The dependencies of the resistance on the applied voltage of a series of molybdenum-disulfide-oxide (MoSxOy) powders was studied. The MoSxOy samples were prepared by reaction of (NH4)6Mo7O24 with thiourea in aqueous solution followed by aerial oxidation. These materials had the structure of nanoflowers consisting of self-assembled 10-20 nm thin nanoflakes. Chemical composition of the materials was evaluated by XPS and confirmed by Raman spectroscopy. The MoSxOy nanoflakes revealed unusual electric transport features. The current-voltage characteristics (I-V curves) grew monotonically with the applied voltage for the samples, containing ca. 25 % of Mo in oxidized species (in contrast to MoS2), being non-ohmic at lower voltages and becoming close to the ohmic at higher voltages. On the other hand, the I-V curves acquire N-like shaped with a strongly pronounced negative differential resistivity (NDR) part for the samples containing more than 50 % of Mo in oxidized forms. In all cases the I-V curves manifested hysteretic behavior with the difference between increasing and decreasing voltage sweeps, and the hysteresis loop parameters depended on the Ts. Important, that the studied nanoflakes were characterized by very long living deep charging and discharging after the voltage switching "on" and "off". The flexo-chemical model of the polar and electro-transport properties of the pressed MoSxOy nanoflakes was proposed, and the observed I-V curves were theoretically described. The revealed experimentally and explained features of resistive switching and charge accumulation in the MoSxOy materials look promising for applications in memristors and high-performance supercapacitors.

Two-dimensional (2D) layered ferroelectrics, as an emerging area of research, have attracted extensive attention, while memristors based on new 2D ferroelectric materials have yet to be fully explored, thereby limiting their applications in modern nanoelectronics. In this work, we report the observation of intrinsic memristive behavior in a newly discovered 2D in-plane ferroelectric material, NbOI2, and the giant enhancement of the memristive performance using LED visible light. The results show that NbOI2 exhibits intrinsically strong memristive response with a current on/off ratio of up to 10^4 and stable switching cycles, which is independent of back-gate voltage. Under LED visible light illumination, the current on/off ratio in NbOI2 is over one order of magnitude higher than that without light, meanwhile, the coercive field is significantly reduced to less than 1.22 kVcm-1, much lower than other 2D ferroelectric material-based memristors. Interestingly, both the intrinsic and the light-enhanced resistive switching phenomena only occur along the in-plane b-axis direction, indicating that the memristive behavior in NbOI2 is driven by electric field-induced and optical field-enhanced ferroelectric polarization switching mechanisms, as evidenced by a combined orientation-dependent electrical/optoelectrical measurement and sweep cycle-induced structural evolution analysis. Our study not only provides a materials strategy based on new 2D ferroelectrics for designing memristor applications, but also offers a simple optical method to enhance its performance, paving the path for its implementation in novel nanoelectronics and optoelectronics.

Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analysis due to prohibitive time and energy costs. These technologies generate a massive amount of noisy analog signals that traditionally require basecalling and digital mapping, both demanding frequent and costly data movement on von Neumann hardware. To overcome these challenges, we present a memristor-based hardware-software co-design that processes raw sequencer signals directly in analog memory, effectively combining the separated basecalling and read mapping steps. Here we demonstrate, for the first time, end-to-end memristor-based genomic analysis in a fully integrated memristor chip. By exploiting intrinsic device noise for locality-sensitive hashing and implementing parallel approximate searches in content-addressable memory, we experimentally showcase on-site applications including infectious disease detection and metagenomic classification. Our experimentally-validated analysis confirms the effectiveness of this approach on real-world tasks, achieving a state-of-the-art 97.15% F1 score in virus raw signal mapping, with 51x speed up and 477x energy saving compared to implementation on a state-of-the-art ASIC. These results demonstrate that memristor-based in-memory computing provides a viable solution for integration with portable sequencers, enabling truly real-time on-site genomic analysis for applications ranging from pathogen surveillance to microbial community profiling.

Non-volatile memristor-based memories with resistive switching materials are promising for next-generation data storage and neuromorphic technologies. Here, the importance of precise tuning of TiNx bottom electrodes in HfO2-based memristive devices is demonstrated.

