Recent Progress in Physics-Based Modeling of Electromigration in Integrated Circuit Interconnects.

Micromachines (Basel)

Zhejiang Provincial Key Lab of Large-Scale Integrated Circuit Design, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.

Published: May 2022

The advance of semiconductor technology not only enables integrated circuits with higher density and better performance but also increases their vulnerability to various aging mechanisms which occur from front-end to back-end. Analysis on the impact of aging mechanisms on circuits' reliability is crucial for the design of reliable and sustainable electronic systems at advanced technology nodes. As one of the most crucial back-end aging mechanisms, electromigration deserves research efforts. This paper introduces recent studies on physics-based modeling of electromigration aging of on-chip interconnects. At first, the background of electromigration is introduced. The conventional method and physics-based modeling for electromigration are described. Then studies on how electromigration affects powers grids and signal interconnects are discussed in detail. Some of them focus on the comprehensiveness of modeling methodology, while others aim at the strategies for improving computation accuracy and speed and the strategies for accelerating/decelerating aging. Considering the importance of electromigration for circuit reliability, this paper is dedicated to providing a review on physics-based modeling methodologies on electromigration and their applications for integrated circuits interconnects.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230697PMC
http://dx.doi.org/10.3390/mi13060883DOI Listing

Publication Analysis

Top Keywords

physics-based modeling
16
modeling electromigration
12
aging mechanisms
12
electromigration
8
integrated circuits
8
modeling
5
aging
5
progress physics-based
4
electromigration integrated
4
integrated circuit
4

Similar Publications

Trench MOS Barrier Schottky (TMBS) rectifiers offer superior static and dynamic electrical characteristics when compared with planar Schottky rectifiers for a given active die size. The unique structure of TMBS devices allows for efficient manipulation of the electric field, enabling higher doping concentrations in the drift region and thus achieving a lower forward voltage drop (VF) and reduced leakage current (IR) while maintaining high breakdown voltage (BV). While the use of trenches to push electric fields away from the mesa surface is a widely employed concept for vertical power devices, a significant gap exists in the analytical modeling of this effect, with most prior studies relying heavily on computationally intensive numerical simulations.

View Article and Find Full Text PDF

Automating alloy design and discovery with physics-aware multimodal multiagent AI.

Proc Natl Acad Sci U S A

January 2025

Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA 02139.

The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.

View Article and Find Full Text PDF

Glycoside hydrolases (GHs) are enzymes involved in the degradation of oligosaccharides and polysaccharides. The sequence space of GHs is rapidly expanding due to the increasing number of available sequences. This expansion paves the way for the discovery of novel enzymes with peculiar structural and functional properties.

View Article and Find Full Text PDF

Hydration free energy (HFE) of molecules is a fundamental property having importance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Machine learning (ML) models are increasingly being used to predict HFE.

View Article and Find Full Text PDF

Protein-ligand binding affinity prediction using multi-instance learning with docking structures.

Front Pharmacol

January 2025

Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United States.

Introduction: Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!