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Dominique Drouin

Researcher at Université de Sherbrooke

Publications -  213
Citations -  4353

Dominique Drouin is an academic researcher from Université de Sherbrooke. The author has contributed to research in topics: Silicon & Cathodoluminescence. The author has an hindex of 22, co-authored 204 publications receiving 3798 citations. Previous affiliations of Dominique Drouin include Institut national des sciences Appliquées de Lyon & STMicroelectronics.

Papers
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Journal ArticleDOI

Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning

TL;DR: This short review introduces some key considerations for circuit design and the most common non-idealities, and illustrates the possible benefits of stochasticity and compression with examples of well-established software methods.
Posted Content

Low cost Ge/Si virtual substrate through dislocation trapping by nanovoids

TL;DR: In this article, a low-cost method to reduce the threading disloca-tions density (TDD) in relaxed germanium epilayers grown on silicon (Si) substrates is presented.
Patent

Fabrication process for high resolution lithography masks using evaporated or plasma assisted electron sensitive resists with plating image reversal

TL;DR: In this paper, a method for fabricating a high resolution lithography mask, comprising providing a blank mask, coating the blank mask with a conductive layer, depositing a negative electron sensitive resist layer on the conductive surface, applying an electron beam irradiation to the negative electron-sensitive resist layer to form patterns of non-soluble resist, dissolving the negative ERS layer to leave on theconductive layer only the patterns of nonsluble resist and conducting a directional etch through the patterns not protected by the plated etch-resistant material to transfer these patterns into
Proceedings ArticleDOI

Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

TL;DR: In this paper, the spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model, which yields better performance than state-of-the-art convolutional neural networks.