Compact pixel architecture for CMOS lateral flow immunoassay readout systems

A novel pixel architecture for CMOS image sensors is presented. It uses only one amplifier for both integration of the photocurrent and in-pixel noise cancelation, thus minimizing power consumption. The circuit is specifically designed to be used in readout systems for lateral flow immunoassays. In addition a switching technique is introduced enabling the use of column correlated double sampling technique in capacitive transimpedance amplifier pixel architectures without the use of any memory cells. As a result the reset noise which is crucial in these architectures can be suppressed. The circuit has been designed in a 0.35-μm CMOS technology and simulations are presented to show its performance

New Architectures and Circuits for Pushing the Dynamic Range and Multiplexing Boundaries of CMOS-Integrated Sensors

Over the last decades, CMOS-integrated sensors have made impressive progress in performance, form-factor, and energy-efficiency for various applications such as imaging, physical/chemical sensing, bio/health monitoring. In the era of the artificial intelligence (AI) and the internet-of-things (IoT), such CMOS-integrated sensors are essential for massive and comprehensive data acquisition, where sensing range (or dynamic range), signal fidelity (or signal-to-noise ratio), and data throughput are key factors. Towards pushing the boundaries of such sensing capabilities, in this dissertation, novel sensing architectures are presented with energy/area-efficient circuit design techniques for multi-channel CMOS optical sensors and neural interfaces. The first topic is a fully-integrated, wide linear dynamic range optical sensor array combining linear and single-photon avalanche diode operation within each pixel. A pulse-counting readout scheme provides in-pixel digitization in an area-efficient manner for both operation modes, enabling fully parallel measurement across the array. The proposed dual-mode optical sensor array alternately requires high-voltage(10-20 V) and low-voltage supply (2-5 V) for reverse bias of the photodiodes, which is provided by a reconfigurable, closed-loop high-voltage charge pump in the same substrate. An 8 x 8 array architecture along with the dual-mode bias generator is fabricated in a general purpose 180 nm CMOS process and demonstrates 129 dB dynamic range while maintaining linear photoresponse operating with a dual-mode frame rate of 20 Hz. The second topic is a new approach for applying code-division multiplexing (CDM) to current-mode and voltage-mode sensor arrays with analog-domain orthogonal encoding directly in a shared, single analog-front-end circuit, which enables simultaneous readout for multiple sensors. The approach is applied to a 8 x 16 array of CMOS-integrated photodetectors and implemented in a general purpose 180 nm CMOS process, where the 16 channel CDM-based oversampling readout achieves an SNR improvement of more than 12 dB compared with time-division multiplexing at the same sampling rate. In addition, a CDM-based neural recording architecture is presented, which offers a significant tolerance to interference that can be injected through long cables

Circuits and Systems for Lateral Flow Immunoassay Biosensors at the Point-of-Care

