A new lens capability for three-dimensional (3D) focal control is presented using an optofluidic system consisting of
- Award ID(s):
- 2046134
- NSF-PAR ID:
- 10397362
- Date Published:
- Journal Name:
- The 36th IEEE International Conference on Micro Electro Mechanical Systems (MEMS 2023)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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n × n arrayed liquid prisms. Each prism module contains two immiscible liquids in a rectangular cuvette. Using the electrowetting effect, the shape of the fluidic interface can be rapidly adjusted to create its straight profile with the prism’s apex angle. Consequently, an incoming ray is steered at the tilted interface due to the refractive index difference between two liquids. To achieve 3D focal control, individual prisms in the arrayed system are simultaneously modulated, allowing incoming light rays to be spatially manipulated and converged on a focal point located atP focal(f x,f y,f z) in 3D space. Analytical studies were conducted to precisely predict the prism operation required for 3D focal control. Using three liquid prisms positioned on thex -,y -, and 45°-diagonal axes, we experimentally demonstrated 3D focal tunability of the arrayed optofluidic system, achieving focal tuning along lateral, longitudinal, and axial directions as wide as 0 ≤f x ≤ 30 mm, 0 ≤f y ≤ 30 mm, and 500 mm ≤f z ≤ ∞. This focal tunability of the arrayed system allows for 3D control of the lens’s focusing power, which could not be attained by solid-type optics without the use of bulky and complex mechanical moving components. This innovative lens capability for 3D focal control has potential applications in eye-movement tracking for smart displays, autofocusing of smartphone cameras, or solar tracking for smart photovoltaic systems. -
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Abstract Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.
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Abstract Microinjection protocols are ubiquitous throughout biomedical fields, with hollow microneedle arrays (MNAs) offering distinctive benefits in both research and clinical settings. Unfortunately, manufacturing‐associated barriers remain a critical impediment to emerging applications that demand high‐density arrays of hollow, high‐aspect‐ratio microneedles. To address such challenges, here, a hybrid additive manufacturing approach that combines digital light processing (DLP) 3D printing with “ex situ direct laser writing (
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