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. However, such materials continue to have relatively slow switching speeds, and issues with cyclability related to phase segregation of phase change alloys. Here, using antimony (Sb) thin films with thicknesses less than 5 nm we demonstrate reversible, ultrafast switching on an integrated photonic platform with retention time of tens of seconds. We use subpicosecond pulses, the shortest used to switch such elements, to program seven distinct memory levels. This portends their use in ultrafast nanophotonic applications ranging from nanophotonic beam steerers to nanoscale integrated elements for photonic 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. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

Monatomic phase change memory.

Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way1-3, with potential for neuromorphic computing applications4-6. 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 compounds6-8. 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 miniaturized9,10. Removing compositional optimization issues may allow one to capitalize on nanosize effects in information storage.

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. Continued miniaturization of PCM devices is not only prescribed in order to achieve memories with higher data density and neuromorphic hardware capable of processing larger amounts of information. Smaller PCM elements are also incentivized by the prospect of increased power efficiency per operation as less material needs to be heated up for switching. For this reason, a good understanding of the effects of confinement on phase change materials is crucial. Here we describe how miniaturization increases the importance of interface effects and we show how in consequence the crystallization kinetics of phase change materials, when confined into nanometer sized structures, can change significantly. Based on this analysis, the implications of such nanoscale effects are discussed and possible ways of exploiting them proposed.

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. Here, picosecond electric pulses are used to excite amorphous Ag_{4}In_{3}Sb_{67}Te_{26}. Field-dependent reversible changes in conductivity and pulse-driven crystallization are observed. The present results show that threshold switching can take place within the electric pulse on subpicosecond time scales-faster than crystals can nucleate. This supports purely electronic models of threshold switching and reveals potential applications as an ultrafast electronic switch.

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. Since the imaginary part of their permittivity is negligibly small in the mid-infrared spectral range, resonance damping is avoided. We present resonance shifting to lower as well as to higher wavenumbers with a maximum shift of 19.3% and a tuning figure of merit, defined as the resonance shift divided by the full-width at half-maximum (FWHM) of the resonance peak, of 1.03.

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. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.

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. 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.

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. A small magnitude of both quantities is an inherent characteristic of phase-change materials. This coordinate scheme enables a prediction of trends for the physical properties on changing stoichiometry.

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 Ge2Sb2Te5. Additionally, we present results on the DOS in the bandgap of amorphous AgIn-doped Sb2Te, which has not been subject to high-resolution DOS spectroscopy before. We find experimental evidence for clearly non-constant capture coefficients among a continuous spectrum of localised states in both studied materials. According to this observation especially in AgIn-doped Sb2Te, where no pronounced defect can be detected as main channel for carrier emission, we point out the necessity of modifying the current Poole-Frenkel-based transport modelling.

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. To verify this, we numerically study how the occupation in the DoS of Ge2Sb2Te5 is affected by changes of temperature and illumination. Employing a charge-transport model based on the Poole-Frenkel effect, we correlate these changes to the field- and temperature-dependent current-voltage characteristics of lateral devices of amorphous Ge2Sb2Te5, measured in darkness and under illumination. In agreement with our calculations, we find a pronounced temperature-dependence of s. As the device-current depends exponentially on the value of s, accounting for its temperature-dependence has profound impact on device modeling.

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. A widening of the bandgap upon annealing accompanied by a decrease of the optical dielectric constant 뵰 is observed for all three materials. Quantitative comparison with experimental data for the apparent activation energy of conduction reveals that the temporal evolution of bandgap and activation energy can be decoupled. The case of Ag4In3Sb67Te26, where the increase of activation energy is significantly smaller than the bandgap widening, demonstrates the possibility to identify new phase change materials with reduced resistance drift.

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. We provide an in-depth understanding of the crystallization mechanisms at the atomic level. At temperatures below 550 K, the computed growth velocities are much higher than those obtained from time-resolved reflectivity measurements, due to large deviations in the diffusion coefficients. As a consequence of the high fragility of AIST, experimental diffusivities display a dramatic increase in activation energies and prefactors at temperatures below 550 K. This property is essential to ensure fast crystallization at high temperature and a stable amorphous state at low temperature. On the other hand, no such change in the temperature dependence of the diffusivity is observed in our simulations, down to 450 K. We also attribute this different behavior to the fragility of the system, in combination with the very fast quenching times employed in the simulations.

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)). This can be attributed to the formation of a glass at elevated temperatures because of rapid quenching of the melt. Further, the temperature dependence of the viscosity is derived, which reveals that the supercooled liquid phase must have an extremely high fragility (>100). Finally, the new experimental evidence leads to an interpretation, which comprehensively explains existing data from various different experiments reported in literature.

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. Design rules that can explain why the materials identified so far enable phase-change based devices would hence be very beneficial. This article describes materials that have been successfully employed and dicusses common features regarding both typical structures and bonding mechanisms. It is shown that typical structural motifs and electronic properties can be found in the crystalline state that are indicative for resonant bonding, from which the employed contrast originates. The occurence of resonance is linked to the composition, thus providing a design rule for phase-change materials. This understanding helps to unravel characteristic properties such as electrical and thermal conductivity which are discussed in the subsequent section. Then, turning to the transition kinetics between the phases, the current understanding and modeling of the processes of amorphization and crystallization are discussed. Finally, present approaches for improved high-capacity optical discs and fast non-volatile electrical memories, that hold the potential to succeed present-day's Flash memory, are presented.