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Increasing memory performance and density will require new breakthroughs in atomic-scale technology and three-dimensional device architectures. Here, the authors demonstrate a memory just 3 Å thick that can be stacked by exploiting the atomically thin edge of monolayer graphene.

By integrating two-dimensional MoS₂ transistors with metal-oxide resistive random-access memories, two-transistor–two-resistor ternary content-addressable memory cells can be created, which could be used to search large amounts of data in parallel.

Data-centric applications benefit from dense, low-power memory. Here the authors use a combination of chalcogenide superlattices and nanocomposites to achieve low switching voltage (0.7 V) and fast speed (40 ns) in 40-nm-scale phase-change memory.

Industry compatible solid-state doping of regions between the channel and contacts in carbon nanotube transistors can be used to control device polarity and improve device performance.

Using chips that mimic the human brain to perform cognitive tasks, namely neuromorphic computing, calls for low power and high efficiency hardware. Here, Yaoet al. show on-chip analogue weight storage by integrating non-volatile resistive memory into a CMOS platform and test it in facial recognition.

A networked system of eight computing chips, each with its own on-chip memory, can be used to efficiently implement a range of neural network models and sizes.

Vertically structured electronic synapses, which exhibit both short- and long-term plasticity, can be created using layered two-dimensional hexagonal boron nitride.

The epitaxial growth of single-crystal hexagonal boron nitride monolayers on a copper (111) thin film across a sapphire wafer suggests a route to the broad adoption of two-dimensional layered semiconductor materials in industry.

Carbon nanotube thin-film transistor is promising for solution-processed, large-scale flexible electronics, but the device yields remain poor to date. Lei et al. show low-voltage flexible digital and analog circuits based on high-purity and high-yield separation of semiconducting carbon nanotubes.

A computer built entirely using transistors based on carbon nanotubes, which is capable of multitasking and emulating instructions from the MIPS instruction set, is enabled by methods that overcome inherent challenges with this new technology.

Metallic and semiconducting carbon nanotubes generally coexist in 'as-grown' materials. In this study, single-walled nanotubes are sorted using regioregular poly(3-alkylthiophene)s; rational selection of polymers, solvent and temperature allows the selective dispersion of semiconducting carbon nanotubes.

Continuous monitoring of physiological parameters in clinical practice requires wired connections to the sensors that are attached to or implanted in patients. Here, Chen et al. demonstrate a wireless, millimetre-scale sensor, which can monitor intracranial pressure of mice in real-time.

A compute-in-memory neural-network inference accelerator based on resistive random-access memory simultaneously improves energy efficiency, flexibility and accuracy compared with existing hardware by co-optimizing across all hierarchies of the design.

Incredibly thin transistors could deliver even more powerful computers if three research challenges can be solved, argue Ming-Yang Li and colleagues.

A metasurface comprising electrically controlled heating units and a phase-change material offer non-volatile and reversible modulation of reflectance by more than fourfold.

An optoelectronic resistive switching memory synaptic device enables the realization of an efficient neuromorphic visual system exhibiting non-volatile optical resistive switching and light-tunable synaptic behaviours.

Artificial neural networks can emulate the human vision because of their spike-based operation by employing memristors as synapses. Here, Seo et al. integrate synaptic and optical sensing functions in a single heterostructure, which enables accurate and energy-efficient recognition of colored patterns.

New non-volatile memory devices store information using different physical mechanisms from those employed in today's memories and could achieve substantial improvements in computing performance and energy efficiency.

Scientist and engineer who helped shape semiconductor technologies.

Multiple nanotechnologies are integrated on a single chip to realize a three-dimensional integrated circuit architecture that combines computing and data storage—a potentially transformative advance in computing.

Progress in integrating atomically thin two-dimensional materials with silicon-based technology is reviewed, together with the associated opportunities and challenges, and a roadmap for future applications is presented.

This Review Article examines the development of in-memory computing using resistive switching devices.

Carbon nanomaterials have greatly advanced non-volatile memory technology. In this Review, applications of various carbon nanomaterials as memory electrodes, interfacial engineering layers, memory selectors and resistive-switching media are discussed in the context of emerging non-volatile memory devices.