Lateral Flow Immunoassays (LFIAs) are biosensors, which among others are used for the detection of infectious diseases. Due to their numerous advantages, they are particularly suitable for point of care testing, especially in developing countries where there is lack of medical healthcare centers and trained personnel. When the testing sample is positive, the LFIAs generate a color test line to indicate the presence of analyte. The intensity of the test line relates to the concentration of analyte. Even though the color test line can be visually observed for the accurate quantification of the results in LFIAs an external electronic reader is required. Existing readers are not fully optimized for point-of-care (POC) testing and therefore have significant limitations. This thesis presents the development of three readout systems that quantify the results of LFIAs. The first system was implemented as a proof of concept of the proposed method, which is based on the scanning approach without using any moving components or any extra optical accessories. Instead, the test line and the area around it, are scanned using an array of photodiodes (1 × 128). The small size of the pixels gives the system sufficient spatial resolution, to avoid errors due to positioning displacement of the strip. The system was tested with influenza A nucleoprotein and the results demonstrate its quantification capabilities. The second generation system is an optimized version of the proof of concept system. Optimization was performed in terms of matching the photodetectors wavelength with the maximum absorption wavelength of the gold nanoparticles presented in the tested LFIA. Ray trace simulations defined the optimum position of all the components in order to achieve uniform light distribution across the LFIA with the minimum number of light sources. An experimental model of the optical profile of the surface of LFIA was also generated for accurate simulations. Tests of the developed system with LFIAs showed its ability to quantify the results while having reduced power consumption and better limit of detection compared to the first system. Finally, a third generation system was realized which demonstrated the capability of having a miniaturized reader. The photodetector of the previous systems was replaced with a CMOS Image Sensor (CIS), specifically designed for this application. The pixel design was optimized for very low power consumption via biasing the transistors in subthreshold and by reusing the same amplifier for both photocurrent to voltage conversion and noise cancellation. With uniform light distribution at 525 nm and 76 frames/s the chip has 1.9 mVrms total output referred noise and a total power consumption of 21 μW. In tests with lateral flow immunoassay, this system detected concentrations of influenza A nucleoprotein from 0.5 ng/mL to 200 ng/mL

Image processing on reconfigurable hardware for continuous monitoring of fluorescent biomarkers in cell cultures

Fluorescence microscopy is a widespread tool in biological research. It is the primary modality for bioimaging and empowers the study and analysis of multitudes of biological processes. It can be applied to fixed biosamples, that is samples with frozen biological features by mean of chemical linkers, or live biosamples providing useful insights on the spatio-temporal behavior of fluorescently stained biomarkers. Current fluorescent microscopy techniques use digital image sensors which are used to leverage quantitative studies instead qualitative outcomes. However, state-of-the-art techniques are not suitable for integration in small, contained and (semi-)autonomous systems. They remain costly, bulky and rather quantitatively inefficient methods for monitoring fluorescent biomarkers, which is not on par with the design constraints found in modern Lab-on-a-Chip or Point-of-Use systems requiring the use of miniaturized and integrated fluroscence microscopy. In this thesis, I summarize my research and engineering efforts in bringing an embedded image processing system capable of monitoring fluorescent biomarkers in cell cultures in a continuous and real-time manner. Three main areas related to the problem at hand were explored in the course of this work: simulation, segmentation algorithms and embedded image processing. n the area of simulation, a novel approach for generating synthetic fluorescent 2D images of cell cultures is presented. This approach is dichotomized in a first part focusing on the modeling and generation of synthetic populations of cells (i.e. cell cultures) at the level of single fluorescent biomarkers and in a second part simulating the imaging process occurring in a traditional digital fluorescent microscope to produce realistic images of the synthetic cell cultures. The objective of the proposed approach aims at providing synthetic data at will in order to test and validate image processing systems and algorithms. Various image segmentation algorithms are considered and compared for the purpose of segmenting fluorescent spots in microscopic images. The study presented in this thesis includes a novel image thresholding technique for spot extraction along with three well-known spot segmentation techniques. The comparison is undertaken on two aspects. The segmentation masks provided by the methods are used to extract further metrics related to the fluorescent signals in order to (i) evaluate how well the segmentation masks can provide data for classifying real fluorescent biological samples from negative control samples and (ii) quantitatively compare the segmentations masks based on simulated data from the previously stated simulation tool. Finally, the design of an embedded image processing system based on FPGA technologies is showcased. A semi-autonomous smart camera is conceived for the continuous monitoring of fluorescent biomarkers based on one of the segmentation methods incorporated in the previously stated comparison. Keeping the focus on the need for integration in fluorescence microscopy, the image processing core at the heart of the smart camera results from the use of a novel image processing suite; a suite of IP cores developed under the constraints dictated by the bioimaging needs of fluorescence microscopy for use in FPGA and SoC technologies. As a proof of concept, the smart camera is applied to the monitoring of the kinetics of the uptake of fluorescent silica nano-particles in cell cultures