Engineering topics

Ghosh, Avik W.

Publications and source records attributed to Ghosh, Avik W..

3 records

DOE OSTI2024

Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models

Abstract We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free $$\hbox {Mn}_4$$ Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. Graphical abstract

Biswas, Ankita↗

DOE OSTI2024

Roadmap for unconventional computing with nanotechnology

Abstract In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

Finocchio, Giovanni (ORCID:0000000210433876)↗

NASA NTRS2020

Development of High-Performance Graphene-HgCdTe Detector Technology for Mid-Wave Infrared Applications

A high-performance graphene-based HgCdTe detector technology is being developed for sensing over the mid-wave infrared (MWIR) band for NASA Earth Science, defense, and commercial applications. This technology involves the integration of graphene into HgCdTe photodetectors that combines the best of both materials and allows for higher MWIR(2-5 m) detection performance compared to photodetectors using only HgCdTe material. The interfacial barrier between the HgCdTe-based absorber and the graphene layer reduces recombination of photogenerated carriers in the detector. The graphene layer also acts as high mobility channel that whisks away carriers before they recombine, further enhancing the detector performance. Likewise, HgCdTe has shown promise for the development of MWIR detectors with improvements in carrier mobility and lifetime. The room temperature operational capability of HgCdTe-based detectors and arrays can help minimize size, weight, power and cost for MWIR sensing applications such as remote sensing and earth observation, e.g., in smaller satellite platforms. The objective of this work is to demonstrate graphene-based HgCdTe room temperature MWIR detectors and arrays through modeling, material development, and device optimization. The primary driver for this technology development is the enablement of a scalable, low cost, low power, and small footprint infrared technology component that offers high performance, while opening doors for new earth observation measurement capabilities.

Sood, Ashok K.↗