A neural-network approach to recognize defect spatial pattern in semiconductor fabrication

Chen, Feilong; Liu, Shu-Fan

doi:10.1109/66.857947

Cited by 201 publications

(13 citation statements)

References 22 publications

(13 reference statements)

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“…Reliable semiconductor processes for high yield manufacturing are achieved by visually inspecting many devices at different stages of a process flow. , The inspections generally use optical methods to locate defects quickly, accurately, and consistently . These methods rely on image processing and machine learning algorithms and have been used for detecting scratches in Si wafers, , and defects in lithography and dry etching processes . As the critical device dimensions shrink, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) imaging, which provide a higher spatial resolution than optical methods, become indispensable metrology tools for the semiconductor industry. , For example, to perform high throughput defect inspection in photomasks, new advanced SEM methods have been introduced …”

Section: Resultsmentioning

confidence: 99%

Deep Learning-Based High Throughput Inspection in 3D Nanofabrication and Defect Reversal in Nanopillar Arrays: Implications for Next Generation Transistors

Anand

Ghosh

Aabdin

et al. 2021

ACS Appl. Nano Mater.

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Densely packed high-aspect-ratio (HAR) nanostructures are the core elements of future microelectronics components. Manufacturing these nanostructures for device applications requires multiple fabrication steps involving wet processes, followed by a drying step. During drying, these nanostructures experience strong capillary forces that induce their bending and cause them to permanently stick to their neighbors, a phenomenon often referred to as pattern collapse. The pattern collapse and the difficulty in reliably identifying damaged nanostructures pose a critical challenge for the fabrication of HAR devices. Here, we developed a machine learning-based approach to identify collapsed nanostructures from a large patterned array of vertical Si nanopillars with 99.84% accuracy. Furthermore, we show that the pattern collapse can be reversed by selectively etching the native surface SiO2 layer of the nanopillars at their adhesions. Our approach for accurate and rapid identification of the collapsed nanostructures combined with the method to reverse this damage provides a versatile platform for developing high-yield fabrication processes for nanoscale semiconductor devices.

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Section: Resultsmentioning

confidence: 99%

Deep Learning-Based High Throughput Inspection in 3D Nanofabrication and Defect Reversal in Nanopillar Arrays: Implications for Next Generation Transistors

Anand

Ghosh

Aabdin

et al. 2021

ACS Appl. Nano Mater.

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“…While some techniques apply solely to specific cases, others work on a wide range of domains. Physical defect detection solutions typically employ computer vision algorithms (Li, Wang, and Weikang 2002;Brosnan and Sun 2002;Gallarda et al 2003;Vilella 2009;Viharos et al 2016), artificial neural networks (ANN) (Kumar 2003;Chen and Liu 2000) and Gabor filters (Escofet, Navarro, and Pladellorens et al 1998;Bodnarova, Bennamoun, and Latham 2002).…”

Section: Defect Detectionmentioning

confidence: 99%

An online machine learning framework for early detection of product failures in an Industry 4.0 context

Soto

Tavakolizadeh

Gyulai

2019

International Journal of Computer Integrated Manufacturing

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Current paradigms such as the Internet of Things (IoT) and cyber-physical systems are transforming production environments, where related processes are not only faster and with higher standards, but also more flexible and adaptable to changes in the environment. To address the ever-increasing flexibility requirements while keeping current production standards, a new set of technologies is needed. This paper presents an IoT machine learning and orchestration framework, applied to detection of failures of surface mount devices during production. The paper shows how to build a scalable and flexible system for real-time, online machine learning. Furthermore, the approach is evaluated by using a novel and realistic simulation of a production line for electronic devices as a case study. The system evaluation is done in a holistic manner by analyzing various aspects involving the software architecture, computational scalability, model accuracy, production performance, among others.

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“…Besides ART1, which is implicated in our research, some other models in the family [20][21][22][23][24] are studied by researchers of variable fields such as medical diagnosis [25], semiconductor fabrication [26] and intrusion detection [27].…”

Section: Adaptive Resonance Theorymentioning

confidence: 99%

Adaptive Resonance Theory Based Two-Stage Chinese Name Disambiguation

Wang¹,

Liu²,

Wang³

et al. 2012

IJIS

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It's common that different individuals share the same name, which makes it time-consuming to search information of a particular individual on the web. Name disambiguation study is necessary to help users find the person of interest more readily. In this paper, we propose an Adaptive Resonance Theory (ART) based two-stage strategy for this problem. We get a first-stage clustering result with ART1 model and then merge similar clusters in the second stage. Our strategy is a mimic process of manual disambiguation and need not to predict the number of clusters, which makes it competent for the disambiguation task. Experimental results show that, in comparison with the agglomerative clustering method, our strategy improves the performance by respectively 0.92% and 5.00% on two kinds of name recognition results.

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A neural-network approach to recognize defect spatial pattern in semiconductor fabrication

Cited by 201 publications

References 22 publications

Deep Learning-Based High Throughput Inspection in 3D Nanofabrication and Defect Reversal in Nanopillar Arrays: Implications for Next Generation Transistors

Deep Learning-Based High Throughput Inspection in 3D Nanofabrication and Defect Reversal in Nanopillar Arrays: Implications for Next Generation Transistors

An online machine learning framework for early detection of product failures in an Industry 4.0 context

Adaptive Resonance Theory Based Two-Stage Chinese Name Disambiguation

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