7/–1.1 V	10³	100	>10⁶ s	500	—	全加器	[99]
Au/silk: AgNO₃/Ag	ECM	TS:0.17 V	3 × 10⁶	—	10³ s	100	—	突触模拟	[147]
Ag/MXene/Pt	ECM	BRS: +1.33/–0.94 V	>10⁵	—	10⁴ s	10³	1~10 fJ	ANN	[149]
Ag/a-CO_x/ta-C/Pt	ECM	BRS:1.5 V/–1.0 V	—	—	100 s	—	6 nW	—	[155]
Au/h-BN/Au	VCM	TS:0.1 V	10⁷	40	>20000 s	500	—	逻辑门	[158]
Ag/GeTe/MoTe₂/Pt	ECM	BRS: +0.15/–0.14 V	10²	—	10⁴ s	10⁵	≈30 nJ	突触模拟	[162]
Ag/SnS/Pt	ECM	BRS: +0.2/–0.1 V	10⁸	1.5	10⁵ s	10⁴	100 fJ	图像分类	[73]

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