J. Semicond. > 2023, Volume 44 > Issue 5 > 053102
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REVIEWS

Volatile threshold switching memristor: An emerging enabler in the AIoT era

Wenbin Zuo1, 2, §, Qihang Zhu1, §, Yuyang Fu3, Yu Zhang1, Tianqing Wan4, Yi Li1, 2, , Ming Xu1, 2 and Xiangshui Miao1, 2

+ Author Affiliations

 Corresponding author: Yi Li, liyi@hust.edu.cn

DOI: 10.1088/1674-4926/44/5/053102

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Abstract: With rapid advancement and deep integration of artificial intelligence and the internet-of-things, artificial intelligence of things has emerged as a promising technology changing people’s daily life. Massive growth of data generated from the devices challenges the AIoT systems from information collection, storage, processing and communication. In the review, we introduce volatile threshold switching memristors, which can be roughly classified into three types: metallic conductive filament-based TS devices, amorphous chalcogenide-based ovonic threshold switching devices, and metal-insulator transition based TS devices. They play important roles in high-density storage, energy efficient computing and hardware security for AIoT systems. Firstly, a brief introduction is exhibited to describe the categories (materials and characteristics) of volatile TS devices. And then, switching mechanisms of the three types of TS devices are discussed and systematically summarized. After that, attention is focused on the applications in 3D cross-point memory technology with high storage-density, efficient neuromorphic computing, hardware security (true random number generators and physical unclonable functions), and others (steep subthreshold slope transistor, logic devices, etc.). Finally, the major challenges and future outlook of volatile threshold switching memristors are presented.

Key words: AIoT, threshold switching, memristor, selector, neuromorphic computing, hardware security



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Fig. 1.  (Color online) Overview of volatile TS memristors enabling the entire process of data and information collection, storage, processing, and communication in AIoT systems.

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Fig. 2.  (Color online) Categories and switching mechanisms of volatile TS memristors.

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Fig. 3.  (Color online) Cross-point memory technology in 1S1R configuration. (a) Schematic illustration of sneak path current in cross-point array during read operation. “0” means off-state, while “1” stands for on-state. (b) Memory with integration of selector to limit the sneak current. (c) Requirements of an ideal selector in cross-point memory.

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Fig. 4.  (Color online) Performance enhancement of CF-based selectors from active electrode engineering. (a) SEM image and cross-sectional TEM image of Pd/Ag/HfO2/Ag/Pd/TaOx/Ta2O5/Pd devices. (b) DC I–V characteristic of the Ag/HfO2/Pd selector yields a selectivity of 1010. (c) The device shows a switching slope of 1 mV/decade. (d) switching speed and endurance performance of the selectors. (e) DC I–V characteristic of the vertically integrated selector and RRAM (1S1R). (f) Cross-sectional TEM image of the Ag nanodots/HfO2/Pt device. (g) Threshold switching behavior at different compliance currents from 100 nA to 1 mA. (h) The device has an extremely small switching slope of <1 mV/decade. (i) Read resistance following turn-on operation with compliance currents. (j) DC I–V characteristic of the integrated 1S1R device before and after 108 cycles. (k) Highly improved stable TS in AgTe alloy device with 104 DC cycles. (l) Endurance of the AgTe alloy TS device (109 cycles). (m) Cross-sectional TEM image of the AgGeSe/Al2O3/Pt device. (n) The selector with a 100 nm feature size shows repeatable TS characteristics at a compliance current of 1 mA, providing a 12.7 MA/cm2 on-current density. Reproduced with permission from (a–e) Ref. [75] Copyright 2017 Wiley-VCH, (f–j) Ref. [77] Copyright 2019 Wiley-VCH, (k, l) Ref. [80] Copyright 2020 IEEE Publishing, (m-o) Ref. [81] Copyright 2021 IEEE Publishing.

