Harnessing machine learning to improve the success rate of stimuli generation

Fine, Shai; Freund, Ari; Jaeger, I.; Mansour, Yishay; Naveh, Y.; Ziv, Avi

doi:10.1109/hldvt.2005.1568823

Cited by 2 publications

(1 citation statement)

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“…Some initial states can even lead to poor fault coverage, thereby resulting in faulty products. Bayesian networks are used to automatically and approximately identify the region of favorable initial states; however, they require a certain level of human guidance to select one of the initial states [243]. Identification of powerrisky test patterns is also an important task as excessive test power can lead to test failures due to IR drop, noise, etc.…”

Section: A Functional Verificationmentioning

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 Functional Verificationmentioning

confidence: 99%