Highly reproducible and CMOS-compatible VO-based oscillators for brain-inspired computing.

With remarkable electrical and optical switching properties induced at low power and near room temperature (68 °C), vanadium dioxide (VO) has sparked rising interest in unconventional computing among the phase-change materials research community. The scalability and the potential to compute beyond the von Neumann model make VO especially appealing for implementation in oscillating neural networks for artificial intelligence applications, to solve constraint satisfaction problems, and for pattern recognition. Its integration into large networks of oscillators on a Silicon platform still poses challenges associated with the stabilization in the correct oxidation state and the ability to fabricate a structure with predictable electrical behavior showing very low variability.

Analytical modelling of the transport in analog filamentary conductive-metal-oxide/HfO ReRAM devices.

The recent co-optimization of memristive technologies and programming algorithms enabled neural networks training with in-memory computing systems. In this context, novel analog filamentary conductive-metal-oxide (CMO)/HfO redox-based resistive switching memory (ReRAM) represents a key technology. Despite device performance enhancements reported in literature, the underlying mechanism behind resistive switching is not fully understood.

Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm.

A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the NN parameters and efficiently perform the required synaptic operations. The "" algorithm was developed to facilitate NN training in the presence of device nonidealities.

A multi-timescale synaptic weight based on ferroelectric hafnium zirconium oxide.

Brain-inspired computing emerged as a forefront technology to harness the growing amount of data generated in an increasingly connected society. The complex dynamics involving short- and long-term memory are key to the undisputed performance of biological neural networks. Here, we report on sub-µm-sized artificial synaptic weights exploiting a combination of a ferroelectric space charge effect and oxidation state modulation in the oxide channel of a ferroelectric field effect transistor.

Structural Assessment of Interfaces in Projected Phase-Change Memory.

Non-volatile memories based on phase-change materials have gained ground for applications in analog in-memory computing. Nonetheless, non-idealities inherent to the material result in device resistance variations that impair the achievable numerical precision. Projected-type phase-change memory devices reduce these non-idealities.

State dependence and temporal evolution of resistance in projected phase change memory.

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.

Back-End, CMOS-Compatible Ferroelectric Field-Effect Transistor for Synaptic Weights.

Neuromorphic computing architectures enable the dense colocation of memory and processing elements within a single circuit. This colocation removes the communication bottleneck of transferring data between separate memory and computing units as in standard von Neuman architectures for data-critical applications including machine learning. The essential building blocks of neuromorphic systems are nonvolatile synaptic elements such as memristors.

Electrical and optical properties of epitaxial binary and ternary GeTe-SbTe alloys.

Phase change materials such as pseudobinary GeTe-SbTe (GST) alloys are an essential part of existing and emerging technologies. Here, we investigate the electrical and optical properties of epitaxial phase change materials: α-GeTe, GeSbTe5 (GST225), and SbTe. Temperature-dependent Hall measurements reveal a reduction of the hole concentration with increasing temperature in SbTe that is attributed to lattice expansion, resulting in a non-linear increase of the resistivity that is also observed in GST225.

Chemical and structural arrangement of the trigonal phase in GeSbTe thin films.

The thermal and electrical properties of phase change materials, mainly GeSbTe alloys, in the crystalline state strongly depend on their phase and on the associated degree of order. The switching of Ge atoms in superlattice structures with trigonal phase has been recently proposed to develop memories with reduced switching energy, in which two differently ordered crystalline phases are the logic states. A detailed knowledge of the stacking plane sequence, of the local composition and of the vacancy distribution is therefore crucial in order to understand the underlying mechanism of phase transformations in the crystalline state and to evaluate the retention properties.

Metal-Insulator Transition Driven by Vacancy Ordering in GeSbTe Phase Change Materials.

Phase Change Materials (PCMs) are unique compounds employed in non-volatile random access memory thanks to the rapid and reversible transformation between the amorphous and crystalline state that display large differences in electrical and optical properties. In addition to the amorphous-to-crystalline transition, experimental results on polycrystalline GeSbTe alloys (GST) films evidenced a Metal-Insulator Transition (MIT) attributed to disorder in the crystalline phase. Here we report on a fundamental advance in the fabrication of GST with out-of-plane stacking of ordered vacancy layers by means of three distinct methods: Molecular Beam Epitaxy, thermal annealing and application of femtosecond laser pulses.

Revisiting the Local Structure in Ge-Sb-Te based Chalcogenide Superlattices.

The technological success of phase-change materials in the field of data storage and functional systems stems from their distinctive electronic and structural peculiarities on the nanoscale. Recently, superlattice structures have been demonstrated to dramatically improve the optical and electrical performances of these chalcogenide based phase-change materials. In this perspective, unravelling the atomistic structure that originates the improvements in switching time and switching energy is paramount in order to design nanoscale structures with even enhanced functional properties.

Sub-nanometre resolution of atomic motion during electronic excitation in phase-change materials.

Phase-change materials based on Ge-Sb-Te alloys are widely used in industrial applications such as nonvolatile memories, but reaction pathways for crystalline-to-amorphous phase-change on picosecond timescales remain unknown. Femtosecond laser excitation and an ultrashort x-ray probe is used to show the temporal separation of electronic and thermal effects in a long-lived (>100 ps) transient metastable state of Ge2Sb2Te5 with muted interatomic interaction induced by a weakening of resonant bonding. Due to a specific electronic state, the lattice undergoes a reversible nondestructive modification over a nanoscale region, remaining cold for 4 ps.