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Fig. 5.  (Color online) Performance enhancement of CF-based selectors from interface engineering. (a) False colored SEM and cross-sectional TEM images of the Ag/TiN/HfO2/Pt device. (b) Forming voltage dependence on TiN diffusion barrier thickness for 25 devices. (c) I–V characteristics of the selector with 3 nm TiN barrier. (d) Endurance comparison of the selectors with and without the TiN barrier. (e) Cell structure and typical DC I–V characteristics of the Ag/defective-Graphene/SiO2/Pt devices. Reproduced with permission from (a–d) Ref. [82] Copyright 2019 IEEE Publishing, (e) Ref. [83] Copyright 2018 Wiley-VCH.

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Fig. 6.  (Color online) Performance enhancement of CF selectors from dielectric engineering. (a) Repeatable highly nonlinear threshold switching I–V characteristics for devices with different host lattices doped with silver. (b) Cross-sectional SEM image of the Pt/Ag:ZnO/Pt selector device. (c) Normal probability plot of Vth variation for selector devices in the case of device-to-device, sample-to-sample and batch-to-batch. (d) Transient characteristics of the selector devices with AC pulse test showing switching speed and relaxation time. (e) Cross-sectional TEM images and composition results of the Ag/HfOy/HfOx/Pt selector. (f) Schematic demonstration of the coupling mechanism between crystallite kinetics and Ag filaments. (g) Cross-sectional TEM image and DC I–V characteristic of the HfOx/HfOy/HfOx selector. (h) Schematic illustrations of the proposed TS model. Reproduced with permission from (a) Ref. [45] Copyright 2017 Springer Nature, (b–d) Ref. [84] Copyright 2021 IEEE Publishing, (e, f) Ref. [87] Copyright 2018 ACS Publishing, (g, h) Ref. [86] Copyright 2022 Wiley-VCH.

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Fig. 7.  (Color online) Te-based and GeSe-based OTS selectors. (a) Schematic of the TiN/Ge-Se/W device structure in an As-doped GeSe OTS selector. (b) The OTS I–V curves of the GeSe-based device. (c) Four Te-based OTS materials show good thermal stability. (d) The J–V characteristics of the W/C-Te/W device with good selectivity of >105. (e) Cross-sectional TEM images of single-element Te device. (f) JON parameters versus electrode diameter in various OTS devices. Reproduced with permission from (a, b) Ref. [104] Copyright 2018 IEEE Publishing, (c, d) Ref. [93] Copyright 2019 ACS Publishing, (e, f) Ref. [110] Copyright 2021 Science Publishing.

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Fig. 8.  (Color online) (a) Schematic diagram of biological neurons. Presynaptic currents are integrated in neurons. When the membrane potential reaches a certain threshold, an action potential is generated. (b) The circuit of LIF neuron. The capacitor acts as the membrane and implements integration effect via the charging process. The resistor is used to limit the current through the VO2-based MIT device, which is the output signal of the neuron. (c) LIF neurons with voltage spike output. (d) Neurons based on single volatile TS memristors and its voltage input and current output. (e) HH neurons based on two VO2 devices, detailed simulation of biological neuronal cell membrane ion channels with over 30 neural dynamic characteristics. (f) The device used to achieve neuron intrinsic plasticity and the frequency of the output spike of this neuron versus the strength of the presynaptic input signal. (g) FeFET-based LIF neurons with inhibitory synaptic inputs, and their pulse input and output signals. (h) LIF neuron circuit with spiking frequency adaptation, and the output spiking waveform (frequency becomes stable over time). Reproduced with permission from (b) Ref. [122] Copyright 2016 IEEE publishing, (c) Ref. [123] Copyright 2017 IEEE publishing, (d) Ref. [124] Copyright 2018 Wiley-VCH, (e) Ref. [127] Copyright 2018 Springer Nature, (f) Ref. [129] Copyright 2021 IEEE publishing, (g) Ref. [130] Copyright 2019 IEEE publishing, (h) Ref. [131] Copyright 2022 IEEE publishing.

