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

Journal of Microelectronic Research - May 2003

https://scholarworks.rit.edu/meec_archive/1012/thumbnail.jp

Analysis of Backscattered Electron Signals for X-Ray Mask Inspection

A rapid and automated inspection system is a necessity for the detection of defects in x-ray and optical lithography masks. The design of an electron-beam mask inspection system requires a complete understanding of the backscattered electron signal from the various defects which will be encountered. A Monte Carlo simulation program has been used to study the effects of electron-beam size, detector placement, defect type, electron-beam voltage, and absorber thickness on the back-scattered electron signal