Demonstrating low Raman background in UV-written SiO2 waveguides

Raman spectroscopy can give a chemical ’fingerprint’ from both inorganic and organic samples, and has become a viable method of measuring the chemical composition of single biological particles. In parallel, integration of waveguides and microfluidics allows for the creation of miniaturized optical sensors in lab-on-a-chip devices. The prospect of combining integrated optics and Raman spectroscopy for Raman-on-chip offers new opportunities for optical sensing. A major limitation for this is the Raman background of the waveguide. This background is very low for optical fibers but remains a challenge for planar waveguides. In this work, we demonstrate that UV-written SiO2 waveguides, designed to mimic the performance of optical fibers, offer a significantly lower background than competing waveguide materials such as Si3N4. The Raman scattering in the waveguides is measured in absolute units and compared to that of optical fibers and Si3N4 waveguides. A limited study of the sensitivity of the Raman scattering to changes in pump wavelength and in waveguide design is also conducted. It is revealed that UV-written SiO2 waveguides offer a Raman background lower than −107.4 dB relative to a 785 nm pump and −106.5 dB relative to a 660 nm pump. Furthermore, the UV-written SiO2 waveguide demonstrates a 15 dB lower Raman background than a Si3N4 waveguide and is only 8.7 − 10.3 dB higher than optical fibers. Comparison with a polystyrene bead (in free space, diameter 7 µm) reveal an achievable peak SNR of 10.4 dB, showing the potential of UV-SiO2 as a platform for a Raman-on-chip device capable of measuring single particles

Raman-spectroscopy of extracellular vesicles and self-supervised deep learning

This thesis explores the prospect of a waveguide device for optical trapping and Raman spectroscopy of single biological nanoparticles. The aim of the work is to develop a Raman-on-chip device capable of training and measuring single particles in individual trapping sites, such that the throughput of the device can be increased through parallelization. The challenge posed by the induced Raman scattering in the waveguide device, producing a background in the measured Raman spectra of the nanoparticles, is investigated. It is found that the candidate UV-written silica waveguides produce a Raman spectrum lower than -107.4 dB in a 1 cm waveguide, which is 15 dB lower than silicon nitride. The background is found to exhibit no prominent features in the biological fingerprint region (800-1700 cm-1). Machine learning is explored for mitigating the significance of the background. A general platform for a machine learning model for this purpose is developed, first as a convolutional neural network is developed for analysis of tomographic scans of silicon boules. The developed neural network demonstrates an accuracy of 98.7% and robustness to a noise increase of up to 18 dB. The convolutional neural network is further expanded into a convolutional autoencoder for Raman spectra. The autoencoder model is based on the convolutional neural network is shown to be able to recover Raman spectra of extracellular vesicles with high fidelity, even in the presence of stochastic noise and an emulated waveguide background, recovering the spectra from a signal-to-noise ratio of -18±3 dB to 5.4 dB. The model is also shown to be able to differentiate the extracellular vesicles by their biological origins through self-supervised learning. Further developments of the model are demonstrated to enable it to differentiate 13 different origins, allowing a classifier to classify the nanoparticles to their known origins with an accuracy of 92.2%.Denne avhandlingen utforsker bruken av en innretning basert på bølgeledere til å optisk fange og fasilitere Raman-spektroskopi av biologiske nanopartikler. Hensikten med dette verket er å utvikle en metode som benytter integrert optikk til å fange enkeltpartikler ved flere punkter samtidig slik at hastigheten til målemetoden kan økes gjennom å ta måling fra flere partikler i parallell. Utfordringer med en slik innretning utforskes, med fokus på Raman-spredningen som oppstår i bølgelederen og hvordan denne tilfører et bakgrunn til de målte Raman-spektrene av biologiske nanopartikler. Det er vist at UV-skrevne bølgeledere av SiO2 produserer et Raman-spekter med en styrke lavere enn -107.4 dB, noe som er 15 dB lavere enn silisiumnitrid. Det er også vist at Raman-spekteret til bølgelederne ikke inneholder sterke elementer, noe som gjør det relativt flatt i interesseområdet for biologiske partikler (800-1700cm-1). Maskinlæring utforskes som en metode for å skille Raman-spektrene fra ekstracellulære vesikler fra Raman-spekteret til bølgelederne. En generell plattform for en slik modell utvikles som et neuralt nettverk med hensikten å estimere kvaliteten til krystallinsk silisium gjennom infrarød tomografi. Denne metoden er vist å oppnå en nøyaktighet på 98.7% og demonstrerer høy motstandsdyktighet ved tilførsel av opp til 18 dB stokastisk støy. Det neurale nettverket er videreutviklet til en autoenkoder for behandling av Raman-spektra som er i stand til selv-veiledet læring. Denne modellen er vist i stand til å gjenskape Raman-spektrene i høy detalj, selv i tilfeller med stokastisk støy og bakgrunn fra en bølgeleder, og kan gjenskape spektrene fra ett signal-til-støyforhold på -18±3 dB til 5.4 dB. Modellen er også vist å være i stand til differensiering av biologiske nanopartikler ut fra deres biologiske opprinnelse. Videre utvikling av modellen har vist at den muliggjør differensiering og klassifisering av 13 typer biologiske nanopartikler med en nøyaktighet på 92.2%

Evaluation of crystalline structure quality of Czochralski-silicon using near-infrared tomography

In this work, three silicon samples are subject to tomographic scans using a 1.6μm laser. The samples were prematurely terminated due to anomalies during the Czhochralski-process. They are taken as analogues of the in situ crystal, where one sample has known aberrant structure in its lowermost 45 mm. The results of the tomographic scans show a distinct difference in transmission profile between the material of known poor monocrystalline structure and assumed good structure. Three different analysis tools are constructed and applied to quantify the quality of the structure from the results of the tomographic scans. The first two analysis tools are applied as correlation filters constructed from patterns resembling the indicative transmission profiles of highquality structure, one pattern being an ideal square wave and the other being experimentally determined from the measurements. Both correlation filters yield clear differentiation of low- vs. high-quality material. The final analysis tool is a deep convolutional neural network (deep CNN) evolved from a predetermined architecture configuration using a genetic algorithm. The trained CNN is shown to differentiate the usable high-quality material from the unusable material with a 98.7% accuracy on a testing set of 76 profiles and successfully assigns quality factors to the material that are in good agreement with the correlation filters and previous observations