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Front. Phys.    2024, Vol. 19 Issue (2) : 23501    https://doi.org/10.1007/s11467-023-1344-9
TOPICAL REVIEW
Recent advances in halide perovskite memristors: From materials to applications
Sixian Liu1, Jianmin Zeng1(), Qilai Chen2(), Gang Liu1()
1. Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. Aerospace Science & Industry Shenzhen (Group) Co. Ltd., Shenzhen 518000, China
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Abstract

With the emergence of the Internet of Things (IoT) and the rapid growth of big data generated by edge devices, there has been a growing need for electronic devices that are capable of processing and transmitting data at low power and high speeds. Traditional Complementary Metal-Oxide-Semiconductor (CMOS) devices are nonvolatile and often limited by their ability for certain IoT applications due to their unnecessary power consumption for data movement in von Neuman architecture-based systems. This has led to a surge in research and development efforts aimed at creating innovative electronic components and systems that can overcome these shortcomings and meet the evolving needs of the information era, which share features such as improved energy efficiency, higher processing speeds, and increased functionality. Memristors are a novel type of electronic device that has the potential to break down the barrier between storage and computing. By storing data and processing information within the same device, memristors can minimize the need for data movement, which allows for faster processing speeds and reduced energy consumption. To further improve the energy efficiency and reliability of memristors, there has been a growing trend toward diversifying the selection of dielectric materials used in memristors. Halide perovskites (HPs) have unique electrical and optical properties, including ion migration, charge trapping effect caused by intrinsic defects, excellent optical absorption efficiency, and high charge mobility, which makes them highly promising in applications of memristors. In this paper, we provide a comprehensive overview of the recent development in resistive switching behaviors of HPs and the underlying mechanisms. Furthermore, we summarize the diverse range of HPs, their respective performance metrics, as well as their applications in various fields. Finally, we critically evaluate the current bottlenecks and possible opportunities in the future research of HP memristors.
Keywords halide perovskite      memristor      mechanism      neuromorphic computing      non-volatile memory     
Corresponding Author(s): Jianmin Zeng,Qilai Chen,Gang Liu   
Issue Date: 10 October 2023
 Cite this article:   
Sixian Liu,Jianmin Zeng,Qilai Chen, et al. Recent advances in halide perovskite memristors: From materials to applications[J]. Front. Phys. , 2024, 19(2): 23501.
 URL:  
https://academic.hep.com.cn/fop/EN/10.1007/s11467-023-1344-9
https://academic.hep.com.cn/fop/EN/Y2024/V19/I2/23501
Fig.1  Classification of perovskite materials according to material dimensions. (a) Zero-dimensional QDs. (b) One-dimensional nanowires. (c) Two-dimensional nanowires. (d) Three-dimensional polycrystalline. (a) Reproduced from Ref. [74]. (b) Reproduced from Ref. [75]. (c) Reproduced from Ref. [76]. (d) Reproduced from Ref. [77].
Fig.2  (a) The resistive mechanism of the Ag/CH3NH3PbI3/FTO memristor, in which the upper part of the process involves the electroforming process. (b) Schematic diagram of the filamentary memristors. (c) Current−voltage characteristics of the “set” process in double-logarithmic scale. (d) Current−voltage characteristics of the “reset” process in double-logarithmic scale. (e) The relationship of I and V1/2 in the LRS semi-logarithmic scale in the high voltage region. A schematic diagram of the band evolution corresponding to various states of the device. (f) Initial state and HRS-1 state. The device change (g) from HRS-1 to LRS, (h) from LRS to HRS-1, and (i) Transition from HRS-2 to LRS. (j) Optical response of devices in HRS-1 and (k) HRS-2. (a) Reproduced from Ref. [112]. (b) Reproduced from Ref. [115]. (c) Reproduced from Ref. [76]. (c−e) Reproduced from Ref. [117]. (f−k) Reproduced from Ref. [123].
Fig.3  (a) Schematic diagram of biological neurons and their experimental circuits. Using the above neuron model to demonstrate biologically inspired (b) Spatial integration and fire. (c) Time integration and fire. (d) Conductance changes under the same set of pulses stimulation. (e) The function of the increment of conductance and the value of absolute conductance (under the condition that the conductance is enhanced). The functional relationship between the change of conductance and (f) the number of reset pulses. (g) The level of absolute conductance (under the condition of suppression). (h) The HRS/LRS distribution of the memristor in pulse programming mode under different humidity. (a−c) Reproduced from Ref. [147]. (d−g) Reproduced from Ref. [151]. (h) Reproduced from Ref. [157].
Fig.4  (a) I−V curve in light (365 nm) and dark conditions. (b) Light response with and without laser diodes at 365 nm, 405 nm, 420 nm, and 500 nm. (c) Rise and decay time of light response under 365 nm. (d) Set voltage statistics in light (365 nm) and dark conditions. (e) Schematic diagram structure and (f) optical image of CsPbBr3-based memristor. (a−d) Reproduced from Ref. [123]. (e, f) Reproduced from Ref. [162].
Fig.5  (a) The I−V characteristics of Ag/(CH3NH3)2FeCl4/Cu devices measured in the temperature range from 290 K to 340 K, and (b) the ratio of HRS/LRS at different temperatures when the reading voltage is 0.5 V. (c) The biological synaptic conceptual diagram simulated by the memristor. (d) The long-term potentiation and long-term depression properties of the memristor under continuous pulse sequence stimulation. I−V characteristic curves of Cs2AgBiBr6-based memristor under (e) different humidity and (f) continuous alcohol lamp burning. (a, b) Reproduced from Ref. [175]. (c, d) Reproduced from Ref. [176]. (e, f) Reproduced from Ref. [181].
