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Silicon photonic microring resonators have emerged as a sensitive and highly multiplexed platform for real-time biomolecule detection. Herein, we profile the evanescent decay of device sensitivity towards molecular binding as a function of distance from the microring surface. By growing multilayers of electrostatically bound polymers extending from the sensor surface, we are able to empirically determine that the evanescent field intensity is characterized by a 1/e response decay distance of 63 nm. We then applied this knowledge to study the growth of biomolecular assemblies consisting of alternating layers of biotinylated antibody and streptavidin, which follow a more complex growth pattern. Additionally, by monitoring the shift in microring resonance wavelength upon the deposition of a radioactively labeled protein, the mass sensitivity of the ring resonator platform was determined to be 14.7 ± 6.7 [pg/mm 2 ]/Δpm. By extrapolating to the instrument noise baseline, the absolute mass limit of detection is found to be 1.5 ± 0.7 pg/mm 2 . Taking the small surface area of the microring sensor into consideration, this value corresponds to an absolute mass detection limit of 97 ag (i.e. 0.6 zmol of IgG), demonstrating the remarkable sensitivity of this promising label-free biomolecular sensing platform.

Photonic ring resonance is a property of light where in certain circumstances specific wavelengths are trapped in a ring resonator. Sensors based on silicon photonic ring resonators function by detecting the interaction between light circulating inside the sensor and matter deposited on the sensor surface. Binding of biological material results in a localized change in refractive index on the sensor surface, which affects the circulating optical field extending beyond the sensor boundary. That is, the resonant wavelength will change when the refractive index of the medium around the ring resonator changes. Ring resonators can be fabricated onto small silicon chips, allowing development of a miniature multiplex array of ring based biosensors. This paper describes the properties of such a system when responding to the refractive index changed in a simple and precise way by changing the ionic strength of the surrounding media, and in a more useful way by the binding of macromolecules to the surface above the resonators. Specifically, a capture immunoassay is described that measures the change of resonant wavelength as a patient serum sample with anti-SS-A autoantibodies is flowed over a chip spotted with SS-A antigen and amplified with anti-IgG. The technology has been miniaturized and etched into a 4×6mm silicon chip that can measure 32 different reactions in quadruplicate simultaneously. The variability between 128 rings on a chip as measured by 2M salt assays averaged 0.6% CV. The output of the assays is the average shift per cluster of 4 rings, and the assays averaged 0.5% CV between clusters. The variability between chips averaged 1.8%. Running the same array on multiple instruments showed that after some improvements to the wavelength referencing system, the upper boundary of variation was 3% between 13 different instruments. The immunoassay displayed about 2% higher variability than the salt assays. There are several outstanding features of this system. The amount of antigen used on the chip for each test is around 200 picograms, only a few microliters of sample is necessary, and the assays take <10min.

Silicon photonic technology has incredible potential to transform multiplexed bioanalysis on account of the scalability of device fabrication, which maps favorably to a myriad of medical diagnostic applications. The optical properties of CMOS-fabricated microring resonators are incredibly responsive to changes in the local dielectric environment accompanying a biological binding event near the ring surface. Arrays of high-Q microrings were designed to be individually addressable both in surface derivitization, using well-established microarraying technologies, and in optical evaluation. The optical response of each ring can be determined in near real time allowing multiple biomolecular interactions to be simultaneously monitored. We describe a stable and robust measurement platform that allows sensitive visualization of small molecule surface chemical derivitization as well as monitoring of biological interactions, including the detection of proteins and nucleic acids. We also present recent results demonstrating multiplexed measurement of cancer markers. These demonstrations establish a pathway to higher level multiparameter analysis from real-world patient samples; a development that will enable individualized disease diagnostics and personalized medicine.

The growing use of nonlinear devices is introducing harmonics in the power system networks that results in distortion of current and voltage signals causing damage to the power distribution system. Therefore, in power systems, the elimination of harmonics is of great concern. This paper presents an efficient techno-economical approach to suppress harmonics and improve the power factor in the power distribution network using neural network algorithms-based Shunt Hybrid Active Power Filter (SHAPF), such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN). The objective of the proposed algorithms for SHAPF is to reduce Total Harmonic Distortion (THD) within an acceptable range to improve system quality. In our filter design approach, we tested and compared conventional pq0 theory and neural networks to detect the harmonics present in the power system. Moreover, for the regulation of the DC supply to the inverter of the SHAPF, the conventional PI controller and neural networks-based controllers are used and compared. The applicability of the proposed filter is tested for three different nonlinear load cases. The simulation results show that the neural networks-based filter control techniques satisfy all international standards with minimum current THD, neutral wire current elimination, and small DC voltage fluctuations for voltage regulation current. Furthermore, all three neural network architectures are tested and compared based on accuracy and computational complexity, with RNN outperforming the rest.