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.
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.
Although multilevel capability is probably the most important property of resistive random access memory (RRAM) technology, it is vulnerable to reliability issues due to the stochastic nature of conducting filament (CF) creation. As a result, the various resistance states cannot be clearly distinguished, which leads to memory capacity failure. In this work, due to the gradual resistance switching pattern of TiO2−x-based RRAM devices, we demonstrate at least six resistance states with distinct memory margin and promising temporal variability. It is shown that the formation of small CFs with high density of oxygen vacancies enhances the uniformity of the switching characteristics in spite of the random nature of the switching effect. Insight into the origin of the gradual resistance modulation mechanisms is gained by the application of a trap-assisted-tunneling model together with numerical simulations of the filament formation physical processes.
In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.
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