TensorFlow: Revolutionizing Large-Scale Machine Learning in Complex Semiconductor Design
DOI:
https://doi.org/10.47941/ijce.1812Keywords:
Semiconductor Design, Machine Learning, Tensorflow, Google, PRISMAAbstract
The development of semiconductor manufacturing processes is becoming more intricate in order to meet the constantly growing need for affordable and speedy computing devices with greater memory capacity. This calls for the inclusion of innovative manufacturing techniques hardware components, advanced intricate assemblies and. Tensorflow emerges as a powerful technology that comprehensively addresses these aspects of ML systems. With its rapid growth, TensorFlow finds application in various domains, including the design of intricate semiconductors. While TensorFlow is primarily known for ML, it can also be utilized for numerical computations involving data flow graphs in semiconductor design tasks. Consequently, this SLR (Systematic Literature Review) focuses on assessing research papers about the intersection of ML, TensorFlow, and the design of complex semiconductors. The SLR sheds light on different methodologies for gathering relevant papers, emphasizing inclusion and exclusion criteria as key strategies. Additionally, it provides an overview of the Tensorflow technology itself and its applications in semiconductor design. In future, the semiconductors may be designed in order to enhance the performance, and the scalability and size can be increased. Furthermore, the compatibility of the tensor flow can be increased in order to leverage the potential in semiconductor technology.
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Copyright (c) 2024 Rajat Suvra Das
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