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1.
Opt Express ; 32(8): 14705-14712, 2024 Apr 08.
Artigo em Inglês | MEDLINE | ID: mdl-38859407

RESUMO

Tomographic Volumetric Additive Manufacturing (TVAM) allows printing of mesoscopic objects within seconds or minutes. In TVAM, tomographic patterns are illuminated onto a rotating glass vial which contains a photosensitive resin. Current pattern optimization is based on a ray optical assumption which ultimately leads to limited resolution around 20 µm and varying throughout the volume of the 3D object. In this work, we introduce a rigorous wave-based optical amplitude optimization scheme for TVAM which shows that high-resolution printing is theoretically possible over the full volume. The wave optical optimization approach is based on an efficient angular spectrum method of plane waves with custom written memory efficient gradients and allows for optimization of realistic volumes for TVAM such as (100µm)3 or (10 mm)3 with 5503 voxels and 600 angles. Our simulations show that ray-optics start to produce artifacts when the desired features are 20 µm and below and more importantly, the amplitude modulated TVAM can reach sub 20 µm features when optimizing the patterns using a full wave model.

2.
Opt Express ; 31(9): 13763-13797, 2023 Apr 24.
Artigo em Inglês | MEDLINE | ID: mdl-37157257

RESUMO

Conventional (CP) and Fourier (FP) ptychography have emerged as versatile quantitative phase imaging techniques. While the main application cases for each technique are different, namely lens-less short wavelength imaging for CP and lens-based visible light imaging for FP, both methods share a common algorithmic ground. CP and FP have in part independently evolved to include experimentally robust forward models and inversion techniques. This separation has resulted in a plethora of algorithmic extensions, some of which have not crossed the boundary from one modality to the other. Here, we present an open source, cross-platform software, called PtyLab, enabling both CP and FP data analysis in a unified framework. With this framework, we aim to facilitate and accelerate cross-pollination between the two techniques. Moreover, the availability in Matlab, Python, and Julia will set a low barrier to enter each field.

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