Chip Industry Technical Paper Roundup: Mar. 3

GNN acceleration; IGZO near-infrared sensing; LiDAR super-resolution for autonomous driving; cobalt ALD/MLD thin films; ultrafast laser nonlinear effects; atomic-scale GAA metrology; ReRAM-based neuromorphic online learning; GenAI SW-to-HW design; fusion-aware accelerator mapping; laser fault injection in SRAM.

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New technical papers recently added to Semiconductor Engineering’s library:

Technical Paper Research Organizations
AutoGNN: End-to-End Hardware-Driven Graph Preprocessing for Enhanced GNN Performance
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KAIST, Panmnesia, Peking University, Hanyang University, Pennsylvania State University
Sputtering-driven formation of interstitial oxygen for intrinsic NIR detection in IGZO phototransistor
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KICET, Korea University, Yonsei University, Argonne National Laboratory
A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving
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University College London
Amido-Amine Co(II) Precursor-Based Atomic/Molecular Layer Deposition for Cobalt-Organic Thin Films
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Aalto University, Ruhr University Bochum, Tyndall National Institute, ESRF
Extreme optical nonlinearities unveiled by ultrafast laser filamentation in semiconductors
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Abbe Center of Photonics, Laboratoire Hubert Curien
3D atomic-scale metrology of Gate-All-Around transistors via electron ptychography
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Cornell University, ASM, TSMC
NeoHebbian synapses to accelerate online training of neuromorphic hardware
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IIT Madras, University of California Santa Barbara
GenAI design challenges from software to hardware
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Harvard University
Fast and Fusiest: an Optimal Fusion-Aware Mapper for Accelerator Modeling and Evaluation
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MIT
Electrical modelisation of a bitflip in SRAM memory induced by laser fault injection
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University of Rennes, CNRS, IETR

Find more semiconductor research papers here.



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