Related papers: Multiscale simulations guided advances for all-opt…
Superlattice-like phase change memory (SL-PCM) promises lower switching current than conventional PCM based on Ge2Sb2Te5 (GST). However, a fundamental understanding of SL-PCM requires detailed characterization of the interfaces within such…
This paper presents a unified optimization framework for phase change material (PCM) based cooling systems. Thermal management is critical in applications such as photovoltaic (PV) modules, battery packs, and power electronics, where…
Organic molecular crystals are appealing for next-generation optoelectronic applications, most notably due to their multiexciton generation process that can increase the efficiency of photovoltaic devices. However, a general understanding…
We report on the investigation of an approach for modelling light transmission through systems consisting of several jointed optical fibres, in which the analytical modelling of the waveguides was replaced by Finite Element Modelling (FEM)…
The ever-increasing demand for processing data with larger machine learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower…
It is widely agreed that phase change materials (PCMs) are of high interest for sustainable energy future. Many of the applications require anti-leakage properties of PCM, that can be accomplished through PCM encapsulation. In this study,…
All-optical and fully reconfigurable diffractive optical neural network (DONN) architectures are promising for high-throughput and energy-efficient machine learning (ML) hardware accelerators for broad applications. However, current device…
The on-chip photonic switch is a critical building block for photonic integrated circuits (PICs) and the integration of phase change materials (PCMs) enables non-volatile switch designs that are compact, low-loss, and energy-efficient.…
Two dimensional transition metal dichalcogenides exhibit strong excitonic responses, direct bandgaps, and remarkable nonlinear optical properties, making them highly attractive for integrated photonic, optoelectronic, and quantum…
The accurate, reliable and efficient numerical approximation of multi-physics processes in heterogeneous porous media with varying media coefficients that include fluid flow and structure interactions is of fundamental importance in energy,…
Guided-wave plasmonic circuits are promising platforms for sensing, interconnection, and quantum applications in the sub-diffraction regime. Nonetheless, the loss-confinement trade-off remains a collective bottleneck for plasmonic-enhanced…
Next-nearest-neighbor Heisenberg chain plays important roles in solid state physics, such as predicting exotic electric properties of two-dimensional materials or magnetic properties of organic compounds. Direct experimental studies of the…
From the background of microwave-optomechanical experiments involving carbon nanotubes, the optimization of superconducting coplanar waveguide resonator devices is discussed. Two devices, one with unmodified geometry compared to previous…
Recently, the joint design of optical systems and downstream algorithms is showing significant potential. However, existing rays-described methods are limited to optimizing geometric degradation, making it difficult to fully represent the…
We report an electrically actuated, low-loss non-volatile optical switch based on the phase-change material (PCM) Sb2Se3 integrated on a silicon nitride (Si3N4) platform. The device is fabricated using an 8-inch wafer-scale process flow,…
Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations…
We propose an optical memory cell based on ultrafast wavelength switching in coupled-cavity microlasers, featuring bistability between modes separated by several nanometers. A numerical implementation is demonstrated by simulating a…
Despite its ability to draw precise inferences from large and complex datasets, the use of data analytics in the field of condensed matter and materials sciences -- where vast quantities of complex metrology data are regularly generated --…
Neural processing on devices and circuits is fast becoming a popular approach to emulate biological neural networks. Elaborate CMOS and memristive technologies have been employed to achieve this, including chalcogenide-based in-memory…
Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light can determine the ultimate performance of scientific…