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Fig. 9.  (Color online) (a) An 8 × 8 artificial neural network integrated by Pt/HfOx/Pd non-volatile memristors and Pt/Ag/SiO2:Ag/Ag/Pt MCF-TS memristors. (b) Schematic of the constructed fully memristive Temporal Coding 256 × 5 SNNs and the hardware implementation. (c) Artificial neuron integrated with dendrite and soma, where Pt/TiOx/Ti volatile memristor is used as dendrite and Pd/NbOx/Nb/Pd MIT device is used to generate action potential. (d) A simple neural network demonstration, using the same device in the synaptic and neuronal parts, and showing I–V sweeping operating in resistive switching mode and threshold switching mode, respectively. Reproduced with permission from (a) Ref. [138] Copyright 2018 Springer Nature, (b) Ref. [143] Copyright 2022 Wiley-VCH, (c) Ref. [141] Copyright 2020 IEEE publishing, (d) Ref. [144] Copyright 2022 Wiley-VCH.

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Fig. 10.  (Color online) (a–c) The environmental input signal are mechanical force, light, and heat, respectively. The sensory neurons, and their output signal characteristics are also shown. Essentially, the load resistors of the common LIF neurons are replaced with various types of sensing resistors. (d) A 20 × 20 sensory neuron array for object classification simulation, which shows 8 objects with different shapes and temperatures, having 400 afferent neurons, 50 hidden layer neurons, and 8 output neurons. The final result is a reliable classification. Reproduced with permission from (a) Ref. [154] Copyright 2022 Wiley-VCH, (b) Ref. [152] Copyright 2022 Wiley-VCH, (c) Ref. [157] Copyright 2022 Springer Nature, (d) Ref. [158] Copyright 2022 Wiley-VCH.

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Fig. 11.  (Color online) (a) Schematic illustration of TRNG and its difference with PRNG. (b) OTS-based TRNG using the Weibull distribution of on-time to generate random bits. (c) One of the mainstream TRNG structures containing comparator, AND, clock and counter. (d) NbOx-based TRNG circuits. (e) The circuit design of a recent reported TRNG based on CF-TS devices. (f) 15 excitation pulses are applied to the diffusive memristor (V1). The stochastic fire spikes (V2) are sent to a comparator and will induce the random number of voltage pulses at node 3 (V3). V3 will be then sent to a one-bit counter as the clock signal. The output of the one-bit counter represents the odd or even of the fire spikes. “1” and “0” represent that the numbers of fire spikes are ODD and EVEN, respectively. (g) Demonstration of the basic conception of PUF. (h) Hardware implementation of the PUF. (i) Read-conductance of each device at 0.5 V in the array. (j) Operation principle of the PUF in the array. (k) The uniqueness (inter-PUF) and reliability (intra-PUF) performances of the TS-based PUF. Reproduced with permission from (b) Ref. [159] Copyright 2019 IEEE publishing, (c) Ref. [160] Copyright 2017 Springer Nature, (d) Ref. [163] Copyright 2021 Springer Nature, (e, f) Ref. [81] Copyright 2022 Wiley-VCH, (h–k) Ref. [164] Copyright 2021 IEEE publishing.

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Fig. 12.  (color online) Selected performance of three types of TS devices. The selected values are the maximum or minimum values of the performances in Table 1 from Refs. [75, 84, 86, 90, 93, 104, 116, 181, 183, 188, 189]. Switching energy is roughly estimated from voltage pulse measurements, E = VIt where V and t are the amplitude and width of the applied voltage pulse, respectively, I is the current response to the pulse[27].

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Table 1.   Summary of the recent reported volatile threshold switching devices and their corresponding characteristics. “–” means that no such characteristic was found.

