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Miniaturization of diagnostics for indoor farmers

Small microscope created that allows existing mobile phone cameras to turn into high-res microscopes

Researchers from the Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP) and the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) Interdisciplinary Research Groups (IRG) of Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore have developed the world’s smallest LED (light emitting diode) that enables the conversion of existing mobile phone cameras into high-resolution microscopes.

Smaller than the wavelength of light, the new LED was used to build the world’s smallest holographic microscope, paving the way for existing cameras in everyday devices such as mobile phones to be converted into microscopes via only modifications to the silicon chip and software. This technology also represents a significant step forward in miniaturizing diagnostics for indoor farmers and sustainable agriculture, the team says.

This breakthrough was supplemented by the researchers’ development of a revolutionary neural networking algorithm that is able to reconstruct objects measured by the holographic microscope, thus enabling enhanced examination of microscopic objects such as cells and bacteria without the need for bulky conventional microscopes or additional optics. The research also paves the way for a major advancement in photonics - building a powerful on-chip emitter smaller than a micrometer, which has long been a challenge in the field.

(a) Photograph of a fully fabricated 300 mm wafer. (b) Close-up of a chip die. (c) Infrared micrograph with the LED turned on. (d) Holographic microscope setup. (e) Close-up of a reconstructed holographic image compared with the (f) ground truth.

Photo Credit: Singapore-MIT Alliance for Research and Technology (SMART)

The research team also developed a deep neural network architecture to improve the quality of image reconstruction. This novel, untrained deep neural network incorporates total variation regularisation for increased contrast and takes into account the wide spectral bandwidth of the source. Unlike traditional methods of computational reconstruction that require training data, this neural network eliminates the need for training by embedding a physics model within the algorithm. In addition to holographic image reconstruction, the neutral network also offers blind source spectrum recovery from a single diffracted intensity pattern, which marks a groundbreaking departure from all previous supervised learning techniques.

Illustration of the process of image reconstruction using the LED, holographic microscope, and neural network

Photo Credit: Singapore-MIT Alliance for Research and Technology (SMART)

“On top of its immense potential in lensless holography, our new LED has a wide range of other possible applications. Because its wavelength is within the minimum absorption window of biological tissues, together with its high intensity and nanoscale emission area, our LED could be ideal for bio-imaging and bio-sensing applications, including near-field microscopy and implantable CMOS devices,” added Rajeev Ram, Principal Investigator at SMART CAMP and DiSTAP, Professor of Electrical Engineering at MIT and co-author of both papers. “Also, it is possible to integrate this LED with on-chip photodetectors, and it could then find further applications in on-chip communication, NIR proximity sensing, and on-wafer testing of photonics.”

This research was carried out by SMART and supported by the National Research Foundation (NRF) Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program.

For more information:
Disruptive & Sustainable Technologies for Agricultural Precision
Singapore-MIT Alliance for Research and Technology 

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