Advancements in Semiconductor Devices and Circuit Design

Últimos artículos

• Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration

  Leonid Popryho, Ayoub Sadeghi, Inna Partin-Vaisband · 4 de septiembre de 2026

  High-fidelity TCAD simulation of drift-diffusion transport remains the workhorse of emerging FinFET device design, but it is computationally expensive, especially for 3D structures where runtime escalates steeply with mesh complexity. This sharply limits multi-objective design space exploration. Exi…

• Neural Guided Sampling for Quantum Circuit Optimization

  Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche · 24 de julio de 2026

  Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit. Due to decoherence, the quality of the computational outcome can degrade seriously with…

• Rapid FinFET Modelling Using an Autoencoder

  Amit Sarkar Suman Sau, Swagata Mandal · 24 de junio de 2026

  This work presents a machine learning framework that leverages an autoencoder (AE) for the efficient modeling of FinFET. We first calibrated a BSIM-CMG model to generate a dataset of current-voltage (ID-VG) characteristics. This data was used to train an autoencoder that compresses full I-V curves i…

• Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra

  Ivan Bioli, Carlo Marcati, Giancarlo Sangalli · 28 de mayo de 2026

  Natural Gradient Descent (NGD) has emerged as a promising optimization algorithm for training neural network-based solvers for partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs). However, its practical use is often limited by the high computational cost of solvin…

• QDFlow: A Python package for physics simulations of quantum dot devices

  Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler, Jacob M. Taylor, Justyna P. Zwolak · 5 de marzo de 2026

  Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, representative datasets designed to capture the full spectrum of data quality encountered in practice, with both high- and lo…

• AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization

  Guangxi Fan, Tianliang Ma, Xuguang Sun, Xun Wang, Kain Lu Low, Leilai Shao · 1 de enero de 2026

  With the continued scaling of advanced technology nodes, the design-technology co-optimization (DTCO) paradigm has become increasingly critical, rendering efficient device design and optimization essential. In the domain of TCAD simulation, however, the scarcity of open-source resources hinders lang…

• DiffBreak: Is Diffusion-Based Purification Robust?

  Andre Kassis, Urs Hengartner, Yaoliang Yu · 25 de noviembre de 2025

  Diffusion-based purification (DBP) has become a cornerstone defense against adversarial examples (AEs), regarded as robust due to its use of diffusion models (DMs) that project AEs onto the natural data manifold. We refute this core claim, theoretically proving that gradient-based attacks effectivel…

• RTNinja: A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices

  Anirudh Varanasi, Robin Degraeve, Philippe Roussel, Clement Merckling · 20 de noviembre de 2025

  Random telegraph noise is a prevalent variability phenomenon in nanoelectronic devices, arising from stochastic carrier exchange at defect sites and critically impacting device reliability and performance. Conventional analysis techniques often rely on restrictive assumptions or manual interventions…

• Surrogate Quantum Circuit Design for the Lattice Boltzmann Collision Operator

  Monica L\u{a}c\u{a}tu\c{s}, Matthias M\"oller · 14 de noviembre de 2025

  This study introduces a framework for learning a low-depth surrogate quantum circuit (SQC) that approximates the nonlinear, dissipative, and hence non-unitary Bhatnagar-Gross-Krook (BGK) collision operator in the lattice Boltzmann method (LBM) for the D2Q9 lattice. By appropriately selecting the qua…

• RTNinja: a generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices

  Anirudh Varanasi, Robin Degraeve, Philippe Roussel, Clement Merckling · 3 de noviembre de 2025

  Random telegraph noise is a prevalent variability phenomenon in nanoelectronic devices, arising from stochastic carrier exchange at defect sites and critically impacting device reliability and performance. Conventional analysis techniques often rely on restrictive assumptions or manual interventions…