Spintronic device modeling and evaluation using modular approach to spintronics

Spintronics technology finds itself in an exciting stage today. Riding on the backs of rapid growth and impressive advances in materials and phenomena, it has started to make headway in the memory industry as solid state magnetic memories (STT-MRAM) and is considered a possible candidate to replace the CMOS when its scaling reaches physical limits. It is necessary to bring all these advances together in a coherent fashion to explore and evaluate the potential of spintronic devices. This work creates a framework for this exploration and evaluation based on Modular Approach to Spintronics, which encapsulate the physics of transport of charge and spin through materials and the phenomenology of magnetic dynamics and interaction in benchmarked elemental modules. These modules can then be combined together to form spin-circuit models of complex spintronic devices and structures which can be simulated using SPICE like circuit simulators. In this work we demonstrate how Modular Approach to Spintronics can be used to build spin-circuit models of functional spintronic devices of all types: memory, logic, and oscillators. We then show how Modular Approach to Spintronics can help identify critical factors behind static and dynamic dissipation in spintronic devices and provide remedies by exploring the use of various alternative materials and phenomena. Lastly, we show the use of Modular Approach to Spintronics in exploring new paradigms of computing enabled by the inherent physics of spintronic devices. We hope that this work will encourage more research and experiments that will establish spintronics as a viable technology for continued advancement of electronics

A Deep Dive into the Computational Fidelity of High Variability Low Energy Barrier Magnet Technology for Accelerating Optimization and Bayesian Problems

Low energy barrier magnet (LBM) technology has recently been proposed as a candidate for accelerating algorithms based on energy minimization and probabilistic graphs because their physical characteristics have a one-to-one mapping onto the primitives of these algorithms. Many of these algorithms have a much higher tolerance for error compared to high-accuracy numerical computation. LBM, however, is a nascent technology, and devices show high sample-to-sample variability. In this work, we take a deep dive into the overall fidelity afforded by this technology in providing computational primitives for these algorithms. We show that while the compute results show finite deviations from zero variability devices, the margin of error is almost always certifiable to a certain percentage. This suggests that LBM technology could be a viable candidate as an accelerator for popular emerging paradigms of computing.Comment: 5 pages, 8 figure

An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis

Privacy and annotation bottlenecks are two major issues that profoundly affect the practicality of machine learning-based medical image analysis. Although significant progress has been made in these areas, these issues are not yet fully resolved. In this paper, we seek to tackle these concerns head-on and systematically explore the applicability of non-contrastive self-supervised learning (SSL) algorithms under federated learning (FL) simulations for medical image analysis. We conduct thorough experimentation of recently proposed state-of-the-art non-contrastive frameworks under standard FL setups. With the SoTA Contrastive Learning algorithm, SimCLR as our comparative baseline, we benchmark the performances of our 4 chosen non-contrastive algorithms under non-i.i.d. data conditions and with a varying number of clients. We present a holistic evaluation of these techniques on 6 standardized medical imaging datasets. We further analyse different trends inferred from the findings of our research, with the aim to find directions for further research based on ours. To the best of our knowledge, ours is the first to perform such a thorough analysis of federated self-supervised learning for medical imaging. All of our source code will be made public upon acceptance of the paper

Physics of Nanostructure Design for Infrared Detectors

Infrared detectors and focal plane array technologies are becoming ubiquitous in military, but are limited in the commercial sectors. The widespread commercial use of this technology is lacking because of the high cost and large size, weight and power. Most of these detectors require cryogenic cooling to minimize thermally generated dark currents, causing the size, weight, power and cost to increase significantly. Approaches using very thin detector design can minimize thermally generated dark current, but at a cost of lower absorption efficiency. There are emerging technologies in nanostructured material designs such as metasurfaces that can allow for increased photon absorption in a thin detector architecture. Ultra-thin and low-dimensional absorber materials may also provide unique engineering opportunities in detector design. This chapter discusses the physics and opportunities to increase the operating temperature using such techniques