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.

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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.

phase change memory (pcM) is being actively explored for in-memory computing and neuromorphic systems. the ability of a pcM device to store a continuum of resistance values can be exploited to realize arithmetic operations such as matrix-vector multiplications or to realize the synaptic efficacy in neural networks. However, the resistance variations arising from structural relaxation, 1/f noise, and changes in ambient temperature pose a key challenge. the recently proposed projected pcM concept helps to mitigate these resistance variations by decoupling the physical mechanism of resistance storage from the information-retrieval process. even though the device concept has been proven successfully, a comprehensive understanding of the device behavior is still lacking. Here, we develop a device model that captures two key attributes, namely, resistance drift and the state dependence of resistance. the former refers to the temporal evolution of resistance, while the latter refers to the dependence of the device resistance on the phase configuration of the phase change material. The study provides significant insights into the role of interfacial resistance in these devices. The model is experimentally validated on projected pcM devices based on antimony and a metal nitride fabricated in a lateral device geometry and is also used to provide guidelines for material selection and device engineering.

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Nano-confined phase change memory cells based on pure Sb have been electrically characterized.

Phase-change memory devices have found applications in in-memory computing where the physical attributes of these devices are exploited to compute in places without the need to shuttle data between memory and processing units. However, nonidealities such as temporal variations in the electrical resistance have a detrimental impact on the achievable computational precision. To address this, a promising approach is projecting the phase configuration of phase change material onto some stable element within the device. Here, the projection mechanism in a prominent phase-change memory device architecture, namely mushroom-type phase-change memory, is investigated. Using nanoscale projected Ge 2 Sb 2 Te 5 devices, the key attributes of state-dependent resistance, drift coefficients, and phase configurations are studied, and using them how these devices fundamentally work is understood.

Chalcogenide phase change materials enable non‐volatile, low‐latency storage‐class memory. They are also being explored for new forms of computing such as neuromorphic and in‐memory computing. A key challenge, however, is the temporal drift in the electrical resistance of the amorphous states that encode data. Drift, caused by the spontaneous structural relaxation of the newly recreated melt‐quenched amorphous phase, has consistently been observed to have a logarithmic dependence in time. Here, it is shown that this observation is valid only in a certain observable timescale. Using threshold‐switching voltage as the measured variable, based on temperature‐dependent and short timescale electrical characterization, the onset of drift is experimentally measured. This additional feature of the structural relaxation dynamics serves as a new benchmark to appraise the different classical models to explain drift.