Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way, with potential for neuromorphic computing applications. However, its future impact in electronics depends crucially on how the materials at the core of this technology adapt to the requirements arising from continued scaling towards higher device densities. A common strategy to fine-tune the properties of phase change memory materials, reaching reasonable thermal stability in optical data storage, relies on mixing precise amounts of different dopants, resulting often in quaternary or even more complicated compounds. Here we show how the simplest material imaginable, a single element (in this case, antimony), can become a valid alternative when confined in extremely small volumes. This compositional simplification eliminates problems related to unwanted deviations from the optimized stoichiometry in the switching volume, which become increasingly pressing when devices are aggressively miniaturized. Removing compositional optimization issues may allow one to capitalize on nanosize effects in information storage.

Carbon-based electronics is a promising alternative to traditional silicon-based electronics as it could enable faster, smaller and cheaper transistors, interconnects and memory devices. However, the development of carbon-based memory devices has been hampered either by the complex fabrication methods of crystalline carbon allotropes or by poor performance. Here we present an oxygenated amorphous carbon (a-COx) produced by physical vapour deposition that has several properties in common with graphite oxide. Moreover, its simple fabrication method ensures excellent reproducibility and tuning of its properties. Memory devices based on a-COx exhibit outstanding non-volatile resistive memory performance, such as switching times on the order of 10 ns and cycling endurance in excess of 10(4) times. A detailed investigation of the pristine, SET and RESET states indicates a switching mechanism based on the electrochemical redox reaction of carbon. These results suggest that a-COx could play a key role in non-volatile memory technology and carbon-based electronics.

Nanoscale memory devices, whose resistance depends on the history of the electric signals applied, could become critical building blocks in new computing paradigms, such as brain-inspired computing and memcomputing. However, there are key challenges to overcome, such as the high programming power required, noise and resistance drift. Here, to address these, we present the concept of a projected memory device, whose distinguishing feature is that the physical mechanism of resistance storage is decoupled from the information-retrieval process. We designed and fabricated projected memory devices based on the phase-change storage mechanism and convincingly demonstrate the concept through detailed experimentation, supported by extensive modelling and finite-element simulations. The projected memory devices exhibit remarkably low drift and excellent noise performance. We also demonstrate active control and customization of the programming characteristics of the device that reliably realize a multitude of resistance states.

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In-memory computing is an emerging non-von Neumann approach in which certain computational tasks such as matrix-vector multiplication are performed using resistive memory devices organized in a crossbar array. However, the conductance variations associated with the memory devices limit the precision of this computation. Here, we demonstrate that the so-called projected phase-change memory (Proj-PCM) devices can achieve 8-bit precision while performing scalar multiplication. The devices were fabricated and characterized using electrical measurements and STEM investigation. They are found to be remarkably immune to conductance variations arising from structural relaxation, 1/f noise and temperature variations. Moreover, it is possible to compensate for the temperature-dependent conductance variations in a crossbar array using a simple model. Finally, we experimentally demonstrate a neural network-based image classification task involving 30 such Proj-PCM devices.