A Review of Machine Learning-Based Device Modeling and Performance Optimization for FinFETs

The research explores the use of performance optimization strategies to further enhance FinFET device efficiency. By analyzing data from FinFets under various conditions, optimization algorithms are developed to fine-tune device parameters. This approach not only refines individual device performance but also contributes to improved circuit efficiency. The synergy between accurate models and optimization algorithms leads to faster, more energy-efficient, and reliable devices.The significance of this research extends to its potential to revolutionize design and manufacturing processes in the electronics industry. The ability to accurately model FinFET devices and optimize their performance opens new possibilities for innovation. Engineers can now design and manufacture devices with greater precision, tailoring them to specific requirements and achieving unprecedented levels of efficiency. Moreover, the integration of ML in this context addresses the limitations of traditional approaches, particularly the computational demands associated with extensive simulations. ML-based models streamline the prediction of device behavior, significantly reducing the need for time-consuming simulations. This not only accelerates the design process but also conserves computational resources, making it a more sustainable and efficient solution. In conclusion, this research marks a paradigm shift in semiconductor device modeling and optimization, leveraging machine learning to advance FinFET understanding and enhance performance. Beyond individual devices, it influences circuit efficiency, paving the way for a more advanced and sustainable electronics industry.