Fig.6  (a) Designed device structure of simple two-terminal photodetector and the active layer is (C4H9NH3)2PbBr4. (b) Current−voltage characteristics of the device in the dark for four consecutive cycles in a semi-logarithmic scale. (c) Reversible write and erasure using pulses of ± 2.5 V (read voltage is 1 V). (d) Schematic device structure and bias configuration of δ-FAPbI3. Resistive characteristics of δ-FAPbI3: (e) Retention time of LRS and HRS; (f) Multi-valued memory characteristics measured at different current levels. (a−c) Reproduced from Ref. [195]. (d−f) Reproduced from Ref. [196].
Fig.7  (a) Schematic showing MAPbI3 QWs memristors: (i) Device structure of ITO/Ag/MAPbI3/Au/PET; (ii) enlarged schematic of a single QW; (iii) crystal structure of MAPbI3. (b) The durability of different resistance levels of QWs devices is studied, showing the constancy of the on/off ratio and repeatability of the system. (c) For different voltage pulses, multiple different LRS are displayed, and constant amplitude voltage pulses are used to read and erase. (d) The concept of storing data in alphabetical is illustrated matrix design order and the corresponding data time retention. (e) EPSCs of the device under single pulse, illustrated with a schematic diagram of the connection between EPSCs behavior and biological synapse. (f) The change of photocurrent of the device under different light intensity. (g) Structure diagram of synapses and devices. (a−d) Reproduced from Ref. [197]. (e, f) Reproduced from Ref. [199]. (g) Reproduced from Ref. [200].
Fig.8  (a) The evolution and durability of the conductance of devices as volatile devices in diffusion mode. (b) The evolution and durability of conductance of devices as nonvolatile devices in drift mode. (c) The ANN model built to demonstrate storage cell calculation; and (d) Four different neural discharge input modes and their resulting conductance changes in the device (voltage stimulus is 1 V, a read voltage is ±0.5 V). (a−d) Reproduced from Ref. [203].
Fig.9  (a) Schematic of the phototransistor with a CNTs/CsPbBr3-QDs channel. (b) Energy band diagram at the light-off (top panel) and light-on states (bottom panel). (c) Dependence of the EPSC triggered by an optical spike on the source-drain voltage (VD). (d) EPSC of a synaptic transistor at VD = 0.01 V triggered by an optical spike. (e) PPF behavior of a synaptic transistor at VD = 0.01 V. (f) Transition from STM to LTM for a synaptic transistor at VD = 0.01 V with the increasing quantity of optical spikes. (g) Schematic showing the growth of HP QDs on graphene to form the G-HP QDs heterostructures and the proposed applications. (a, b) Reproduced from Ref. [207]. (c−f) Reproduced from Ref. [208]. (g) Reproduced from Ref. [211].
Fig.10  (a) Schematic diagram of the mechanism of optically tunable synaptic behavior. (b) The conductive state retention of the device when the light is selectively turned on. (c) Current curves of memristors with and without PAE processing in pulse programming mode. (d) The structure diagram of the light-induced logic gate. (e) Light-induced switching of the device under HRS (read voltage is 10 mV). (f) The light-induced switch of the device under LRS (the reading voltage is 0.1, 1, and 10 mV). (a, b) Reproduced from Ref. [215]. (c) Reproduced from Ref. [216]. (d−f) Reproduced from Ref. [217].
Fig.11  (a) Optical photographs of centimeter-sized MAPbBr3 single crystal blocks with gold electrodes deposited. (b) J−V characteristic curves of the device. (c) Various resistance states of the device under different bias stimuli. (d) Schematic diagrams of input, reservoir, and readout for the classification of different pulse sequences. (e) Original images of flowers. (f) The sharpened flower image through the filter when the cutoff frequency is 4.8 Hz. (g) The sharpened flower image through the filter when the cutoff frequency is 19 Hz. (a) Reproduced from Ref. [218]. (b−d) Reproduced from Ref. [225]. (e−g) Reproduced from Ref. [226].
Fig.12  (a) The postsynaptic activity is caused by network exposure to specific input patterns. After some epochs, the postsynaptic activity became higher for the selected mode (−22.5°), and the choice of the left and right eyes was the same. (b) The time evolution of the synaptic weight of the left eye and the right eye: the selectivity increased significantly after continuous exposure to the input mode after the random weight matrix was initialized. (c) The device structure of the CsFAMA photovoltaic sensor and the schematic diagram of the crossbar array. (d) The adaptive dynamic process of the overexposed image. (e) The recognition accuracy of the car with the door open, aircraft and bird images increase with the increase of adaptation time. (a, b) Reproduced from Ref. [227]. (c−e) Reproduced from Ref. [231].
Fig.13  (a) Schematic diagram of the recognition results of the self-powered artificial retina system and ANNs. (b) The charge transfer characteristics of HPs can be used as the source of entropy for designing a new type of PUFs. The coexistence of many kinds of switching physics in HPs makes the resistance states highly random, and the encryption key is generated from the difference of the HRS of these memories. (c) Before a cycle of reconstruction. (d) After a cycle of reconstruction, analog-digital maps and digital maps show different results. (a) Reproduced from Ref. [232]. (b−d) Reproduced from Ref. [233].
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