Device/MaterialsVth (V)IonIoffON/OFFEnduranceSpeedPolarity
Cu/Cu:HfO2/HfO2/Pt[85]0.410 μA1 pA107–50 ns/100 nsUnidirectional
Cu/SiO2/Pt[21]0.7500 μA10 pA5 × 107504 ms/1 msUnidirectional
Ag/ZrO2/Pt[175]0.251 mA100 pA107200–Unidirectional
Ag/SiTe/TiN[176]0.6100 μA10 nA1041055 μs/3 μsUnidirectional
Ag/DDG/SiO2/Pt[83]0.6500 μA1 pA5 × 108106100 ns/1 μsBidirectional
Ag/HfO2/Pd[75]0.151 mA0.1 pA101010875 ns/250 nsUnidirectional
Ag/HfO2:N/Pt[173]0.2500 μA1 pA5 × 1081061.5 μs/5 μsUnidirectional
Ag/MoS2/Au[32]0.35100 μA100 pA1065 × 106–Unidirectional
Pt/Cu2O/Ag:Cu2O/Cu2O/Pt[177]0.51 μA1 nA103––Bidirectional
Ag/TaOx/TaOy/TaOx/Ag[178]0.151 mA1 pA10910675 ns/500 nsBidirectional
Ag nanodot/HfO2/Pt[76]0.251 mA1 pA109108110 ns/240 nsBidirectional
AgTe35%/TiN/TiO2/Pt[169]0.4100 μA0.1 pA10910810 ns/100 nsUnidirectional
AgGeSe/Al2O3/Pt[81]0.43 mA0.1 pA1010105300 ns/30 μsUnidirectional
Ag/TiN/HfO2/Pt[82]0.2100 μA10 pA10710528 ns/50 μsUnidirectional
Pt/Ag:ZnO2/Pt[84]0.71 mA10 fA101110638 ns/64 nsUnidirectional
Ag/TiN/HfOx/HfOy/HfOx/Pt[86]0.253 mA0.1 pA101010660 ns/500 nsUnidirectional
Ge–Se[90]1.4450 μA129 nA1031082 nsBidirectional
Ge58Se42[91]3.510 μA100 pA10510950 nsBidirectional
Ge–Se–N[92]4450 μA2 nA105108–Bidirectional
Ge–Se–As[93]2.510 mA1 nA1075×105–Bidirectional
Ge–Se–Sb–N[94-96]1.94100 μA10 pA106109–Bidirectional
GeTe6[97]1.6650 μA–1056005 nsBidirectional
Ge–As–Se–Te[98]11 μA200 pA1031010–Bidirectional
Ge–As–Te–Si[99]1.2100 μA1 μA102102–Bidirectional
Ge–As–Te–Si–Se[100, 101]2.2420 μA1.9 nA105101050 nsBidirectional
Si-Te[102]1100 μA100 nA1031082 nsBidirectional
Si–As–Te[103]1.49–103 nA–103–Bidirectional
C–Te[104]0.65400 μA5 nA1051082 nsBidirectional
B–Te[105]0.75380 μA2 nA1051082 nsBidirectional
Zn35Te65[106]0.66 mA60 nA105––Bidirectional
AlTe[107]0.7200 μA4 nA1051072 nsBidirectional
Te[110]1.31 mA1 μA10310911 nsBidirectional
GeTe–C–N[179]1.3200 μA10 nA1041011–Bidirectional
GeTex–C[34]1.262 mA2 nA4.2×1041078.5 nsBidirectional
GeS[180]3.210 mA10 nA10610810 nsBidirectional
SiOTe[111]1.2–3 nA1041093 nsBidirectional
SiGeAsTe[181]2.2100 μA0.8 nA1051011–Bidirectional
TiN/VO2/HfO2/TiN[182]0.7400 μA20 μA20–30 nsBidirectional
Pt/Ti:NbOx/TiN[115]1.366 mA–10103–Bidirectional
V/VOx/TiN[183]110 mA10 μA100010960 nsBidirectional
Pt/VOxNy/VOx/VOxNy/Pt[184]1.3820 μA2 μA10103–Bidirectional
Pt/Ti:NbOx/Pt[114]1.5–50 pA5000010520 nsBidirectional
Pt/NbOx/Ru[185]0.7500 μA–10010690 nsBidirectional
Pt/Nb2O5/Pt[186]––––1090.7/2.3 nsBidirectional
Pt/(V1–xCrx)2O3/TiN[187]1.7450 μA11 μA–101210 nsBidirectional
Pt/NbO2/TiN[116]1.081 mA2 μA500101210 nsBidirectional
V/VOx/HfWOx/Pt[188]11 mA10 μA100101225/30 nsBidirectional
Pt/NbOx/ZrO2/TiN[189]0.53500 μA6 μA84––Bidirectional
DownLoad: CSV

Table 2.   Comparisons of TRNGs based on different types of TS devices. “–” means that no such characteristic was found.

