Optical based noninvasive glucose monitoring sensor prototype

Diabetes mellitus claims millions of lives every year. It affects the body in various ways by leading to many serious illnesses and premature mortality. Heart and kidney diseases, which are caused by diabetes, are increasing at an alarming rate. In this paper, we report a study of a noninvasive measurement technique to determine the glucose levels in the human body. Current existing methods to quantify the glucose level in the blood are predominantly invasive that involve taking the blood samples using finger pricking. In this paper, we report a spectroscopy-based noninvasive glucose monitoring system to measure glucose concentration. Near-infrared transmission spectroscopy is used and in vitro experiments are conducted, as well as in vivo. Our experimental study confirms a correlation between the sensor output voltage and glucose concentration levels. We report a low-cost prototype of spectroscopy-based noninvasive glucose monitoring system that demonstrates promising results in vitro and establishes a relationship between the optical signals and the changing levels of blood–glucose concentration

Efficient layout-aware statistical analysis for photonic integrated circuits

Fabrication variability significantly impacts the performance of photonic integrated circuits (PICS), which makes it crucial to quantify the impact of fabrication variations at the design and simulation stage. The variability analysis enables circuit and system designers to optimize their designs to be more robust and obtain maximum yield when designing for manufacturing. The variability analysis requires a total of six parameters to model spatially correlated manufacturing variations in photonic circuits: mean, standard deviation, and correlation length for both width and thickness variations of photonic components. The correlation lengths are spatial parameters that describe how the width and thickness variations are distributed along a chip’s or a wafer’s surface. The methods that allow for the non-invasive characterization of variations are limited to extracting mean and standard deviations of width and thickness variations. In this thesis, we present a method to extract the physical correlation lengths, which are crucial to model manufacturing variations. In this thesis, we also present the Reduced Spatial Correlation Matrix based Monte Carlo (RSCM-MC), a methodology to study the impact of spatially correlated manufacturing variations on the performance of photonic circuits. The presented methodology is compared with another layout-dependent Monte Carlo (MC) simulation methodology, called Virtual Wafer-based Monte Carlo (VW-MC). First, we describe the process of generating spatially correlated physical variations using the presented methodology and use the generated correlated physical variations to conduct MC simulations. We then use a Mach-Zehnder lattice filter photonic circuit as a benchmark circuit to study the accuracy of the proposed method. We compare the statistical parameters of quantities defining the flatness of the transmission spectra of the filter. We then compare the computation performance of RSCM-MC with VW-MC using a combination of a small-sized circuit (two-stage Mach-Zehnder filter) and a large circuit (a 16x16 ring matrix) with thousands of components. For the best case, i.e. the small-sized circuit, we observe a decrease in computational times by 98.9% and a reduction in memory requirement by 72%. For the worst case, i.e. the 16x16 ring matrix, we observe a decrease in computational times by 99.8% and a reduction in memory requirement by 87%.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

High-performance, intelligent, on-chip speckle spectrometer using two-dimensional silicon photonic disordered microring lattice

High-performance integrated spectrometers are highly desirable for many applications ranging from mobile phones to space probes. Based on silicon photonic integrated circuit technology, we propose and demonstrate an on-chip speckle spectrometer consisting of a 15 ×15, two-dimensional (2D), disordered microring lattice. The proposed 2D, disordered microring lattice is simulated by the transfer-matrix method. The fabricated device features a spectral resolution better than 15 pm and an operating bandwidth larger than 40 nm. We also demonstrate that, based on the speckle patterns, our device can perform a spectrum classification using machine learning algorithms, which will have a huge potential in fast, intelligent material and chemical analysis