The Role Of Metrology And Inspection In Semiconductor Processing

Keefer, Mark E.; Pinto, Rebecca Howland; Dennison, Cheri; Turlo, James

doi:10.1016/b978-081551442-8.50011-0

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…As the complexity of the devices increases, having a robust platform for fast monitoring becomes critical for the process development, optimization, and troubleshooting. 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 .…”

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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show abstract

“…Metrology techniques can be roughly classified into three categories by application: critical dimension (CD) and overlay measurement, particle and pattern defect detection, and thin film parameter measurement. 1 The metrology ecosystem required to characterize different stages in the semiconductor manufacturing process includes several of these techniques.…”

Section: Introductionmentioning

confidence: 99%

Reflective grazing incidence EUV nanoscope for wafer metrology

Shen

Ekinci²,

Mochi³

2022

International Conference on Extreme Ultraviolet Lithography 2022

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EUV lithography is currently being used in semiconductor high volume manufacturing, however, performing non-destructive metrology at the nanoscale for different types of structures and materials is still an extremely challenging task. The EUV reflective grazing incidence nanoscope (REGINE) is a new synchrotron end-station dedicated to non-destructive EUV metrology at the nanoscale for surface/layered structures with different material compositions. REGINE is being developed at the Swiss Light Source synchrotron and aims to enable grazing-incidence coherent diffraction imaging (CDI), scatterometry, and reflectometry in the energy range of 80 to 200 eV. In this work, we present the concept of the REGINE tool and the results of its commissioning experiments.

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Introduction to Measuring MEMS

Airaksinen¹

2015

Handbook of Silicon Based MEMS Materials and Technologies

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The Role Of Metrology And Inspection In Semiconductor Processing

Cited by 10 publications

References 4 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

Reflective grazing incidence EUV nanoscope for wafer metrology

Introduction to Measuring MEMS

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