Machine learning-guided analysis of CIGS solar cell efficiency: Deep learning classification and feature importance evaluation
The increasing sensitivity of thin-film solar cells to variations in design parameters is
becoming more pronounced with ongoing advancements in material science and device …
becoming more pronounced with ongoing advancements in material science and device …
Machine learning-assisted investigation of CIGS thin-film solar cell degradation using deep learning analysis
The increasing sensitivity of Copper Indium Gallium Selenide (CIGS) thin-film solar cells to
variations in design parameters and degradation effects highlights the necessity for a …
variations in design parameters and degradation effects highlights the necessity for a …
Efficient SnS Solar Cells via Plasmonic Light Trapping and Alternative Buffer Layers: A Combined Machine Learning and FDTD Analysis
In this work, we propose a novel design framework based on combined finite-difference time-
domain (FDTD) simulations and machine learning (ML) analysis, aiming to improve the light …
domain (FDTD) simulations and machine learning (ML) analysis, aiming to improve the light …
Analog performance investigation of 10 nm Junctionless GAA FETs using Machine learning methods and deep learning analysis
With the continuous downscaling of analog CMOS-based circuits, the sensitivity of
nanoelectronic devices to design parameter variations has significantly increased. In this …
nanoelectronic devices to design parameter variations has significantly increased. In this …
Machine Learning‐Guided Design of 10 nm Junctionless Gate‐All‐Around Metal Oxide Semiconductor Field Effect Transistors for Nanoscaled Digital Circuits
In this paper, we introduce an innovative design approach based on combined numerical
simulations and machine learning (ML) analysis to investigate the design key parameters of …
simulations and machine learning (ML) analysis to investigate the design key parameters of …