Integrated Circuits and Semiconductor Failure Analysis

Últimos artículos

• MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniques

  Vanessa Wirth, Johanna Br\"aunig, Nikolai Hofmann, Martin Vossiek, Tim Weyrich, Marc Stamminger · 22 de septiembre de 2026

  Utilizing the complementary strengths of wavelength-specific range or depth sensors is crucial for robust computer-assisted tasks such as autonomous driving. Despite this, there is still little research done at the intersection of optical depth sensors and radars operating close range, where the tar…

• An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks

  Hallgrimur Thorsteinsson, Valdemar J Henriksen, Daniel I R Cruz, Raghavendra Selvan, Tong Chen · 29 de mayo de 2026

  As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks by introducing small, targeted perturba…

• Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency

  Hallgrimur Thorsteinsson, Valdemar J Henriksen, Daniel I R Cruz, Raghavendra Selvan, Tong Chen · 28 de mayo de 2026

  As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks by introducing small, targeted perturba…

• Two failure modes of deep transformers and how to avoid them: a unified theory of signal propagation at initialisation

  Alessio Giorlandino, Sebastian Goldt · 10 de febrero de 2026

  Finding the right initialisation for neural networks is crucial to ensure smooth training and good performance. In transformers, the wrong initialisation can lead to one of two failure modes of self-attention layers: rank collapse, where all tokens collapse into similar representations, and entropy …

• On Fine-Grained I/O Complexity of Attention Backward Passes

  Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song, Song Yue, Jiahao Zhang · 26 de enero de 2026

  Large Language Models (LLMs) exhibit exceptional proficiency in handling extensive context windows in natural language. Nevertheless, the quadratic scaling of attention computation relative to sequence length creates substantial efficiency bottlenecks, necessitating the development of I/O-optimized …