Silicon Photonic Device for Wavelength Sensing and Monitoring

Over the last decade advances and innovations from Silicon Photonics technology were observed in the telecommunications and computing industries. This technology which employs Silicon as an optical medium, relies on current CMOS micro-electronics fabrication processes to enable medium scale integration of many nano-photonic devices to produce photonic integrated circuitry. However, other fields of research such as optical sensor processing can benefit from silicon photonics technology, specially in sensors where the physical measurement is wavelength encoded. In this research work, we present a design and application of a thermally tuned silicon photonic device as an optical sensor interrogator. The main device is a micro-ring resonator filter of 10 $\mu m$ of diameter. A photonic design toolkit was developed based on open source software from the research community. With those tools it was possible to estimate the resonance and spectral characteristics of the filter. From the obtained design parameters, a 7.8 x 3.8 mm optical chip was fabricated using standard micro-photonics techniques. In order to tune a ring resonance, Nichrome micro-heaters were fabricated on top of the device. Some fabricated devices were systematically characterized and their tuning response were determined. From measurements, a ring resonator with a free-spectral-range of 18.4 nm and with a bandwidth of 0.14 nm was obtained. Using just 5 mA it was possible to tune the device resonance up to 3 nm. In order to apply our device as a sensor interrogator in this research, a model of wavelength estimation using time interval between peaks measurement technique was developed and simulations were carried out to assess its performance. To test the technique, an experiment using a Fiber Bragg grating optical sensor was set, and estimations of the wavelength shift of this sensor due to axial strains yield an error within 22 pm compared to measurements from spectrum analyzer. Results from this study implies that signals from FBG sensors can be processed with good accuracy using a micro-ring device with the advantage of ts compact size, scalability and versatility. Additionally, the system also has additional applications such as processing optical wavelength shifts from integrated photonic sensors and to be able to track resonances from laser sources.

Seasonal dynamics and operational monitoring of hedgerow olive tree transpiration in response to applied water

We used 2012 sap flow measurements to assess the seasonal dynamics of daily plant transpiration (ETc) in a high-density olive orchard (Olea europaea L. cv. ‘Arbequina’) with a well-watered (HI) control treatment A to supply 100 % of the crop water needs, and a moderately (MI) watered treatment B that replaced 70% of crop needs. To assure that treatment A was well-watered, we compared field daily ETc values against ETc obtained with the Penman-Monteith (PM) combination equation incorporating the Orgaz et al. (2007) bulk daily canopy conductance (gc) model, validated for our non-limiting conditions. We then tested the hypothesis of indirectly monitoring olive ETc from readily available vegetation index (VI) and ground-based plant water stress indicator. In the process we used the FAO56 dual crop coefficient (Kc) approach. For the HI olive trees we defined Kcb as the basal transpiration coefficient, and we related Kcb to remotely sensed Soil Adjusted Vegetation Index (SAVI) through a Kcb-SAVI functional relationship. For the MI treatment, we defined the actual transpiration ETc as the product of Kcb and the stress reduction coefficient Ks obtained as the ratio of actual to crop ETc, and we correlated Ks with MI midday stem water potential (ψst) values through a Ks-ψ functional relationship. Operational monitoring of ETc was then implemented with the ETc = Kcb(SAVI)Ks(ψ)ETo relationship stemmed from the FAO56 approach and validated taking as inputs collected SAVI and ψst data reporting to year 2011. Low validation error (6%) and high goodness-of-fit of prediction were observed (R2 = 0.94, RSME = 0.2 mm day-1, P = 0.0015), allowing to consider that under field conditions it is possible to predict ETc values for our hedgerow olive orchards if SAVI and water potential (ψst) values are known.