A random approach of test macro generation for early detection of hotspots

Lee, Jong-hyun; Kim, Chin; Kang, Minsoo; Hwang, Seung Hyun; Yang, Joshua; Harb, Mohammed; Al-Imam, Mohamed; Madkour, Kareem; ElManhawy, Wael; Kwan, J.W.

doi:10.1117/12.2218806

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combined with the test pattern enumeration methodology, this method could catch some yield detractor patterns [48] . Test patterns are similar to the real self-aligned multiple patterning design, and simulations are conducted on them to identify hotspots [49,50] . These hotspots are used to consequently optimize the fabrication process and models.…”

Section: Hotspot Detection At An Early Stage Of New Technology Node Developmentmentioning

confidence: 99%

Advanced process and electron device technology

Zhang¹,

Su²,

Chang³

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

This article reviews advanced process and electron device technology of integrated circuits, including recent featuring progress and potential solutions for future development. In 5 years, for pushing the performance of fin field-effect transistors (FinFET) to its limitations, several processes and device boosters are provided. Then, the three-dimensional (3D) integration schemes with alternative materials and device architectures will pave paths for future technology evolution. Finally, it could be concluded that Moore's law will undoubtedly continue in the next 15 years.

show abstract

Section: Hotspot Detection At An Early Stage Of New Technology Node Developmentmentioning

confidence: 99%

Advanced process and electron device technology

Zhang¹,

Su²,

Chang³

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Calibre® LSG [19] is a tool for the random generation of realistic design-like layouts, without design rule violations. The tool uses a Monte Carlo method to apply randomness in the generation of layout clips by inserting basic unit patterns in a grid.…”

Section: Calibre ® Lsgmentioning

confidence: 99%

Machine Learning in Failure Regions Detection and Parameters Analysis

Wahab¹,

Adawi²,

Khater³

2019

IJNDC

View full text Add to dashboard Cite

Testing automation is one of the challenges facing the software development industry, especially for large complex products. This paper proposes a mechanism called Multi Stage Failure Detector (MSFD) for automating black box testing using different machine learning algorithms. The input to MSFD is the tool's set of parameters and their value ranges. The parameter values are randomly sampled to produce a large number of parameter combinations that are fed into the software under test. Using neural networks, the resulting logs from the tool are classified into passing and failing logs and the failing logs are then clustered (using mean-shift clustering) into different failure types. MSFD provides visualization of the failures along with the responsible parameters. Experiments on and results for two real-world complex software products are provided, showing the ability of MSFD to detect all failures and cluster them into the correct failure types, thus reducing the analysis time of failures, improving coverage, and increasing productivity.

show abstract

A random approach of test macro generation for early detection of hotspots

Cited by 2 publications

References 0 publications

Advanced process and electron device technology

Advanced process and electron device technology

Machine Learning in Failure Regions Detection and Parameters Analysis

Contact Info

Product

Resources

About