As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of Bias Temperature Instability (BTI) and Process Variation (PV). Traditional reliability analysis methods, which rely on computationally intensive simulations or extensive lookup tables, fail to scale efficiently for large designs, creating a critical bottleneck in design space exploration.
arXiv:2308. 07867v4 Announce Type: replace-cross Abstract: The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy.
By Parikshit Pareek, Sidhant Misra, Deepjyoti Deka
arXiv:2608. 09622v1 Announce Type: new Abstract: Reliability qualification of advanced semiconductor devices requires sequential stress decisions that balance characterization objectives against multiple competing failure mechanisms.
By Youssef A. Elhagrasy, Ian Hill, Andr\'e Ivanov
arXiv:2606. 04266v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are used in a variety of real-world applications including, for example, image classification and speech recognition.
By Alireza Sarmadi, Virinchi Roy Surabhi, Prashanth Krishnamurthy, Hussam Amrouch, Ramesh Karri, Farshad Khorrami
arXiv:2609.07046v1 Announce Type: new
Abstract: AI has revolutionized various engineering domains, but its impact on semiconductor device design and defect discovery is still limited, due to limited...
By Hiu Yung Wong
arXiv:2504. 03711v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI)-driven electronic design automation (EDA) techniques have been extensively explored for VLSI circuit design applications.
By Wenji Fang, Jing Wang, Yao Lu, Shang Liu, Yuchao Wu, Yuzhe Ma, Zhiyao Xie
arXiv:2606. 01265v1 Announce Type: cross Abstract: This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical power delivery systems.
By Ayoub Sadeghi, Leonid Popryho, Inna Partin-Vaisband
arXiv:2608. 08048v1 Announce Type: cross Abstract: This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for power system dynamics.
By Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis
arXiv:2507.05134v2 Announce Type: replace
Abstract: We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimens...
By Robert K. A. Bennett, Jan-Lucas Uslu, Harmon F. Gault, Asir Intisar Khan, Lauren Hoang, Tara Pe\~na, Kathryn Neilson, Young Suh Song, Zhepeng Zhang, Andrew J. Mannix, Eric Pop
arXiv:2607. 16183v1 Announce Type: new Abstract: To address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack that leverages stochastic analog processes in physical hardware.
By Owen Lockwood, J\'er\'emy B\'ejanin, Joost Bus, Christopher Chamberland, Patrick Huembeli, Frank Sch\"afer, Guillaume Verdon
arXiv:2510. 23472v2 Announce Type: replace-cross Abstract: Chip placement is a vital stage in modern chip design, and black-box optimization (BBO) has been applied to it for decades.
By Ke Xue, Ruo-Tong Chen, Rong-Xi Tan, Xi Lin, Yunqi Shi, Siyuan Xu, Mingxuan Yuan, Chao Qian
The paper investigates how temperature affects analog deep neural network (DNN) inference, focusing on both stochastic and systematic non‑idealities in analog hardware. Experiments show that temperature‑induced performance loss is mainly driven by systematic errors rather than random noise. The study evaluates various mitigation techniques, finding that noise‑aware training and temperature‑aware calibration—especially hardware‑in‑the‑loop training—best preserve inference accuracy across different thermal conditions.
By Niklas Summ, Xiao Wang, Hendrik Borras, Bernhard Klein, Holger Fr\"oning