Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges

Building Uniformly Structured Polymer Memristors via a 2D Conjugation Strategy for Neuromorphic Computing

Polymer memristors represent a highly promising avenue for the advancement of next-generation computing systems. However, the intrinsic structural heterogeneity characteristic of most polymers often results in organic polymer memristors displaying erratic resistive switching phenomena, which in turn lead to diminished production yields and compromised reliability. In this study, a 2D conjugated polymer, named PBDTT-BPQTPA, is synthesized by integrating the coplanar bis(thiophene)-4,8-dihydrobenzo[1,2-b:4,5-b]dithiophene (BDTT) as an electron-donating unit with a quinoxaline derivative serving as an electron-accepting unit. The incorporation of triphenylamine groups at the quinoxaline termini significantly enhances the polymer's conjugation and planarity, thereby facilitating more efficient charge transport. The fabricated polymer memristor with the structure of Al/PBDTT-BPQTPA/ITO exhibits typical non-volatile resistive switching behavior under high voltage conditions, along with history-dependent memristive properties at lower voltages. The unique memristive behavior of the device enables the simulation of synaptic enhancement/inhibition, learning algorithms, and memory operations. Additionally, the memristor demonstrates its capability for executing logical operations and handling decimal calculations. This study offers a promising and innovative approach for the development of artificial neuromorphic computing systems.

Ultrasoft and High-Adhesion Block Copolymers for Neuromorphic Computing

The "von Neumann bottleneck" is a formidable challenge in conventional computing, driving exploration into artificial synapses. Organic semiconductor materials show promise but are hindered by issues such as poor adhesion and a high elastic modulus. Here, we combine polyisoindigo-bithiophene (PIID-2T) with grafted poly(dimethylsiloxane) (PDMS) to synthesize the triblock-conjugated polymer (PIID-2T-PDMS). The polymer exhibited substantial enhancements in adhesion (4.8-68.8 nN) and reductions in elastic modulus (1.6-0.58 GPa) while maintaining the electrical characteristics of PIID-2T. The three-terminal organic synaptic transistor (three-terminal p-type organic artificial synapse (TPOAS)), constructed using PIID-2T-PDMS, exhibits an unprecedented analog switching range of 276×, surpassing previous records, and a remarkable memory on-off ratio of 106. Moreover, the device displays outstanding operational stability, retaining 99.6% of its original current after 1600 write-read events in the air. Notably, TPOAS replicates key biological synaptic behaviors, including paired-pulse facilitation (PPF), short-term plasticity (STP), and long-term plasticity (LTP). Simulations using handwritten digital data sets reveal an impressive recognition accuracy of 91.7%. This study presents a polyisoindigo-bithiophene-based block copolymer that offers enhanced adhesion, reduced elastic modulus, and high-performance artificial synapses, paving the way for the next generation of neuromorphic computing systems.

Interface Engineering Enables Multilevel Resistive Switching in Ultra-Low-Power Chemobrionic Copper Silicate

Memristor is assuming prominence due to its exceptionally low power consumption, adaptable, and parallel signal processing capabilities that address the limitations of the von Neumann architecture to meet the growing demand for advanced technologies such as artificial intelligence, Internet of Things (IoTs), and neuromorphic computation. In this work, we demonstrate resistive switching in copper silicate-based hollow tube-forming self-organized membrane structures belonging to the category of chemobrionics or chemical gardens to demonstrate cost-effective and highly efficient memristor devices. The device architecture is configured as ITO/PEDOT:PSS/active layer (copper silicate)/PMMA/Ag, an arrangement that serves to stabilize current-voltage hysteresis and exhibit a low SET voltage ∼0.2 V with a 0.8 nJ power consumption while manifesting robust data endurance and multilevel resistive switching. The inherent self-rectifying behavior, characterized by a high rectification ratio of 60, underscores the potential utility of these devices across a spectrum of electronic applications. To emulate the functionality of biological synapses, fundamental synaptic characteristics are assessed, including paired-pulse facilitation (PPF) and potentiation and depression (P&D). We validate the potential of copper silicate chemical garden-based memristor devices for applications that require real-time synaptic processing. Importantly, the fabrication of these devices was accomplished through a comprehensive solution-based, low-temperature process conducted under ambient environmental conditions, obviating the need for specialized glovebox facilities.

IGZO/WO3-x -Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture

Currently, artificial neural networks (ANNs) based on memristors are limited to recognizing static images of objects when simulating human visual system, preventing them from performing high-dimensional information perception, and achieving more complex biomimetic functions is subject to certain limitations. In this work, indium gallium zinc oxide (IGZO)/tungsten oxide (WO3-x )-heterostructured artificial optoelectronic synaptic devices mimicking image segmentation and motion capture exhibiting high-performance optoelectronic synaptic responses are proposed and demonstrated. Upon electrical and optical stimulations, the device shows a variety of fundamental and advanced electrical and optical synaptic plasticity. Most importantly, outstanding and repeatable linear synaptic weight changes are attained by the developed memristor. By taking advantage of the notable linear synaptic weight changes, ANNs have been constructed and successfully utilized to demonstrate two applications in the field of computer vision, including image segmentation and object tracking. The accuracy attained by the memristor-based ANNs is similar to that of the computer algorithms, while its power has been significantly reduced by 105 orders of magnitude. With successful emulations of the human brain reactions when observing objects, the demonstrated memristor and related ANNs can be effectively utilized in constructing artificial optoelectronic synaptic devices and show promising potential in emulating human visual perception.

Multi-level resistive switching in hafnium-oxide-based devices for neuromorphic computing

In the growing area of neuromorphic and in-memory computing, there are multiple reviews available. Most of them cover a broad range of topics, which naturally comes at the cost of details in specific areas. Here, we address the specific area of multi-level resistive switching in hafnium-oxide-based devices for neuromorphic applications and summarize the progress of the most recent years. While the general approach of resistive switching based on hafnium oxide thin films has been very busy over the last decade or so, the development of hafnium oxide with a continuous range of programmable states per device is still at a very early stage and demonstrations are mostly at the level of individual devices with limited data provided. On the other hand, it is positive that there are a few demonstrations of full network implementations. We summarize the general status of the field, point out open questions, and provide recommendations for future work.