Nanowerk August 15, 2022 In this review article an international team of researchers (Germany, Switzerland) addressed resistive switching devices operating according to the bipolar valence change mechanism (VCM). VCM cells consist of three parts: an electronically active electrode (AE), a mixed ionic-electronic conducting (MIEC) layer consisting of a nanometer-scale metal oxide, and an ohmic counter electrode (OE). They provided detailed insights into the status of understanding of these devices as a fundament for their use in the different fields of application. The review covered the microscopic physics of memristive states and the switching kinetics of VCM devices. Electroforming, a process […]
Category Archives: Memristor
Better memristors for brain-like computing
Nanowerk March 8, 2022 Researchers in China reviewed the latest developments in the design of memristors for artificial synapses, the main component of a neuromorphic computing architecture, used in neuromorphic computing. Memristors are a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. The discussion is organized according to the blending schemes as well as the working mechanisms of […]
Squeezing light inside memory devices could help improve performance
Nanowerk October 6, 2020 Memristors used in a range of memory-centric technologies are driven by an externally applied potential leading to a change in electrical conductivity. The ability to look inside the memristors and understand how morphological changes characterize their function has been vital in their development. An international team of researchers (UK, USA- Perdue University) has developed a non-destructive optical spectroscopy technique that can detect the motion of a few hundred oxygen vacancies with nanometre-scale sensitivity. They constructed cavities small enough to trap light within the device. They used the tiny gap between a gold nanoparticle and a mirror […]
Engineers put tens of thousands of artificial brain synapses on a single chip
MIT News June 8, 2020 To make memristors an international team of researchers (USA – MIT, Lawrence Berkeley National Laboratory, IBM, China, South Korea) first fabricated a negative electrode out of silicon, a positive electrode by depositing a slight amount of copper, followed by a layer of silver. They sandwiched the two electrodes around an amorphous silicon medium patterning a millimeter-square silicon chip with tens of thousands of memristors. When they ran the chip through several visual tasks, the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper, and cleaner compared […]
Special blend of circuits and memristive devices created for brain-mimicking processing systems
Phys.org March 24, 2020 Memristive systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis problems. An international team of researchers (Germany, Switzerland) argue that they are also ideal building blocks for integration in neuromorphic electronic circuits suitable for ultra-low power brain-inspired sensory processing systems. They present a recipe for creating such systems based on design strategies and computing principles inspired by those used in mammalian brains, enumerate the specifications and properties of memristive devices required to support always-on learning in neuromorphic computing systems and to minimize their power consumption. They discuss in what […]
Memristor based equation solver could cut energy used by 100 times for longer lasting smartphones
University of Michigan, July 30, 2018 Memristors enable memory and processing in the same device. However, memristors can have resistances that are on a continuum. Researchers at the University of Michigan got around the problem by digitizing the current outputs and mapped large mathematical problems into smaller blocks within the array, called “memory-processing units,” improving the efficiency and flexibility of the system. This is particularly useful for implementing machine learning and artificial intelligence algorithms, weather prediction and other matrix-based operations… read more.