An Automated Framework for Variability Analysis for Integrated Circuits Using Metaheuristics

Chordia, Aksh; Tripathi, Jai Narayan

doi:10.1109/tsipi.2022.3202150

Cited by 5 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Sensitivity to Design Parameter Variations/Robustness [ 232 ]: Optimizing designs to wider variations in process parameters (temperature, atmosphere, material choice and the tolerances of their properties, various technological uncertainties) can be used to achieve the maximum performance robustness and insensitivity to variations in external parameters. Production Yield [ 233 ]: Bio-inspired optimization can help decrease faults and increase the percentage of successfully produced chips during planar technology fabrication. By considering process variations, temperature variations and component tolerances, bio-inspired algorithms can optimize circuit designs to achieve robust performance and improve yield.…”

Section: Applications In Microelectronicsmentioning

confidence: 99%

“…Production Yield [ 233 ]: Bio-inspired optimization can help decrease faults and increase the percentage of successfully produced chips during planar technology fabrication. By considering process variations, temperature variations and component tolerances, bio-inspired algorithms can optimize circuit designs to achieve robust performance and improve yield.…”

Section: Applications In Microelectronicsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Jakšić

Devi²,

Jakšić

et al. 2023

Biomimetics

View full text Add to dashboard Cite

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

show abstract