The Photoinduced Response of Antimony from Femtoseconds to Minutes.

As a phase change material (PCM), antimony exhibits a set of desirable properties that make it an interesting candidate for photonic memory applications. These include a large optical contrast between crystalline and amorphous solid states over a wide wavelength range. Switching between the states is possible on nanosecond timescales by applying short heating pulses.

Probabilistic photonic computing with chaotic light.

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process.

Event-driven adaptive optical neural network.

We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory.

Antimony as a Programmable Element in Integrated Nanophotonics.

The use of nonlinear elements with memory as photonic computing components has seen a huge surge in interest in recent years with the rise of artificial intelligence and machine learning. A key component is the nonlinear element itself. A class of materials known as phase change materials has been extensively used to demonstrate the viability of such computing.

Roadmap on emerging hardware and technology for machine learning.

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing.

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.

Localised states and their capture characteristics in amorphous phase-change materials.

As phase-change materials are poised to play a key role in next-generation computing systems, improving the current understanding of electrical transport in their amorphous phase can further strengthen their technological competitiveness. Even though the interaction of charge carriers with disorder-induced localised states largely affect the field-dependent conductivity, a clear link between electrical transport and specific features of the electronic density of states (DOS) could not be established yet due to a lack of knowledge of the capture characteristics of trap states. Here, we address this knowledge gap and employ modulated photocurrent spectroscopy (MPC) to investigate localised states in the frequently studied amorphous phase of GeSbTe.

Exploiting nanoscale effects in phase change memories.

The market launch of Intel's 3D XPoint™ proves phase change technology has grown mature. Besides storing information in a fast and non-volatile way, phase change memories (PCMs) may facilitate neuromorphic and in-memory computing. In order to establish PCM as a lasting element of the electronics ecosystem, scalability to future technology nodes needs to be assured.

Monatomic phase change memory.

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.

Picosecond Electric-Field-Induced Threshold Switching in Phase-Change Materials.

Many chalcogenide glasses undergo a breakdown in electronic resistance above a critical field strength. Known as threshold switching, this mechanism enables field-induced crystallization in emerging phase-change memory. Purely electronic as well as crystal nucleation assisted models have been employed to explain the electronic breakdown.

Impact of defect occupation on conduction in amorphous Ge2Sb2Te5.

Storage concepts employing the resistance of phase-change memory (PRAM) have matured in recent years. Attempts to model the conduction in the amorphous state of phase-change materials dominating the resistance of PRAM devices commonly invoke a connection to the electronic density-of-states (DoS) of the active material in form of a "distance between trap states s". Here, we point out that s depends on the occupation of defects and hence on temperature.

Relation between bandgap and resistance drift in amorphous phase change materials.

Memory based on phase change materials is currently the most promising candidate for bridging the gap in access time between memory and storage in traditional memory hierarchy. However, multilevel storage is still hindered by the so-called resistance drift commonly related to structural relaxation of the amorphous phase. Here, we present the temporal evolution of infrared spectra measured on amorphous thin films of the three phase change materials Ag4In3Sb67Te26, GeTe and the most popular Ge2Sb2Te5.

How fragility makes phase-change data storage robust: insights from ab initio simulations.

Phase-change materials are technologically important due to their manifold applications in data storage. Here we report on ab initio molecular dynamics simulations of crystallization of the phase change material Ag4In3Sb67Te26 (AIST). We show that, at high temperature, the observed crystal growth mechanisms and crystallization speed are in good agreement with experimental data.

Measurement of crystal growth velocity in a melt-quenched phase-change material.

Phase-change materials are the basis for next-generation memory devices and reconfigurable electronics, but fundamental understanding of the unconventional kinetics of their phase transitions has been hindered by challenges in the experimental quantification. Here we obtain deeper understanding based on the temperature dependence of the crystal growth velocity of the phase-change material AgInSbTe, as derived from laser-based time-resolved reflectivity measurements. We observe a strict Arrhenius behaviour for the growth velocity over eight orders of magnitude (from ~10 nm s(-1) to ~1 m s(-1)).

Using low-loss phase-change materials for mid-infrared antenna resonance tuning.

We show tuning of the resonance frequency of aluminum nanoantennas via variation of the refractive index n of a layer of phase-change material. Three configurations have been considered, namely, with the antennas on top of, inside, and below the layer. Phase-change materials offer a huge index change upon the structural transition from the amorphous to the crystalline state, both stable at room temperature.

Design rules for phase-change materials in data storage applications.

Phase-change materials can rapidly and reversibly be switched between an amorphous and a crystalline phase. Since both phases are characterized by very different optical and electrical properties, these materials can be employed for rewritable optical and electrical data storage. Hence, there are considerable efforts to identify suitable materials, and to optimize them with respect to specific applications.

A map for phase-change materials.

Phase-change materials are characterized by a unique property portfolio well suited for data storage applications. Here, a first treasure map for phase-change materials is presented on the basis of a fundamental understanding of the bonding characteristics. This map is spanned by two coordinates that can be calculated just from the composition, and represent the degree of ionicity and the tendency towards hybridization ('covalency') of the bonding.