Extracting functional modules from flattened gate-level netlist

2012 ◽  
Author(s):  
Yiqiong Shi ◽  
Bah-Hwee Gwee ◽  
Ye Ren ◽  
Thet Khaing Phone ◽  
Chan Wai Ting
Keyword(s):  
Functional Modules

Metal Bridge Defects Localization by a Tiny Leakage between One of Bridged Signals and VDD (GND)

2014 ◽  
Author(s):  
Chunlei Wu ◽  
Suying Yao
Keyword(s):  
Circuit Model ◽  
Functional Modules ◽  
Semiconductor Technology ◽  
Simple Circuit ◽  
Lock In

Abstract As semiconductor technology continues to advance to smaller dimensions and more complex circuit designs, it is becoming more challenging to locate the resistive short directly between two metal lines (signals) due to a metal bridge defect. Especially these two metal lines are very long and relevant to many functional modules. After studying the failed circuit model, we found there should be a tiny leakage between one of the bridged signals and one of common power signals (such as VDD and GND) on a failed IC compared with the reference one, if there is a metal bridge defect between these two bridged signals. The tiny leakage between one of the bridged signals and one of power signals is an indirect leakage that is a mapping of the direct resistive short between these two bridged signals. The metal bridge defect could be pinpointed with the tiny leakage between one of the bridged signals and one of power signals by Lock-in IR-OBIRCH. It is an easier and faster way to locate the metal bridge defects. In this paper, the basic and simple circuit model with a metal bridge defect will be presented and two cases will be studied to demonstrate how to localize a metal bridge defect by the tiny leakage between one of the bridged signals and one of power signals.

Identification of biomarkers and functional modules from genomic data in stage-wise breast cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Athira K ◽  
Vrinda C ◽  
Sunil Kumar P V ◽  
Gopakumar G
Keyword(s):  
Breast Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Predictive Biomarkers ◽  
Stage I ◽  
Stage Ii ◽  
Stage Iii ◽  
Functional Modules ◽  
Incidence And Mortality ◽  
Multiple Stages

Background: Breast cancer is the most common cancer in women across the world, with high incidence and mortality rates. Being a heterogeneous disease, gene expression profiling based analysis plays a significant role in understanding breast cancer. Since expression patterns of patients belonging to the same stage of breast cancer vary considerably, an integrated stage-wise analysis involving multiple samples is expected to give more comprehensive results and understanding of breast cancer. Objective: The objective of this study is to detect functionally significant modules from gene co-expression network of cancerous tissues and to extract prognostic genes related to multiple stages of breast cancer. Methods: To achieve this, a multiplex framework is modelled to map the multiple stages of breast cancer, which is followed by a modularity optimization method to identify functional modules from it. These functional modules are found to enrich many Gene Ontology terms significantly that are associated with cancer. Result and Discussion: predictive biomarkers are identified based on differential expression analysis of multiple stages of breast cancer. Conclusion: Our analysis identified 13 stage-I specific genes, 12 stage-II specific genes, and 42 stage-III specific genes that are significantly regulated and could be promising targets of breast cancer therapy. That apart, we could identify 29, 18 and 26 lncRNAs specific to stage I, stage II and stage III respectively.

scholarly journals Characterization of the Survival Influential Genes in Carcinogenesis

2021 ◽  
Vol 22 (9) ◽  
pp. 4384
Author(s):  
Divya Sahu ◽  
Yu-Lin Chang ◽  
Yin-Chen Lin ◽  
Chen-Ching Lin
Keyword(s):  
Cell Cycle ◽  
Cox Regression ◽  
Monte Carlo Algorithm ◽  
Functional Modules ◽  
Protective Genes ◽  
Cancer Types ◽  
Underlying Mechanisms ◽  
Patient Prognosis ◽  
Pan Cancer

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

scholarly journals DNA-based constitutional dynamic networks as functional modules for logic gates and computing circuit operations

2021 ◽  
Vol 12 (15) ◽  
pp. 5473-5483
Author(s):  
Zhixin Zhou ◽  
Jianbang Wang ◽  
R. D. Levine ◽  
Francoise Remacle ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Logic Gates ◽  
Functional Modules ◽  
Logic Operations

A nucleic acid-based constitutional dynamic network (CDN) provides a single functional computational module for diverse input-guided logic operations and computing circuits.

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

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