3D-IC design solution for modular integration of chiplet over silicon interposer

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Unsupervised Learning with Ferroelectric Synapses

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Observation of Highly Nonlinear Resistive Switching of Al2O3/TiO2-based Memristors at Cryogenic Temperature (1.5 K)

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Thermomechanical finite element modeling of Cu–SiO 2 direct hybrid bonding with a dishing effect on Cu surfaces

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

International audienceSurface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyper-parameters inspired by the readout layer concept in reservoir computing. Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-The-Art spiking neural networks on two open-source datasets for hand gesture recognition. The spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model. When trained with the unsupervised activity regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir yields better performance than state-of-The-Art convolutional neural networks. The reservoir performance with regulated activity was found to be 89.72% for the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset. Therefore, the biologically-inspired computing paradigm, which is known for being power efficient, also proves to have a great potential when compared with conventional AI algorithms. © 2021 Owner/Author

Development of a plasmaetching process of copper for the microfabrication of high-density interconnects in advanced packaging

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Fabrication of Planar Back End of Line Compatible HfOx Complementary Resistive Switches

International audienceThis paper presents the fabrication, together with morphological and electrical characterizations of complementary resistive switches using the nanodamascene process. The as-fabricated devices are fully embedded in an insulating oxide, opening the way for further process steps such as three-dimensional monolithic integration. Complementary resistive switches electrical performance is consistent with resistive random access memories fabricated and characterized with the same procedure that showed ROFF/RON ratios of 100. Complementary operating voltages of Vth1,3 = |0.8| V and Vth2.4 = |1.1| V are obtained for 88 × 22 nm2 junction with a 6 nm thick HfOx junction

Next generation image sensor via direct hybrid bonding

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