Nanotechnology

The world’s smallest LED and holographic microscope enables the conversion of existing smartphone cameras into high-resolution microscopes

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May 04, 2023

(Nanowerk News) Researchers from Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP) and Critical Analytics for Manufacturing Personalized-Medicine (CAMP) Interdisciplinary Research Groups (IRG) Singapore-MIT Alliance for Research and Technology (SMART), an MIT research firm in Singapore have developed LED (world’s smallest light emitting diode) that enables the conversion of existing smartphone cameras into high-resolution microscopes. Smaller than the wavelength of light, new LEDs are used to build the world’s smallest holographic microscope, paving the way for cameras found in everyday devices like cell phones to be turned into microscopes simply through modifications to silicon chips and software.

This technology is also a significant step forward in the miniaturization of diagnostics for indoor growers and sustainable agriculture.

This breakthrough was complemented by the researchers’ development of a revolutionary neural network algorithm capable of reconstructing objects measured by a holographic microscope, thereby enabling enhanced examination of microscopic objects such as cells and bacteria without the need for bulky conventional microscopes or additional optics. This research also paved the way for a major advance in photonics – the construction of powerful on-chip emitters smaller than a micrometer, which has long been a challenge in the field. LED and holographic microscopes a) Fully fabricated 300mm wafer (b) Close-up chip die (c) Infrared micrograph with LED on (d) Holographic microscope (e) Close-up of reconstructed holographic image (f) ground truth. (Image: SMART)

The light in most photonic chips comes from sources outside the chip, which leads to low overall energy efficiency and essentially limits the scalability of these chips. To overcome this problem, researchers have developed on-chip emitters using various materials such as doped rare-earth glass, Ge-on-Si, and heterogeneously integrated III-V materials.

While emitters based on these materials have shown promising device performance, integrating their fabrication processes into standard complementary metal-oxide-semiconductor (CMOS) platforms remains challenging. While silicon (Si) has shown potential as a candidate material for nanoscale and individually controllable emitters, Si emitters suffer from low quantum efficiency due to indirect bandgaps, and this fundamental drawback combined with limitations imposed by available materials and fabrication tools been in the way. the realization of a small original Si emitter in CMOS.

In a recent publication Nature Communications paper (“Si LED sub-wavelength integrated in the CMOS platform”), SMART researchers describe their development of the smallest reported Si emitter with a light intensity comparable to a state-of-the-art Si emitter with a much larger emission area.

In a related breakthrough, the SMART researchers also unveiled their novel construct, an untrained deep neural network architecture capable of reconstructing images from a holographic microscope in a paper recently published in the journal. OPTICAL (“Simultaneous spectral recovery and holography of CMOS micro-LEDs with untrained deep neural networks”).

The new LED developed by SMART researchers is a sub-wavelength-scale integrated CMOS LED at room temperature that exhibits high spatial intensity (102 ± 48 mW/cm2) and has the smallest emission area (0.09 ±00.04 µm2) among all known Si emitters in the scientific literature. To demonstrate the potential practical application, the researchers then integrated these LEDs into an in-line, centimeter-scale, silicon holographic microscope, which requires no lens or pinhole, integral to the field known as lensless holography.

A common obstacle in lensless holography is the computational reconstruction of the object being imaged. Traditional reconstruction methods require detailed knowledge of the experimental setup for accurate reconstruction and are sensitive to difficult-to-control variables such as optical aberrations, presence of noise, and twin-image problems.

The research team also developed a deep neural network architecture to improve the quality of image reconstruction. This new, untrained deep neural network incorporates regularization of the total variation to increase contrast and account for the wide spectral bandwidth of the source. Unlike traditional computational reconstruction methods which require training data, this neural network eliminates the need for training by embedding a physical model inside the algorithm.

In addition to holographic image reconstruction, the neutral network also offers recovery of the blind source spectrum from a single diffraction intensity pattern, which marks a breakthrough from all previous supervised learning techniques.

The untrained neural network demonstrated in this study allows researchers to deploy novel light sources without prior knowledge of the source spectrum or beam profile, such as the novel and smallest known Si LEDs described above, fabricated via fully commercial mass CMOS microelectronics and not modified.

The researchers envision that the synergistic combination of CMOS micro-LEDs and neural networks could be used in other computational imaging applications, such as compact microscopy for tracking living cells or spectroscopic imaging of biological tissues such as living plants. This work also demonstrates the feasibility of next-generation on-chip imaging systems.

Already, in-line holographic microscopes have been used for a variety of applications, including particle tracking, environmental monitoring, imaging of biological samples, and metrology. Further applications include constructing these LEDs in CMOS to produce programmable coherent lighting for more complex systems in the future. Illustration of the image reconstruction process using an LED holographic microscope and a neural network Illustration of the image reconstruction process using an LED holographic microscope and a neural network. (Image: SMART)

Iksung Kang, lead author of OPTICAL paper and Research Assistant at MIT at the time of this study, said, “Our breakthrough is a proof-of-concept that could have enormous impact for a wide range of applications requiring the use of micro-LEDs. For example, these LEDs can be combined into arrays for the higher levels of illumination required for large scale applications. Additionally, due to the low cost and scalability of the CMOS microelectronics process, this can be done without increasing system complexity, cost, or form factor. This allowed us to turn, with relative ease, a smartphone camera into this kind of holographic microscope. Additionally, control electronics and even an imager can be integrated onto the same chip by exploiting the electronics available in the process, thereby creating an ‘all-in-one’ micro-LED that can be transformative for the field.”

“On top of their enormous potential in lensless holography, our new LEDs have a variety of other possible applications. Due to their wavelength within the minimum absorption window of biological tissue, along with their high intensity and nanoscale emission area, our LEDs could be ideal for bio-imaging and bio-sensing applications, including close-up microscopy and implantable CMOS devices,” adds Rajeev. Ram, Principal Investigator at SMART CAMP and DiSTAP, Professor of Electrical Engineering at MIT and co-author of both papers. “Also, it may be possible to integrate these LEDs with on-chip photodetectors, and then could find further applications in on-chip communications, NIR proximity sensing, and photonics testing on wafers.”



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