5,514 research outputs found
AI/ML Algorithms and Applications in VLSI Design and Technology
An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations
Artificial intelligence for superconducting transformers
Artificial intelligence (AI) techniques are currently widely used in different parts of the electrical engineering sector due to their privileges for being used in smarter manufacturing and accurate and efficient operating of electric devices. Power transformers are a vital and expensive asset in the power network, where their consistent and fault-free operation greatly impacts the reliability of the whole system. The superconducting transformer has the potential to fully modernize the power network in the near future with its invincible advantages, including much lighter weight, more compact size, much lower loss, and higher efficiency compared with conventional oil-immersed counterparts. In this article, we have looked into the perspective of using AI for revolutionizing superconducting transformer technology in many aspects related to their design, operation, condition monitoring, maintenance, and asset management. We believe that this article offers a roadmap for what could be and needs to be done in the current decade 2020-2030 to integrate AI into superconducting transformer technology
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