Machine Learning Assisted New Product Setup

Torres, J. Andres; Kissiov, Ivan; Mohamed, Essam E. M.; Carsten, Hartig; Richard, Gilles; Ken, J Ishii; Schueler, Stefan; Niehoff, Martin

doi:10.1109/asmc49169.2020.9185215

Cited by 3 publications

(2 citation statements)

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“…To address this, a model was developed to evaluate and identify the key parameters that predict process outcomes, specifically reliability in this case. This model helps prioritize key process steps that impact reliability [10] using Siemens' ML-based solution [3][4][5][6]. It also allows engineers to customize prediction models based on their domain knowledge.…”

Section: Identifying Key Process Steps For Reliabilitymentioning

confidence: 99%

“…Since it is not practical and economical in the fab to measure every wafer at all individual chip sites for every process step, we propose using fab data augmentation (FDA) to estimate missing measurement data for the steps where all measurements are not available. The critical process steps that impact reliability are then identified and ranked using an ensemble ML model based on gradient boosted trees [3][4][5][6]. The model can also predict the voltage-ramp-to-breakdown (Vramp) parameter, which is a critical intrinsic reliability value [7], in real time.…”

Section: Introductionmentioning

confidence: 99%

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AI-guided reliability diagnosis for 5,7nm automotive process

Kim

Lee²,

Choi³

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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Automotive semiconductor products demand high reliability. The current process of performing electrical test after fab-out may not be sufficient for efficient reliability management. This paper proposes an AI solution for improving the reliability of automotive semiconductor products. The solution includes two unique concepts: fab-data augmentation (FDA) to estimate missing values using partially available measurement data during the fabrication process and real-time prediction of reliability using machine learning (ML) models. The ML model is also used to identify and rank critical process steps that impact reliability, and to predict the reliability of wafers in real time. This allows low reliability wafers to be screened out early during the chip fabrication process, improving the overall reliability of the final product.

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Section: Identifying Key Process Steps For Reliabilitymentioning

confidence: 99%