Conference proceeding
Online Learning for Dynamic Structural Characterization in Electron Energy Loss Spectroscopy
Proceedings - Design, Automation, and Test in Europe Conference and Exhibition, pp 1-7
31 Mar 2025
Abstract
In-situ Electron Energy Loss Spectroscopy (EELS) is a crucial technique for determining the elemental composition of materials through EELS Spectrum Images (EELS-SI). While recent innovations have made it possible for EELS-SI data acquisition at rates of 400 frames per second with near-zero read noise, the challenge lies in processing this massive stream of real-time data to capture nanoscale dynamic changes. This task demands advanced machine learning methods capable of identifying subtle and complex features in EELS spectra. Furthermore, the EELS data acquired in difficult experimental conditions often suffer from a low signal-to-noise ratio (SNR), leading to unreliable classification and limiting their utility. In response to this critical need, we introduce a spiking neural network (SNN)-based Variational Autoencoder (VAE) that embeds spectral data into a latent space, facilitating precise prediction of structural changes. VAEs are designed to learn efficient low-dimensional representations while capturing the inherent variability in the data, making them highly effective for processing multidimensional data. Additionally, SNNs, which use biological neurons, offer unmatched scalability and energy efficiency by processing information through binary spikes, making them ideal for high-throughput data. We validate our framework using MXene annealing data, achieving denoised spectrum images with an SNR of 28.3dB. For the first time, we present a fully online learning solution for dynamic structural tracking, implemented directly in hardware, eliminating the traditional bottleneck of offline training. Our method achieves reliable, real-time, on-device characterization of high-speed EELS data when evaluated on an FPGA platform. Joint experiments with the SNN-VAE model on both spiking autoencoder hardware and a softwaretrained hybrid configuration of hardware spiking encoders demonstrated latency reductions of 25.2x, 93.7x, and 1.04x, 4.5x in energy savings, respectively, compared to baseline.
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Details
- Title
- Online Learning for Dynamic Structural Characterization in Electron Energy Loss Spectroscopy
- Creators
- M.L. Varshika - Drexel UniversityJonathan Hollenbach - Johns Hopkins UniversityNicolas Agostini - Pacific Northwest National LaboratoryAnkur Limaye - Pacific Northwest National LaboratoryAntonino Tumeo - Pacific Northwest National LaboratoryAnup Das - Drexel University
- Publication Details
- Proceedings - Design, Automation, and Test in Europe Conference and Exhibition, pp 1-7
- Publisher
- IEEE
- Grant note
- Laboratory Directed Research and Development (10.13039/100007000) CCF-1942697 / NSF (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Scopus ID
- 2-s2.0-105006887772
- Other Identifier
- 991022054333504721