Re-examining VLSI manufacturing and yield through the lens of deep learning

Alawieh, Mohamed Baker; Ye, Wei; Pan, David Z.

doi:10.1145/3400302.3415779

Cited by 3 publications

(1 citation statement)

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“…Based on an adversarial autoencoder, a pattern style detection tool is designed to examine the pattern styles and filter out unrealistic generated patterns. A novel confidence-aware deep learning model for post fabrication wafer map defect is proposed in [217]. The experiment results on industrial wafer datasets demonstrate superior accuracy compared to traditional approached.…”

Section: A Ai For Lithographymentioning

confidence: 99%

AI/ML Algorithms and Applications in VLSI Design and Technology

Amuru¹,

Vudumula²,

K.³

et al. 2022

Preprint

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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 dataintensive 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.

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Section: A Ai For Lithographymentioning

confidence: 99%