Prof. Assad A. Oberai Profile

Prof. Assad A. Oberai

at Univ of Southern California

SPIE Involvement:

Author

Publications (8)

SPIE Journal Paper | 20 April 2020 Open Access

Deep learning-based detection, classification, and localization of defects in semiconductor processes

Dhruv Patel, Ravi Bonam, Assad Oberai

JM3, Vol. 19, Issue 02, 024801, (April 2020) https://doi.org/10.1117/12.10.1117/1.JMM.19.2.024801

KEYWORDS: Data modeling, Convolution, Semiconductors, Defect detection, Performance modeling, Semiconducting wafers, Image classification, Inspection, Content addressable memory, Process modeling

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Proceedings Article | 26 March 2019 Paper

Engineering neural networks for improved defect detection and classification

Dhruv Patel, Ravi Bonam, Assad Oberai

Proceedings Volume 10959, 1095910 (2019) https://doi.org/10.1117/12.2515065

KEYWORDS: Convolution, Defect detection, Semiconducting wafers, Inspection, Neural networks, Image classification, Semiconductors, Convolutional neural networks

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Proceedings Article | 4 March 2019 Presentation

Quantitative micro-elastography for cell mechanobiology (Conference Presentation)

Matt Hepburn, Philip Wijesinghe, Luke Major, Lixin Chin, Nicholas Hugenberg, Dawei Song, Assad Oberai, Yu Suk Choi, Brendan Kennedy

Proceedings Volume 10867, 1086728 (2019) https://doi.org/10.1117/12.2511486

KEYWORDS: Tissue optics, Image compression, 3D image processing, Coherence (optics), Elastography, Natural surfaces, Optical coherence tomography, Visualization, Stem cells

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Proceedings Article | 4 March 2019 Presentation

Linear and nonlinear optical coherence elastography in three dimensions (Conference Presentation)

Assad Oberai, Nicholas Hugenberg, Matt Hepburn, Philip Wijesinghe, Dawei Song, Brendan Kennedy

Proceedings Volume 10880, 1088002 (2019) https://doi.org/10.1117/12.2509574

KEYWORDS: Elastography, Coherence (optics), Tissues, Phase measurement, Optical coherence tomography, Inverse problems, In vivo imaging

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Proceedings Article | 24 April 2017 Presentation

Inverse methods for 3D quantitative optical coherence elasticity imaging (Conference Presentation)

Li Dong, Philip Wijesinghe, Nicholas Hugenberg, David Sampson, Peter R. Munro, Brendan Kennedy, Assad Oberai

Proceedings Volume 10067, 100670S (2017) https://doi.org/10.1117/12.2252600

KEYWORDS: Tissue optics, Elastography, Inverse optics, Coherence (optics), Coherence imaging, Inverse problems, 3D image processing, Optical imaging, Data processing, Visualization

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Proceedings Article | 27 April 2016 Presentation

Quantitative optical coherence elastography as an inverse elasticity problem (Conference Presentation)

Li Dong, Philip Wijesinghe, James Dantuono, David Sampson, Peter R. Munro, Brendan Kennedy, Assad Oberai

Proceedings Volume 9710, 971011 (2016) https://doi.org/10.1117/12.2213806

KEYWORDS: Inverse optics, Elastography, Tissue optics, Coherence (optics), Tissues, Imaging systems, Diagnostics, Inverse problems, Solids, Solid modeling

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Proceedings Article | 27 April 2016 Presentation

Inverse problems biomechanical imaging (Conference Presentation)

Assad Oberai

Proceedings Volume 9710, 97100W (2016) https://doi.org/10.1117/12.2216702

KEYWORDS: Inverse problems, Tissues, Tissue optics, Diagnostics, Ultrasonography, Magnetic resonance imaging, Optical coherence tomography, Optical microscopy, Arteries, Liver

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Proceedings Article | 16 March 2009 Paper

A computational technique to optimally design in-situ diffractive elements: applications to projection lithography at the resist resolution limit

Gonzalo Feijóo, Jaione Tirapu-Azpiroz, Alan Rosenbluth, Assad Oberai, Jayanth Jagalur Mohan, Kehan Tian, David Melville, Dario Gil, Kafai Lai

Proceedings Volume 7274, 72741E (2009) https://doi.org/10.1117/12.814251

KEYWORDS: Wavefronts, Holograms, Electromagnetism, Near field, Lithography, Optical design, Photomasks, Diffraction gratings, Optical lithography, Spatial frequencies

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Near-field interference lithography is a promising variant of multiple patterning in semiconductor device fabrication that can potentially extend lithographic resolution beyond the current materials-based restrictions on the Rayleigh resolution of projection systems. With H₂O as the immersion medium, non-evanescent propagation and optical design margins limit achievable pitch to approximately 0.53λ/nH₂O = 0.37λ. Non-evanescent images are constrained only by the comparatively large resist indices (typically1.7) to a pitch resolution of 0.5/nresist (typically 0.29). Near-field patterning can potentially exploit evanescent waves and thus achieve higher spatial resolutions. Customized near-field images can be achieved through the modulation of an incoming wavefront by what is essentially an in-situ hologram that has been formed in an upper layer during an initial patterned exposure. Contrast Enhancement Layer (CEL) techniques and Talbot near-field interferometry can be considered special cases of this approach. Since the technique relies on near-field interference effects to produce the required pattern on the resist, the shape of the grating and the design of the film stack play a significant role on the outcome. As a result, it is necessary to resort to full diffraction computations to properly simulate and optimize this process. The next logical advance for this technology is to systematically design the hologram and the incident wavefront which is generated from a reduction mask. This task is naturally posed as an optimization problem, where the goal is to find the set of geometric and incident wavefront parameters that yields the closest fit to a desired pattern in the resist. As the pattern becomes more complex, the number of design parameters grows, and the computational problem becomes intractable (particularly in three-dimensions) without the use of advanced numerical techniques. To treat this problem effectively, specialized numerical methods have been developed. First, gradient-based optimization techniques are used to accelerate convergence to an optimal design. To compute derivatives of the parameters, an adjoint-based method was developed. Using the adjoint technique, only two electromagnetic problems need to be solved per iteration to evaluate the cost function and all the components of the gradient vector, independent of the number of parameters in the design.

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