Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Kim, Sangpill; Min, Seong-Ji; Jung, Seung-Geun; Yu, Hyun-Yong

doi:10.1016/j.engappai.2024.109064

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2024.109064

|View full text |Cite

Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Sangpill Kim,

Seong-Ji Min,

Seung-Geun Jung

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Normalizing Flow Based Validity‐Preserving Inverse‐Design Model for Nanoscale MOSFETs

Ashai,

Badami,

Sarkar

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

A two‐stage inverse model for the design of gate‐all‐around nanowire metal oxide semiconductor field effect transistors (MOSFETs) is proposed in this article. The proposed model first validates the selection of output characteristics using a normalizing flow based generative model, and then predicts the device parameters corresponding to the valid output characteristics using a cascade of inverse and forward artificial neural networks (ANNs). This accurately captures any out‐of‐distribution datapoint in the output characteristics distribution and computes the device parameters through the inverse ANN, avoiding any conflicts created by non‐unique mappings. The two‐stage model instantly predicts possible device designs for a target output characteristic set without going for multiple iterations to arrive at a device‐design, highlighting the accuracy and robustness of the model.

show abstract

A Normalizing Flow Based Validity‐Preserving Inverse‐Design Model for Nanoscale MOSFETs

Ashai,

Badami,

Sarkar

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Cited by 1 publication

References 26 publications

A Normalizing Flow Based Validity‐Preserving Inverse‐Design Model for Nanoscale MOSFETs

A Normalizing Flow Based Validity‐Preserving Inverse‐Design Model for Nanoscale MOSFETs

Contact Info

Product

Resources

About