CMOS-integrated biosensor circuits and systems : FMCW radar sensor for noncontact multimodal vital signs monitoring and coherent photoacoustic sensor for non-invasive in vivo sensing and imaging

• Main Author: Fang, Zhongyuan
• Other Authors: Zheng Yuanjin
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/150277
• Institution: Nanyang Technological University

For pervasive healthcare monitoring and potential disease diagnosis, biosensors that can be deployed massively with compact size, low power consumption, and efficient and effective near-sensor processing capabilities are required. Synergetic optimizations on multi-physical sensing mechanisms, circuits, and efficient signal processing algorithms are needed to enable efficient and effective operation. In intricate and noisy scenarios, current sensor devices are usually limited by the detecting accuracy, low specificity, and large power consumption. Moreover, current sensors realized by discrete blocks are usually with bulky size, which is inconvenient for the massive deployment for pervasive healthcare monitoring applications. With the rapid development of CMOS technologies, biosensors are on the way to be monolithically integrated into a single chip to enable the highly efficient data acquisition and effective signal processing with low-power consumption and small size. The sensor data is acquired and processed at the edge near-sensor; thus, the data movement's power consumption is eliminated. Moreover, multi-physical sensing mechanisms can be utilized to enhance the biosensor’s capability, achieving multimodal sensor fusion by leveraging the advantages of complementary physical mechanisms, demonstrating the enormous potential to be used for the global COVID-19 pandemic. The CMOS-integrated radar sensor is a suitable candidate to monitor multimodal vital signs and detect specific movements like falling in a noncontact way utilizing electromagnetic (EM) waves. To further enable multimodal biomedical sensing for pervasive healthcare applications, considering that photoacoustic sensor is realized based on light excitation and acoustic sensing, which can be used to achieve the non-invasive monitoring on the in-depth blood core temperature and in vivo imaging on the target tissues. In the thesis, a CMOS-integrated phased-array radar sensor prototype fabricated in the GlobalFoundries 65-nm CMOS process for wide field-of-view (FoV) multimodal vital sign monitoring and falling detection is presented. The co-design of integrated circuit blocks and effective signal processing algorithms is implemented to enable the accuracy, low power consumption, and reliability of the radar sensor for noncontact health applications. Furthermore, to enable in-depth blood core temperature monitoring and in vivo imaging, a high-precision CMOS-integrated mixed-signal coherent lock-in photoacoustic sensor prototype is fabricated in GlobalFoundries 65-nm CMOS process, achieving potential disease diagnosis and health monitoring with compact size. Moreover, a four-channel photoacoustic system-on-chip (SoC) was fabricated in TSMC 65-nm process, which includes the analog front end (AFE), analog-to-digital converter (ADC), digital-to-analog converter (DAC), and the digital processing module implementing coherent lock-in and accurate beamforming on-chip to achieve accurate detection on in-depth target photoacoustic signals with an enhanced signal to noise ratio (SNR), improved sensitivity, and high specificity for the first time. By the optimizations on the mixed-signal integrated circuits (ICs) design, energy-efficient sensor signal processing algorithms, and the multi-physical sensing mechanisms including EM, acoustics and optical techniques, the CMOS-based radar sensor, and photoacoustic sensor prototypes have verified their capabilities on accurate multimodal noncontact vital signs monitoring, falling detection, non-invasive temperature monitoring, and in-depth in vivo vessel imaging. The thesis explores the CMOS-integrated mixed-signal circuits and systems leveraging multi-modal sensing techniques for efficient and effective versatile sensing, pervasive healthcare monitoring, and potential disease diagnosis for the current COVID-19 pandemic as well as for the incoming intelligent Internet of Everything (IoE) era.