HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction

Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles. In this paper, we introduce a novel strategy to fully incorporate topological and geometrical features of circuits by making several key designs in our network architecture. To be more specific, we construct two individual graphs (geometry-graph, topology-graph) with distinct edge construction schemes according to their unique properties. We then propose a dual-branch network with different encoder layers in each pathway and aggregate representations with a sophisticated fusion strategy. Our network, named HybridNet, not only provides a simple yet effective way to capture the geometric interactions of cells, but also preserves the original topological relationships in the netlist. Experimental results on the ISPD2015 benchmarks show that we achieve an improvement of 10.9% compared to previous methods

PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction

IR drop on the power delivery network (PDN) is closely related to PDN's configuration and cell current consumption. As the integrated circuit (IC) design is growing larger, dynamic IR drop simulation becomes computationally unaffordable and machine learning based IR drop prediction has been explored as a promising solution. Although CNN-based methods have been adapted to IR drop prediction task in several works, the shortcomings of overlooking PDN configuration is non-negligible. In this paper, we consider not only how to properly represent cell-PDN relation, but also how to model IR drop following its physical nature in the feature aggregation procedure. Thus, we propose a novel graph structure, PDNGraph, to unify the representations of the PDN structure and the fine-grained cell-PDN relation. We further propose a dual-branch heterogeneous network, PDNNet, incorporating two parallel GNN-CNN branches to favorably capture the above features during the learning process. Several key designs are presented to make the dynamic IR drop prediction highly effective and interpretable. We are the first work to apply graph structure to deep-learning based dynamic IR drop prediction method. Experiments show that PDNNet outperforms the state-of-the-art CNN-based methods by up to 39.3% reduction in prediction error and achieves 545x speedup compared to the commercial tool, which demonstrates the superiority of our method

A Review of Magnetic Nanoparticle-Based Surface-Enhanced Raman Scattering Substrates for Bioanalysis: Morphology, Function and Detection Application

Surface-enhanced Raman scattering (SERS) is a kind of popular non-destructive and water-free interference analytical technology with fast response, excellent sensitivity and specificity to trace biotargets in biological samples. Recently, many researches have focused on the preparation of various magnetic nanoparticle-based SERS substrates for developing efficient bioanalytical methods, which greatly improved the selectivity and accuracy of the proposed SERS bioassays. There has been a rapid increase in the number of reports about magnetic SERS substrates in the past decade, and the number of related papers and citations have exceeded 500 and 2000, respectively. Moreover, most of the papers published since 2009 have been dedicated to analytical applications. In the paper, the recent advances in magnetic nanoparticle-based SERS substrates for bioanalysis were reviewed in detail based on their various morphologies, such as magnetic core–shell nanoparticles, magnetic core–satellite nanoparticles and non-spherical magnetic nanoparticles and their different functions, such as separation and enrichment, recognition and SERS tags. Moreover, the typical application progress on magnetic nanoparticle-based SERS substrates for bioanalysis of amino acids and protein, DNA and RNA sequences, cancer cells and related tumor biomarkers, etc., was summarized and introduced. Finally, the future trends and prospective for SERS bioanalysis by magnetic nanoparticle-based substrates were proposed based on the systematical study of typical and latest references. It is expected that this review would provide useful information and clues for the researchers with interest in SERS bioanalysis

Role of air-sea heat flux on the transformation of Atlantic Water encircling the Nordic Seas

This study reveals that air-sea heat exchange plays differing roles in the transformation of Atlantic Water along the two northward-flowing warm currents in the Nordic Seas, which needs to be considered to understand high-latitude response to climate change