EUV mask feature reconstruction via phase retrieval

Ansuinelli, Paolo; Coene, W.; Urbach, Paul

doi:10.1117/12.2525976

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…5(b,d). The error, defined in (11) and shown in Fig 5(c) shows an overall better reconstruction and convergence when the prior is included in the optimization algorithm. Practically, we have observed the algorithm to converge in about a third of the iterations of the regular PIE.…”

Section: Extrusion Defectsmentioning

confidence: 96%

“…Ptychography [6] -a lensless imaging method that enables wide field-of-view, high resolution imaging via phase retrieval -is a possible candidate for the inspection of samples, in reflective mode at short wavelengths [7][8][9]. This coherent diffractive imaging (CDI) method has been introduced as a potential actinic inspection tool for EUV mask inspection [10][11][12]. In ptychography a probe sequentially illuminates a given scattering object at partially overlapping positions.…”

Section: Introductionmentioning

confidence: 99%

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Improved ptychographic inspection of EUV reticles via inclusion of prior information

2020

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The development of actinic mask metrology tools represents one of the major challenges to be addressed on the roadmap of extreme ultra violet (EUV) lithography. Technological advancements in EUV lithography result in the possibility to print increasingly fine and highly resolved structures on a silicon wafer, however the presence of fine-scale defects, interspersed in the printable mask layout, may lead to defective wafer prints. Hence the development of actinic methods for review of potential defect sites becomes paramount. Here, we report on a ptychographic algorithm that makes use of prior information about the object to be retrieved, generated by means of rigorous computations, to improve the detectability of defects whose dimensions are of the order of the wavelength. The comprehensive study demonstrates that the inclusion of prior information as a regularizer in the ptychographic optimization problem results in a higher reconstruction quality and an improved robustness to noise with respect to the standard ptychographic iterative engine (PIE). We show that the proposed method decreases the number of scan positions necessary to retrieve an high quality image and relaxes requirements in terms of signal to noise ratio (SNR). The results are further compared with the state-of-art total variation based ptychographic imaging.

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Section: Extrusion Defectsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Improved ptychographic inspection of EUV reticles via inclusion of prior information

2020

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“…A well known example is the detection of the mirror defect of the Hubble space telescope, 1 but it can also be applied for instance in characterisation of the objective aberration in semiconductor image-based quality tools. 2 Characterisation (and correction) of such aberrations is necessary to increase the inspection tool throughput required in Industry 4.0. 3,4 A big class of PR methods is based on the projections.…”

Section: Introductionmentioning

confidence: 99%

Phase retrieval from overexposed PSF: a projection-based approach

Soloviev

Noom

Thao

et al. 2022

Quantitative Phase Imaging VIII

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We investigate the general adjustment of projection-based phase retrieval algorithms for use with saturated data. In the phase retrieval problem, model fidelity of experimental data containing a non-zero background level, fixed pattern noise, or overexposure, often presents a serious obstacle for standard algorithms. Recently, it was shown that overexposure can help to increase the signal-to-noise ratio in AI applications. We present our first results in exploring this direction in the phase retrieval problem, using as an example the Gerchberg-Saxton algorithm with simulated data. The proposed method can find application in microscopy, characterisation of precise optical instruments, and machine vision applications of Industry4.0.

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“…In the field of modern advanced optical imaging, wavefront detection is essential for high‐resolution imaging systems design. Phase retrieval [1] has become an important wavefront sensing technique for high‐resolution optical imaging systems [2, 3], which is already used in many advanced imaging systems, such as large aperture high‐resolution space telescope [4–6], optical microscopes [7, 8], X‐ray imaging [9–11], extreme ultraviolet lithography [12], etc. In the field of wavefront sensing, it is well‐known for its very successful application to Hubble space telescope in aberration correction on‐orbit [13].…”

Section: Introductionmentioning

confidence: 99%

Simple accurate model‐based phase diversity phase retrieval algorithm for wavefront sensing in high‐resolution optical imaging systems

et al. 2020

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In optical imaging systems, the aberration is an important factor that impedes realising diffraction‐limited imaging. Accurate wavefront sensing and control play important role in modern high‐resolution optical imaging systems nowadays. In this study, a simple model‐based phase retrieval algorithm is proposed for accurate efficient wavefront sensing with high dynamic range. In the authors’ algorithm, a wavefront is represented by the Zernike polynomials, and the Zernike coefficients are solved by the least‐squares‐based non‐linear optimisation method, i.e. the Lederberg–Marquardt algorithm, with multiple phase‐diversity images. The numerical results show that the proposed algorithm is capable of retrieving wavefront with a large dynamic range up to seven wavelength and robust to noise. In comparison, the proposed algorithm is more efficient than the existing model‐based technique and more accurate than existing Fourier ‐ transformation‐based iterative techniques.

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EUV mask feature reconstruction via phase retrieval

Cited by 4 publications

References 26 publications

Improved ptychographic inspection of EUV reticles via inclusion of prior information

Improved ptychographic inspection of EUV reticles via inclusion of prior information

Phase retrieval from overexposed PSF: a projection-based approach

Simple accurate model‐based phase diversity phase retrieval algorithm for wavefront sensing in high‐resolution optical imaging systems

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