17 citations · 68 across the 19 of their papers we have counts for
19 papers
Microstructural and preliminary optical and microwave characterization of erbium doped CaMoO thin films
Ignas Masiulionis, Bonnie Y. X. Lin, Sagar Kumar Seth +10
This work explores erbium-doped calcium molybdate (CaMoO) thin films grown on silicon and yttria stabilized zirconia (YSZ) substrates, as a potential solid state system for C-b…
Surrogate models to optimize plasma assisted atomic layer deposition in high aspect ratio features
Angel Yanguas-Gil, Jeffrey W. Elam
In this work we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atom…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…
Benchmarking large language models for materials synthesis: the case of atomic layer deposition
Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam +5
In this work we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and in particular in the f…
Modeling scale-up of particle coating by atomic layer deposition
Angel Yanguas-Gil, Jeffrey W. Elam
Atomic layer deposition (ALD) is a promising technique to functionalize particle surfaces for energy applications including energy storage, catalysis, and decarbonization. In this…
Design Principles for Lifelong Learning AI Accelerators
Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah +9
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The…
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems. Many approaches to continual learning r…
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures
Angel Yanguas-Gil, Sandeep Madireddy
In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
General policy mapping: online continual reinforcement learning inspired on the insect brain
Angel Yanguas-Gil, Sandeep Madireddy
We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extractio…
Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
Angel Yanguas-Gil, Jeffrey W. Elam
In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of a…
Reactor scale simulations of ALD and ALE: ideal and non-ideal self-limited processes in a cylindrical and a 300 mm wafer cross-flow reactor
Angel Yanguas-Gil, Joseph A. Libera, Jeffrey W. Elam
We have developed a simulation tool to model self-limited processes such as atomic layer deposition and atomic layer etching inside reactors of arbitrary geometry. In this work, we…
Fast, Smart Neuromorphic Sensors Based on Heterogeneous Networks and Mixed Encodings
Angel Yanguas-Gil
Neuromorphic architectures are ideally suited for the implementation of smart sensors able to react, learn, and respond to a changing environment. Our work uses the insect brain as…
Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardware
Angel Yanguas-Gil
In this work we explore recurrent representations of leaky integrate and fire neurons operating at a timescale equal to their absolute refractory period. Our coarse time scale appr…
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical for designing intelligent systems. Many existing approaches to continual…
Optical and structural properties of Si doped -GaO (010) thin films homoepitaxially grown by halide vapor phase epitaxy
Bahadir Kucukgok, David J. Mandia, Jacob H. Leach +6
We report the optical, electrical, and structural properties of Si doped -GaO films grown on (010)-oriented -GaO substrate via HVPE. Our results show that, de…
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn and adapt in real time is a central feature of biological systems. Neuromorphic architectures demonstrating such versatility can greatly enhance our ability to…
Confinement Effect Emission from Infiltrated ZnO in PS-b-PMMA Nanostructures
Paris L. Blaisdell-Pijuan, David J. Gosztola, Angel Yanguas-Gil +5
We have characterized the growth of ZnO using sequential infiltration synthesis (SiS) on PS-b-PMMA block copolymers (BCP) of spherical and cylindrical sub-20nm morphologies and stu…
An instrument for advanced in situ x-ray studies of metal-organic vapor phase epitaxy of III-nitrides
Guangxu Ju, Matthew J. Highland, Angel Yanguas-Gil +6
We describe an instrument that exploits the ongoing revolution in synchrotron sources, optics, and detectors to enable in situ studies of metal-organic vapor phase epitaxy (MOVPE)…