Smart manufacturing applications for inspection and quality assurance processes

Galindo-Salcedo, Maremys; Pertúz-Moreno, Altagracia; Guzmán-Castillo, Stefania; Gómez-Charris, Yulineth; Romero-Conrado, Alfonso R.

doi:10.1016/j.procs.2021.12.282

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…Such as new equipment release, new process changes at upstream steps or single event toggles (chamber recover from maintenance, new gas injection etc). Which is more applicable for a Smart manufacturing [10].…”

Section: Standard Deviation Variation Reductionmentioning

confidence: 99%

In-depth discussion on pitch doubling flow CpK improvement through process integration

Liu,

han

2024

Advanced Etch Technology and Process Integration for Nanopatterning XIII

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Self-aligned double patterning scheme is the dominant technique which is widely adopted in semiconductor industry to achieve finer patterns before extreme ultraviolet (EUV) lithography volume production is available. The critical dimension (CD) for the key structures fabricated from SADP flow, normally referred to as Bitline (BL) or Wordline (WL), is crucial to enable the microcircuitry to operate properly. Therefore, the CDs' precise control receives substantial attention as a key indicator to demonstrate the manufacturing process capability in quality management. This paper presents an in-depth analysis on BL CDs' variation control from SADP scheme, followed by several applicable approaches for process capability improvement across multiple modules through conceptual and experimental illustration, which could serve as a guideline for semiconductor manufacturing industry.

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Section: Standard Deviation Variation Reductionmentioning

confidence: 99%

In-depth discussion on pitch doubling flow CpK improvement through process integration

Liu,

han

2024

Advanced Etch Technology and Process Integration for Nanopatterning XIII

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“…There exist many studies today that address the challenges and need of quality assurance systems on the production assembly line. The deployment of cameras to capture the workflow on the assembly line and the use of computer vision, machine learning and deep learning methods to provide insights is one of the common solutions that exits [3]. Some of the reasons for using vision-based methods is the ability to monitor the assembly in real-time through live image capturing thus reducing the burden of physical human inspection.…”

Section: Vision Based Assembly Monitoring Systemsmentioning

confidence: 99%

Assembly Line Quality Assurance Through Hand Tracking and Object Detection

Mokuwe

Goorun²,

Crafford³

2022

MATEC Web Conf.

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This work presents a vision-based quality assurance system that does assembly line monitoring. The system is developed using machine learning hand tracking and object detection methods to monitor the worker’s hand movement while evaluating the correctness of the assembly. Feedback about the order of the steps the worker has taken is continuously shown to the user. This work has the potential to reduce the amount of manual work required for quality assurance in assembly line.

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“…Therefore, manufacturers must use a quality control system to reduce financial risk and reputational damage [6]. Thorough testing of products is required before shipment [7].…”

Section: Introductionmentioning

confidence: 99%

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Aziz,

Trisiawan,

Suyasmini

et al. 2023

Sinergi

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Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.

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Smart manufacturing applications for inspection and quality assurance processes

Cited by 12 publications

References 23 publications

In-depth discussion on pitch doubling flow CpK improvement through process integration

In-depth discussion on pitch doubling flow CpK improvement through process integration

Assembly Line Quality Assurance Through Hand Tracking and Object Detection

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

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