Recently, several types of lead halide perovskites have been demonstrated as active layers in resistive switching memory or artificial synaptic devices for neuromorphic computing applications. However, the thermal instability and toxicity of lead halide perovskites severely restricted their further practical applications. Herein, the environmentally friendly and uniform Cs3Cu2I5 perovskite films are introduced to act as the active layer in the Ag/Cs3Cu2I5/ITO memristor. Generally, the Ag ions could react with iodide ions and form AgIx compounds easily, so the Ag/PMMA/Cs3Cu2I5/ITO memristor was designed by employing the ultrathin polymethylmethacrylate (PMMA) layer to avoid the direct contact between the top Ag electrode and Cs3Cu2I5 perovskite films. After optimization, the obtained memristor demonstrated bipolar resistive switching with low operating voltage ( 104 s). Additionally, biological synaptic behaviors including long-term potentiation and long-term depression have been investigated. By using the MNIST handwritten recognition data set, the handwritten recognition rate based on experimental data could reach 94%. In conclusion, our work provides the opportunity of exploring the novel application for the development of next-generation neuromorphic computing based on lead-free halide perovskites.

Opportunity of the Lead-Free All-Inorganic Cs3Cu2I5 Perovskite Film for Memristor and Neuromorphic Computing Applications.

Artificial synaptic devices and systems have become hot topics due to parallel computing, high plasticity, integration of storage, and processing to meet the challenges of the traditional Von Neumann computers. Currently, two-terminal memristors and three-terminal transistors have been mainly developed for high-density storage with high switching speed and high reliability because of the adjustable resistivity, controllable ion migration, and abundant choices of functional materials and fabrication processes. To achieve the low-cost, large-scale, and easy-process fabrication, solution-processed techniques have been extensively employed to develop synaptic electronics towards flexible and highly integrated three-dimensional (3D) neural networks. Herein, we have summarized and discussed solution-processed techniques in the fabrication of two-terminal memristors and three-terminal transistors for the application of artificial synaptic electronics mainly reported in the recent five years from the view of fabrication processes, functional materials, electronic operating mechanisms, and system applications. Furthermore, the challenges and prospects were discussed in depth to promote solution-processed techniques in the future development of artificial synapse with high performance and high integration.

Solution-processed electronics for artificial synapses

Machine vision is an indispensable part of today's artificial intelligence. The artificial visual systems used in industrial production and domestic daily life rely significantly on cameras and image‐processing components for live monitoring and target identifying. They, however, often suffer from bulky volume, high energy consumption, and more critically, lack of adaptive responsiveness under extreme lighting conditions and thus possible mortal visual disability of flash blinding or nyctalopia for applications such as auto‐piloting. Herein, it is demonstrated that perovskite switchable photovoltaic devices are used to effectively construct all‐in‐one sensory neural network. Arising from the spontaneous and electric field‐induced ion‐migration effect, the photoresponsivity of the perovskite device can be reconfigured over the wide range of 540–1270%, which not only allows high‐fidelity adaptive image sensing of the visual information but also acts as updatable synaptic weight to enable the sensor array for performing machine‐learning tasks. With the bioinspired electronic pupil regulation function achieved through adjustable photoresponsivity of the perovskite sensor array, a proof‐of‐concept adaptive machine vision system with a maximum 263% enhancement of the object recognition accuracy for compact, mobile yet delay‐sensitive applications is demonstrated.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202000122

Switchable Perovskite Photovoltaic Sensors for Bioinspired Adaptive Machine Vision

Perovskite solar cells (PSCs) have received a large amount of research funds due to their potential as a frontrunner in a new generation of solar cells; consequently, the desire to commercialize this technology is mounting. In this roadmap, the knowledge and the technological gaps between laboratory and industry are critically analyzed from the perspective of 5S criteria (Stability, Safety, Sustainability, Scalability, and Storage). To avoid any favoritism in the arguments toward commercializing this technology, herein, the average parameters of PSCs (photoconversion efficiency, durability, cost, manufacturability, and sustainability) estimated from previous studies are analyzed and discussed. Unique opportunities for PSCs in their current stage of achievements are identified, where application-driven, instead of performance-driven, developments are shown to favor their commercialization. Efforts required to improve the average performance of PSCs to state-of-the-art levels are also identified and discussed.

