Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This research aims to enhance quantum mechanical modeling of semiconductor nanodevices by automating basis selection through artificial intelligence (AI). The approach involves developing a comprehensive database of optimized basis representations and training AI models to interact with quantum code libraries, enabling efficient identification and prediction of suitable bases. By reducing computational complexity, minimizing manual intervention, and standardizing simulations across diverse device architectures, this AI-driven automation enhances computational efficiency and scalability. The outcomes of this research have the potential to accelerate semiconductor design and simulation, making advanced modeling techniques more accessible to both academic researchers and industry professionals.
Keywords:
Cite Article:
"AI-Driven Optimization for Semiconductor Nanodevice Modeling", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.c291-c294, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503244.pdf
Downloads:
000654
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator