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Personne# Paul Stanislas Gosselin

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Photosensing has proven to be highly useful in biomedical research and medical care. Being able to detect and quantify the light emitted by fluorescent or bioluminescent reporters has significantly participated in the development of new cancer treatments and made possible the development of cost-effective diagnosis techniques. While these advances relied on the large CCD devices or on laboratory-on-chips, the design of a bioluminescence-based implant for the continuous monitoring of biological data can be considered and would allow for the acquisition of insightful information in both research and care.
To exist, such an implant requires a suited photosensing circuit, the design of which can be challenging due to the weakness of the target bioluminescent signal. This thesis addresses the design and evaluation of an appropriate analog-to-digital converter (ADC), with a focus on its expected accuracy. In particular, three integration-base current-to-frequency converters are considered: the current-controlled oscillator, the first-order Sigma-Delta converter with instantaneous feedback, the first-order Sigma-Delta converter with continuous feedback.
Theoretical formulas are established to assess how the input-referred noise would impact the results from such ADCs, depending on the integration time as well as the time passed since last calibration. In addition to the general case, specific formulas are provided for white noise, flicker noise, and Lorentzian-shaped noise. Moreover, in practical cases that are conceivable with target application, these formulas cannot be used as they are due to the finite resolution of computing softwares. Consequently, the related implementation issues are addressed and solved.
To guide the design of the current-to-frequency converters, how each of their imperfections affects their accuracy is analyzed in depth. This analysis and the obtained formulas serve a top-level-focused design strategy that is argued for in this thesis. It relies on maintaining a detailed top-level description of the system under study to help focus the designing efforts on the most impactful areas as well as guide the design choices at the transistor level.
Applying such a strategy is made possible by the development of a MATLAB tool providing a flexible graphic user interface to explore the impact of each parameter of the design. In addition, to further assist the design of the ADC and/or of the photodetector, a Python tool is presented to explore configurations for successive simulations and data analyses, even when using multiple softwares.
As an opening chapter, emphasizing the importance of considering the top-level in design, the case of heart rate monitoring through photoplethysmography is addressed. In this case, the highest level of the system includes the data processing, and only the latter can actually allow for drastic improvements of the power consumption by alleviating the requirements on the data acquisition. In fact, it is demonstrated that in many situations, the sampling frequency for the acquisition of the plethysmograph could be brought down to 10 to 25 Hz, while still ensuring a satisfying accuracy for the heart rate and heart rate variation monitoring.

Paul Stanislas Gosselin, Maher Kayal, Adil Koukab

From first-order incremental Sigma Delta converters to controlled-oscillator-based converters, many ADC architectures are based on the continuous-time integration of the input signal. However, the accuracy of such converters cannot be properly estimated without establishing the impact of noise. In fact, noise is also integrated, resulting in a random error that is added to the measured value. Since drifting phenomena may make simulations and practical measurements unable to ensure long-term reliability of the converters, a theoretical tool is required. This paper presents a solution to compute the standard deviation of the noise-generated error in continuous-time integrator-based ADCs, under the assumption that a previous measure is used to calibrate the system. In addition to produce a realistic case, this assumption allows to handle a theoretical issue that made the problem not properly solvable. The theory is developed, the equations are solved in the cases of pure white noise and pure flicker noise, and the implementation issues implied by the provided formula are addressed.