The status and prospects of the memristor industry are analysed and the obstacles and pathways to their implementation are discussed.

Multi-functional integration! The memristor integrates four functions of multi-level storage, logic gates, UV sensing, and neuromorphic computing in one device.

Advanced operando transmission electron microscopy (TEM) techniques enable the observation of nanoscale phenomena in electrical devices during operation. They can be used to study the switching mechanisms in two-dimensional (2D) materials-based memristive devices, which is crucial to tailor their operating regimes and improve reliability and variability. Here, we investigate lateral memristive devices composed of 2D layered molybdenum disulfide (MoS${_2}$) with palladium (Pd) and silver (Ag) electrodes. We visualized the formation and migration of Ag conductive filaments (CFs) between the two electrodes under external bias voltage and their complete dissolution upon reversing the bias voltage polarity. The CFs exhibited a wide range of sizes, ranging from several Ångströms to tens of nanometers, and followed diverse pathways: along the MoS${_2}$ surfaces, within the van der Waals gap between MoS${_2}$ layers, and through the spacing between MoS${_2}$ bundles. Notably, the Ag electrode functioned as a reservoir for the CFs, as evidenced by the shrinking and growing of the Ag electrode upon switching. Our method enabled correlating the current-voltage responses with real-time TEM imaging, offering insights into failed and anomalous switching behavior, and providing clarity on the cycle-to-cycle variabilities. Our findings provide solid evidence for the electrochemical metallization mechanism, elucidate the formation dynamics of CFs, and reveal key parameters influencing the switching performance. Our approach can be extended to investigate similar memristive devices.

In India, a new brain-inspired chip called 'memristor' is making waves. Developed by Sreetosh Goswami at IISc Bengaluru, this innovative chip promises to propel AI by being more energy-efficient and intelligent. However, scaling it into a globally competitive technology requires infrastructure and a domestic semiconductor fabrication facility.

The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit design, and telecommunication, solutions can often be found more efficiently by representing them with a combination of exclusive OR (XOR) and conjunctive normal form (CNF) clauses. We propose a hardware accelerator architecture that natively embeds and solves such hybrid CNF$-$XOR problems using in-memory computing hardware. To achieve this, we introduce an algorithm and demonstrate, both experimentally and through simulations, how it can be efficiently implemented with memristor crossbar arrays. Compared to the conventional approaches that translate CNF$-$XOR problems to pure CNF problems, our simulations show that the accelerator improves computation speed, energy efficiency, and chip area utilization by $\sim$10$\times$ for a set of hard cryptographic benchmarking problems. Moreover, the accelerator achieves a $\sim$10$\times$ speedup and a $\sim$1000$\times$ gain in energy efficiency over state-of-the-art SAT solvers running on CPUs.

Cryo-computing - both classical and quantum, is severely limited by the absence of a suitable cryo-memory. The challenge both in terms of energy efficiency and speed have been known for decades, but so far conventional technologies have not been able to deliver adequate performance. Here we present a novel non-volatile memory device which incorporates a superconducting nanowire and an all-electronic charge configuration memristor (CCM) based on switching between charge-ordered states in a layered dichalcogenide material. We investigate the time-dynamics and current-voltage characteristics of such a device fabricated using a NbTiN nanowire and a 1T-TaS2 CCM. The observed dynamical response of the device is faithfully reproduced by modelling of the superconducting order parameter showing versatility of application. The inherent ultrahigh energy efficiency and speed of the device, which is compatible with single flux quantum logic, leads to a promising new memory concept for use in cryo-computing and quantum computing peripheral devices.

Ученые ИТМО разработали новое семейство металл-органических кристаллов, способных самопроизвольно переходить из 3D-структуры в 2D. Эти ультратонкие материалы (от 4 нанометров) можно использовать для создания энергоэффективных мемристоров и технологии ReRAM, предназначенных для записи и хранения

Researchers have conducted groundbreaking research on memristor-based brain-computer interfaces (BCIs). This research presents an innovative approach for implementing energy-efficient adaptive neuromorphic decoders in BCIs that can effectively co-evolve with changing brain signals.

A smaller, lighter and more energy efficient computer, demonstrated at the University of Michigan, could help save weight and power for autonomous drones and rovers, with implications for autonomous vehicles more broadly.