A Perspective on the Commercial Viability of Perovskite Solar Cells

Characterisation & modelling of perovskite-based synaptic memristor device

Recent research is a testimony to the fact that perovskite material based solar cells are most efficient since they exhibit high power conversion efficiency and low cost of fabrication. Various perovskite materials display hysteresis in their current-voltage characteristic which accounts for memory behaviour. In this paper, we demonstrate efficient non-volatile memory devices based on hybrid organic-inorganic perovskite (CH3NH3PbI3) as a resistive switching layer on a Glass/Indium Tin Oxide (ITO) substrate. Our perovskite solar cells have been developed over the fully solution processed electron transport layer (ETL) which is a combination of SnO2 and mesoporous (m)-TiO2 scaffold layers. Hysteresis behaviour was observed in the current-voltage analysis achieving high ratio of ON & OFF current under dark and ambient conditions. Proposed perovskite-based Glass/ITO/SnO2/m-TiO2/CH3NH3PbI3/Au device has a hole transport layer (HTL) free structure, which is mainly responsible for a large ratio of ON & OFF current. The presence of voids in the scaffold m-TiO2 layer are also accountable for increasing electron/hole path length which escalates the recombination rate at the surface of the ETL/perovskite interface resulting in large hysteresis in the I-V curve. This memristor device operates at a low energy due to SnO2 layer's higher electron mobility and wide energy band gap. Our experimental results also show the dependency of voltage scan range & rate of scanning on the hysteresis behaviour in dark conditions. This memristive behaviour of the proposed device depicts drift in hysteresis loop with respect to the number of cycles, which would have a significant impact in neuromorphic applications. Moreover, due to the identical fabrication process of the proposed perovskite-based memristor device and perovskite solar cells, this device could be integrated inside a photovoltaic array to work as a power-on-chip device, where generation and computation could be possible on the same substrate for memory and neuromorphic applications.

https://iopscience.iop.org/article/10.1088/1361-6528/aba713/pdf

Investigation of hysteresis in hole transport layer free metal halide perovskites cells under dark conditions.

In artificial intelligence, high speed neuromorphic computing architectures are needed to perform various operations such as learning, transferring information, and processing of data. Due to high power dissipation, high operating energy, and lower density of integration CMOS device has limited application in neuromorphic computing in nanoscale domain. On the other hand memristor devices are promising candidates for implementing synaptic devices in a neuromorphic computing architecture due to their swift information storage, high-speed processing of data and high density with lower power consumption. To the best of our knowledge this paper proposes the first studies made on a perovskite $\left( C H _ { 3 } N H _ { 3 } P b I _ { 3 } \right)$ based photovoltaic memristive device with $I T O / S n O _ { 2 } / C H _ { 3 } N H _ { 3 } P b I _ { 3 } / A u$ structure in the dark condition. This perovskite based memristor is able to mimic the neuromorphic learning and remembering process same as the biological synapses. The proposed synaptic memristor device has potential to operate at low energy, low cost, solution processability, low activation energy, high efficiency and used as a power-on-chip synaptic device in artificial neural network.

Perovskite based Low Power Synaptic Memristor Device for Neuromorphic application

Resistive memory, also known as memristor, is an emerging potential successor to traditional CMOS charge based memories. Memristors have also recently been proposed as a promising candidate for several additional applications such as logic design, sensing, non-volatile storage, neuromorphic computing, Physically Unclonable Functions (PUFs), Content-addressable memory (CAM) and reconfigurable computing. In this paper, we explore three unique applications of memristor technology based implementations, specifically from the perspective of sensing, logic, in-memory computing and their solutions. We review solar cell health monitoring and diagnosis, describe the proposed solutions, and provide directions in memristive gas sensing and in-memory computing. For the gas sensor application, in order to determine the number of memristors to ensure a certain level of accuracy in sensitivity, a technique to optimize the sensor array based on an acceptable sensitivity variation and minimum sensitivity margin is presented. These “out-of-the-box” emerging ideas for applications of memristive devices in enhancing robustness and, at the same time, how the requirements of robust design are enabling unconventional use of the devices. To this end, the papers considers some examples of this mutual interaction.

The Missing Applications Found: Robust Design Techniques and Novel Uses of Memristors

Memristors are an attractive option for use in future architectures due to their non-volatility, high density and low power operation. Gas sensing is one of the proposed application of memristive devices. In spite of these advantages, memristors are susceptible to defect densities due to the nondeterministic nature of nano-scale fabrication. In this paper, a novel spice memristor model incorporating fault models that emulates the gas sensing behaviour with/without faults is developed for simulation and integration with design automation tools. Our simulation results show that the proposed non-linear model detects the presence of the oxidising/reducing gas and analyses the defects/faults affecting the functionality of the sensor.

/pdf/fault-modeling-and-simulation-of-memristor-based-gas-sensors-c49kivnu03.pdf

Vishal Gupta

Papers

Characterisation & modelling of perovskite-based synaptic memristor device

Investigation of hysteresis in hole transport layer free metal halide perovskites cells under dark conditions.

Perovskite based Low Power Synaptic Memristor Device for Neuromorphic application

The Missing Applications Found: Robust Design Techniques and Novel Uses of Memristors

Fault Modeling and Simulation of Memristor based Gas Sensors