Ana Sayfa Science Physic Revolutionizing Mobile Cameras with Microscopic LED Technology

Revolutionizing Mobile Cameras with Microscopic LED Technology

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Revolutionizing Mobile Cameras with Microscopic LED Technology

Revolutionizing Mobile Cameras with Microscopic LED Technology

In a groundbreaking development, researchers from the Singapore-MIT Alliance for Research and Technology (SMART) have created the world’s smallest LED (light-emitting diode) that can convert existing mobile phone cameras into high-resolution microscopes. This innovation could revolutionize various applications, from diagnostics for indoor farmers to sustainable agriculture and biomedical imaging.

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Fig. 1: Photo of the gas cell for high harmonic generation of extremely short-wavelength ultraviolet attosecond pulses. Credit: Max Hartmann, MPI für Kernphysik

The World’s Smallest LED and Holographic Microscope

Researchers at SMART have managed to fabricate the smallest known LED, which is smaller than the wavelength of light itself. This achievement has paved the way for the construction of the world’s smallest holographic microscope, with the potential to transform conventional mobile phone cameras into powerful imaging devices.

The researchers were also able to develop a novel neural networking algorithm that can reconstruct objects imaged by the holographic microscope, eliminating the need for bulky conventional microscopes or additional optics. This advancement could significantly impact the field of photonics, addressing the long-standing challenge of creating a powerful on-chip emitter smaller than a micrometer.

Silicon Emitters and CMOS Integration Challenges

Silicon emitters have shown promise as nanoscale, individually controllable emitters, but their low quantum efficiency due to the indirect bandgap has limited their application. Moreover, integrating the fabrication processes of existing emitter materials, such as rare-earth-doped glass, Ge-on-Si, and heterogeneously integrated III–V materials, into standard complementary metal-oxide-semiconductor (CMOS) platforms has proven difficult.

In their recently published Nature Communications paper, SMART researchers described the development of the smallest reported Si emitter with a light intensity comparable to state-of-the-art Si emitters with much larger emission areas. The researchers also unveiled a novel, untrained deep neural network architecture for reconstructing images from a holographic microscope in another paper published in the journal Optica.

The Novel CMOS-Integrated Sub-Wavelength Scale LED

The LED developed by SMART researchers is a CMOS-integrated sub-wavelength scale LED at room temperature, exhibiting high spatial intensity (102 ± 48 mW/cm2) and possessing the smallest emission area (0.09 ± 0.04 μm2) among all known Si emitters. To demonstrate a potential practical application, the researchers integrated this LED into an in-line, centimeter-scale, all-silicon holographic microscope requiring no lens or pinhole, integral to a field known as lensless holography.

Fig. 2: Illustration of the classical trajectory (green) with multiple recollisions and changes of the quantum wave packets (red/blue background) for a NIR (red) laser-driven electron emerging from XUV (violet) ionization of helium. Credit: MPI für Kernphysik

Image Reconstruction with Untrained Deep Neural Networks

Traditional lensless holography reconstruction methods require detailed knowledge of the experimental setup and are sensitive to variables such as optical aberrations, noise, and the twin image problem. The research team developed a deep neural network architecture that improves image reconstruction quality by incorporating total variation regularization and accounting for the wide spectral bandwidth of the source.

This untrained neural network eliminates the need for training data by embedding a physics model within the algorithm, offering blind source spectrum recovery from a single diffracted intensity pattern. This approach marks a significant departure from previous supervised learning techniques.

Potential Applications and Future Directions

The synergetic combination of CMOS micro-LEDs and the neural network developed by the researchers could be employed in various computational imaging applications, including compact microscopes for live-cell tracking or spectroscopic imaging of biological tissues such as living plants. This work also demonstrates the feasibility of next-generation on-chip imaging systems.

In-line holography microscopes have been employed for various applications, including particle tracking, environmental monitoring, biological sample imaging, and metrology. Further applications could involve arraying these LEDs in CMOS to generate programmable coherent illumination for more.


Original Article: https://www.nature.com/articles/s41467-023-36639-1

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