Low-power flexible organic memristor based on PEDOT:PSS/pentacene heterojunction for artificial synapse

Electric Field-Driven Conformational Changes in Molecular Memristor and Synaptic Behavior

This paper demonstrates the use of molecular artificial synapses in neuromorphic computing systems designed for low energy consumption. A molecular junction, based on self-assembled monolayers (SAMs) of alkanethiolates terminated with 2,2'-bipyridine complexed with cobalt chloride, exhibits synaptic behaviors with an energy consumption of 8.0 pJ µm-2. Conductance can be modulated simply by applying pulses in the incoherent charge transport (CT) regime. Charge injection in this regime allows molecules to overcome the low energy barrier for C─C bond rotations, resulting in conformational changes in the SAMs. The reversible potentiation/depression process of conductance achieves 90% accuracy in recognizing patterns from the Modified National Institute of Standards and Technology (MNIST) handwritten digit database. The molecular junction further exhibits both rectifying and conductance hysteresis behaviors, showing potential for use in selector-free synaptic arrays that efficiently suppress sneak currents.

Recent Progress on Heterojunction-Based Memristors and Artificial Synapses for Low-Power Neural Morphological Computing

Memristors and artificial synapses have attracted tremendous attention due to their promising potential for application in the field of neural morphological computing, but at the same time, continuous optimization and improvement in energy consumption are also highly desirable. In recent years, it has been demonstrated that heterojunction is of great significance in improving the energy consumption of memristors and artificial synapses. By optimizing the material composition, interface characteristics, and device structure of heterojunctions, energy consumption can be reduced, and performance stability and durability can be improved, providing strong support for achieving low-power neural morphological computing systems. Herein, we review the recent progress on heterojunction-based memristors and artificial synapses by summarizing the working mechanisms and recent advances in heterojunction memristors, in terms of material selection, structure design, fabrication techniques, performance optimization strategies, etc. Then, the applications of heterojunction-based artificial synapses in neuromorphological computing and deep learning are introduced and discussed. After that, the remaining bottlenecks restricting the development of heterojunction-based memristors and artificial synapses are introduced and discussed in detail. Finally, corresponding strategies to overcome the remaining challenges are proposed. We believe this review may shed light on the development of high-performance memristors and artificial synapse devices.

Energy Efficient Memristor Based on Green-Synthesized 2D Carbonyl-Decorated Organic Polymer and Application in Image Denoising and Edge Detection: Toward Sustainable AI

According to the United Nations, around 53 million metric tons of electronic waste is produced every year, worldwide, the big majority of which goes unprocessed. With the rapid advances in AI technologies and adoption of smart gadgets, the demand for powerful logic and memory chips is expected to boom. Therefore, the development of green electronics is crucial to minimizing the impact of the alarmingly increasing e-waste. Here, it is shown the application of a green synthesized, chemically stable, carbonyl-decorated 2D organic, and biocompatible polymer as an active layer in a memristor device, sandwiched between potentially fully recyclable electrodes. The 2D polymer's ultramicro channels, decorated with C═O and O─H groups, efficiently promote the formation of copper nanofilaments. As a result, the device shows excellent bipolar resistive switching behavior with the potential to mimic synaptic plasticity. A large resistive switching window (103), low SET/RESET voltage of ≈0.5/-1.5 V), excellent device-to-device stability and synaptic features are demonstrated. Leveraging the device's synaptic characteristics, its applications in image denoising and edge detection is examined. The results show a reduction in power consumption by a factor of 103 compared to a traditional Tesla P40 graphics processing unit, indicating great promise for future sustainable AI-based applications.

CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications

Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and good nonvolatile memory properties. Moreover, it can also mimic various biological synaptic learning properties and can be used for pattern classification applications with the help of the spiking neural network. The time series analysis technique is employed to model and predict the switching variations of the RS device. Moreover, the collagen NFs have shown good cytotoxicity and anticancer properties, suggesting excellent biocompatibility as a switching layer. The biocompatibility of collagen NFs is explored with the help of NRK-52E (Normal Rat Kidney cell line) and MCF-7 (Michigan Cancer Foundation-7 cancer cell line). Additionally, the biodegradability of the device is evaluated through a physical transient test. This work provides a vital step toward developing a biocompatible and biodegradable switching material for sustainable nonvolatile memory and neuromorphic computing applications.

Performance Regulation of a ZnO/WOx-Based Memristor and Its Application in an Emotion Circuit

The development of a memristor is very important for artificial intelligence and new electronic circuits. In this work, Ag(Al)/ZnO/WO_x/FTO memristors are fabricated by magnetron sputtering, and the device performance is further improved through annealing and oxygen supply during sputtering. The experimental data show that the FTO/WO_x/ZnO-O₂/Ag memristor has the largest high resistance state (HRS)/low resistance state (LRS) resistance ratio and the best durability. Through data fitting and analysis, the switching mechanism of memristors with different top electrodes is investigated. Furthermore, the physical model of the best performance memristor was established by Simulink, and an emotion-monitoring circuit was constructed on this basis. The circuit can be used to monitor and record the mood changes, and the feedback of the emotion monitoring can be fed back to the user to help them adjust the mood.

Facile and single step produced Ba:Sn-codoped PEDOT:PSS thin film electrode with improved optics and electrochemical properties for transparent and flexible supercapacitor applications

Figure shows preparation and characterization steps of different ratio (0–3 mg ml−1) Ba:Sn-codoped PEDOT:PSS thin films.