Time-Resolved Temperature Mapping Leveraging the Strong Thermo-Optic Effect in Phase-Change Materials.

Optical phase-change materials are highly promising for emerging applications such as tunable metasurfaces, reconfigurable photonic circuits, and non-von Neumann computing. However, these materials typically require both high melting temperatures and fast quenching rates to reversibly switch between their crystalline and amorphous phases: a significant challenge for large-scale integration. In this work, we use temperature-dependent ellipsometry to study the thermo-optic effect in GST and use these results to demonstrate an experimental technique that leverages the thermo-optic effect in GST to enable both spatial and temporal thermal measurements of two common electro-thermal microheater designs currently used by the phase-change community. Our approach shows excellent agreement between experimental results and numerical simulations and provides a noninvasive method for rapid characterization of electrically programmable phase-change devices.

Designing fast and efficient electrically driven phase change photonics using foundry compatible waveguide-integrated microheaters.

Phase change chalcogenides such as Ge2Sb2Te5 (GST) have recently enabled advanced optical devices for applications such as in-memory computing, reflective displays, tunable metasurfaces, and reconfigurable photonics. However, designing phase change optical devices with reliable and efficient electrical control is challenging due to the requirements of both high amorphization temperatures and extremely fast quenching rates for reversible switching. Here, we use a Multiphysics simulation framework to model three waveguide-integrated microheaters designed to switch optical phase change materials. We explore the effects of geometry, doping, and electrical pulse parameters to optimize the switching speed and minimize energy consumption in these optical devices.

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.