DeviceEntropy sourceThroughput (kb/s)Energy consumption (pJ/bit)EnduranceNIST test
TiN/GeSe/TiN [159]Threshold voltage1000–1 × 101112 tests passed
Pt/Ag/Ag:SiO2/ Pt [160]Switching time60.176 × 10715 tests passed
Pt/HfO2/TiN [161]Switching time6––8 tests passed
Pt/HfO2/TiN [162]Switching time16––15 tests passed
Pt/Ti/NbOx/Pt [163]Oscillation time4052302.4 × 10715 tests passed
Cu0.1Te0.9/HfO2/Pt [74]Switching time32––15 tests passed
Ag/TiN/HfOx/HfOy/HfOx/Pt [81]Firing spiks108–1 × 10615 tests passed
DownLoad: CSV
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    Received: 25 December 2022 Revised: 05 February 2023 Online: Accepted Manuscript: 28 February 2023Uncorrected proof: 01 March 2023Published: 10 May 2023

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      Article Navigation >  Journal of Semiconductors  > 2023  >  44(5): 053102
      Wenbin Zuo, Qihang Zhu, Yuyang Fu, Yu Zhang, Tianqing Wan, Yi Li, Ming Xu, Xiangshui Miao. Volatile threshold switching memristor: An emerging enabler in the AIoT era[J]. Journal of Semiconductors, 2023, 44(5): 053102. doi: 10.1088/1674-4926/44/5/053102 ****W B Zuo, Q H Zhu, Y Y Fu, Y Zhang, T Q Wan, Y Li, M Xu, X S Miao. Volatile threshold switching memristor: An emerging enabler in the AIoT era[J]. J. Semicond, 2023, 44(5): 053102. doi: 10.1088/1674-4926/44/5/053102
      Citation:
      Wenbin Zuo, Qihang Zhu, Yuyang Fu, Yu Zhang, Tianqing Wan, Yi Li, Ming Xu, Xiangshui Miao. Volatile threshold switching memristor: An emerging enabler in the AIoT era[J]. Journal of Semiconductors, 2023, 44(5): 053102. doi: 10.1088/1674-4926/44/5/053102 ****
      W B Zuo, Q H Zhu, Y Y Fu, Y Zhang, T Q Wan, Y Li, M Xu, X S Miao. Volatile threshold switching memristor: An emerging enabler in the AIoT era[J]. J. Semicond, 2023, 44(5): 053102. doi: 10.1088/1674-4926/44/5/053102
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      Volatile threshold switching memristor: An emerging enabler in the AIoT era

      DOI: 10.1088/1674-4926/44/5/053102
      • 1.

        Hubei Key Laboratory for Advanced Memories, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China

      • 2.

        Hubei Yangtze Memory Laboratories, Wuhan 430205, China

      • 3.

        School of Microelectronics and Faculty of Physics and Electronics Science, Hubei University, Wuhan 430062, China

      • 4.

        Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China

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      • Wenbin Zuo：is currently a postdoctoral researcher in School of Integrated Circuits in Huazhong University of Science and Technology. He received his PhD degree from Wuhan University in 2021. His research interests focus on metal oxides thin films and devices, as well as nonvolatile memory technology
      • Qihang Zhu：is currently a postgraduate student in School of Integrated Circuits at Huazhong University of Science and Technology. He received his Bachelor degree in Huazhong University of Science and Technology in 2021. His research interests mainly focus on memristive neural networks
      • Yi Li：is currently an associate professor at Huazhong University of Science and Technology (HUST). He received his PhD degree in microelectronics from HUST in 2014. His major research interests focus on memristors and their applications in neuromorphic computing and in-memory computing
      • Corresponding author: liyi@hust.edu.cn
      • Received Date: 2022-12-25
      • Revised Date: 2023-02-05
        • Available Online: 2023-02-28

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