Multiobjective optimization-based decision support for building digital twin maturity measurement

Chen, Zhen-Song; Chen, Kou-Dan; Xu, Ya-Qiang; Pedrycz, Witold; Skibniewski, Mirosław J.

doi:10.1016/j.aei.2023.102245

Cited by 13 publications

(1 citation statement)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…14 Chen et al introduced a unique evaluation methodology for developing digital twin (BDT) maturity, which employs a fairness-aware MOO model focusing on regulating BDT development and allowing informed decisionmaking in the construction sector. 15 Kim et al applied a ML-based optimization approach to bulk fin field effect transistor (FinFET) to assess the influence of point defects. 16 Zuluaga et al introduced a Pareto active learning (PAL) method that actively samples the design space to anticipate the Pareto-optimal set.…”

mentioning

confidence: 99%

Optimization and performance indication of surrounding gate tunnel field‐effect transistors based on machine learning

Charumathi,

Balamurugan,

Suguna

et al. 2024

Int J Numerical Modelling

View full text Add to dashboard Cite

Selecting designs that efficiently optimize multiple objectives simultaneously is an important problem in several distinct industries. Typically, there is not a single ideal design; rather, there are several Pareto‐optimal designs that provide the best possible trade‐offs between the objectives. However, evaluating every design might be expensive, making a thorough search for the whole Pareto optimum set impractical. The aforementioned issue with technology computer‐aided design (TCAD) while investigating a multidimensional parameter set for device design is addressed using Pareto active learning (PAL) and the nondominated sorting genetic algorithm‐III (NSGA‐III) which are metaheuristics‐based multiobjective optimization (MOO) techniques. NSGA‐III adeptly analyzes the tradeoffs among multiple objectives while ensuring diversity in the design space. PAL forecasts the Pareto‐optimal set with intelligence by deliberately sampling the design space. This work focusses on improving the performance of surrounding gate tunnel field‐effect transistors (SGTFETs) by optimizing and assessing their complex designs in terms of multiple objectives, including power, energy, speed, and variability. This paper presents a novel MOO framework that incorporates machine learning (ML) approaches, including NSGA‐III and PAL in SGTFETs technology. The framework provides effective global optimization without gradients, allowing for the automatic recognition of the best solutions. The outcomes show the possibility of ML‐based MOO to create next‐generation nanoscale transistors.

show abstract

mentioning

confidence: 99%