Double Dielectric Layer Metal-oxide Memristor: Design and Applications

doi:10.15541/jim20220760

Abstract

Abstract:

Memristor, fusing the functions of storage and computing within a single device, is one of the core electronic components to solve the bottleneck of von Neumann architecture. With the unique volatile/non-volatile resistive switching characteristic, memristor can simulate the function of synapses/neurons in brain well. In addition, due to the compatibility with traditional complementary metal-oxide-semiconductor (CMOS) processes, metal-oxide-based memristors have received a lot of attention. In recent years, many kinds of metal-oxide memristors based on single dielectric layer have been proposed. However, there are still some problems such as the instability of switching voltage, fluctuation of high/low resistance state and poor endurance of memristive device. Thus, the researchers have successfully optimized the device performance by introducing the double dielectric layer into the metal-oxide memristors. In this article, we introduce the advantages of double dielectric layers-based metal-oxide memristors, and discuss their mechanism and design of double dielectric layers-based metal-oxide memristors. Eventually, we introduce their potential applications in neuromorphic computing. This review provides some enlightenment on how to design high-performance metal-oxide memristor based on double dielectric layers.

Key words: memristor, synapse, neuron, neuromorphic computing, double dielectric layer metal-oxide, review

CLC Number:

TQ174

YOU Junqi, LI Ce, YANG Dongliang, SUN Linfeng. Double Dielectric Layer Metal-oxide Memristor: Design and Applications[J]. Journal of Inorganic Materials, 2023, 38(4): 387-398.

Figures/Tables 10

Fig. 1 Comparison of the structure and performance between the single/double dielectric layer metal-oxide memristor (a, d) Schematic diagrams for (a) single and (d) double dielectric layer metal-oxide memristors; (b, e) Comparison of I-V curves between (b) ZrO2-based memristor and (e) Ta2O5/ZrO2-based memristor with bi-layer structure exhibiting more uniform switching voltage[17]; (c, f) Comparison of the endurance between (c) HfO2-based memristor and (f) HfO2:Al/HfO2-based memristor with double dielectric layer exhibiting better cycling endurance[18]

Table 1 Performance comparison of the single/double dielectric layer metal-oxide memristors

Memristor structure		Range of Set voltage, ΔV_Set/V	Range of Reset voltage, ΔV_Reset/V	Endurance	On/Off ratio	Retention/s	Ref.
Single dielectric layer	Ta/ZrO₂/TiN	-1.0 ~-1.6 (0.6)	0.8 ~ 1.5 (0.7)	100	10²	-	[15]
	Cu/Al₂O₃/Pt	0.4 ~ 1.2 (0.8)	-0.1 ~-0.8 (0.7)	2×10³	10⁵	10⁵	[19]
	Ag/ZnO/Pt	0.3 ~ 1.0 (0.7)	-0.4 ~-0.8 (0.4)	10²	50	10⁴	[20]
	TaN/Ta₂O₅/Pt	2.0 ~ 4.5 (2.5)	-2.5 ~-4.5 (2)	10⁴	-	10⁴	[21]
	Ta/ZrO₂/Pt	0.4 ~ 2.0 (1.6)	-0.4 ~-1.0 (0.6)	100	-	-	[22]
Double dielectric layer	Ag/SiO₂/Ta₂O₅/Pt	0.14 ~ 0.24 (0.1)	-0.06 ~-0.14 (0.08)	10³	10³	10⁴	[14]
	Ta/ZrO₂/ZTO/TiN	-0.8 ~-1.2 (0.4)	0.8 ~ 1.2 (0.4)	10⁵	10²	3×10³	[15]
	Ta/Ta₂O₅/ZrO₂/Pt	0.7 ~ 1.2 (0.5)	-0.5 ~-0.8 (0.3)	10⁶	10²	10⁴	[17]
	TaN/Ta₂O₅/WO₃/Pt	1.6 ~ 2.3 (0.7)	-1.9 ~-2.5 (0.6)	10⁹	-	10⁶	[21]
	Ti/HfO₂/TiO_x/Pt	-0.8 ~-1.1 (0.3)	1.4 ~ 1.5 (0.1)	10⁷	10³	10⁵	[23]

Fig. 2 Advantages of the double dielectric layer metal-oxide memristor in building neural network (a) I-V curves of Pt/Al2O3/TaOx/Ta memristor with self-rectifying characteristic[26]; (b) Comparison of the pulse response between the HfO2 and the AlOx/HfO2-based memristor[30]

Fig. 3 Mechanism and characteristic of the double dielectric layer metal-oxide memristor based on the localization effect of electric field[14] (a) Schematic illustration for the switching mechanism of Ag/SiO2/Ta2O5/Pt memristor; (b) I-V characteristic of Ag/SiO2/Ta2O5/Pt memristor

Fig. 4 Two mechanisms and characteristics comparison of the double dielectric layer metal-oxide memristor based on oxygen vacancy gradient (a, b) Schematic diagrams of the resistance switching mechanism with (a) the structure of W/AlOx/AlOy/Pt memristor[47], (b) Ti/HfO2/TiOx/Pt memristor[23]; (c, d) Endurance of W/AlOx/AlOy/Pt memristor[47] and Ti/HfO2/TiOx/Pt memristor[23], and the Ti/HfO2/TiOx/Pt memristor with transition layer exhibiting more stable resistance states

Fig. 5 Mechanism and performance of the double dielectric layer metal-oxide memristor based on Joule heating effect[57] (a) Schematic diagrams of the switching mechanism of Ta/ZrO2(Y)/Ta2O5/TiN memristor; (b) I-V characteristic of Ta/ZrO2(Y)/Ta2O5/TiN memristor with nonlinear low-resistance state

Fig. 6 Mechanism of the double dielectric layer metal-oxide memristor with the linear symmetrical pulse response[58] (a) Schematic representation of the switching mechanism of Ag/SiO2/VOx/Pt memristor; (b) Pulse response of Ag/SiO2/VOx/Pt memristor represents highly linear and symmetric properties

Fig. 7 Data set classification using memristor based on double dielectric layer metal-oxide[51] (a) Schematic of Pd/TaOx/Ta2O5/Pd memristor crossbar array; (b, c) The initial data and classification results of the data set

Fig. 8 Demonstration of speech recognition using double dielectric layer metal-oxide memristor[61] (a) Schematic of the memristor-based reservoir computing system; (b) Diagram of Ti/TiOx/TaOy/Pt memristor structure; (c) Typical audio waveform of digit 9; (d) Recognition error rate of speech varies as a function of the mask length with error bar representing variation between memristor devices

Fig. 9 Demonstration of image recognition using double dielectric layer metal-oxide memristor (a) SEM image of the 64×64 memristor crossbar array; (b) All experimental output currents for the digit “7”[59]; (c) Recognition diagrams of conductance and synaptic weights of digit “3”; (d) Evolution of the recognition accuracy of the MNIST under different synaptic coupling and training epochs[62]

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