[month] [year]

Adithya S E –  Portable Sensing Applications

Adithya Sunil Edakkadan, supervised by Dr. Abhishek Srivastava received his Master of Science – Dual Degree in Computer Science and Engineering (CSE). Here’s a summary of his research work on Design Techniques and

Architectural Solutions for RF Circuits and Systems for Portable Sensing Applications:

Advancements in electronics have revolutionized technology by making devices more portable and powerful. Integrated circuit technology has allowed for packing more functionality into compact spaces, leading to the development of portable gadgets that offer convenience and advanced features. Industries like healthcare, aerospace, and automotive have also benefited from miniaturized electronics, enabling remote patient monitoring, enhancing aircraft performance, and advancing smart vehicle technology. Additionally, miniaturization has improved energy efficiency, resulting in wireless and battery-powered devices for remote and off-grid applications. In this thesis, various circuit design techniques have been presented and prototype systems implemented for portable sensing applications.

Magnetometers form a key component of sensing systems used in fields such as geophysical and oceanic exploration and aerospace. Recently, diamond colour defect-based quantum sensing applications such as nitrogen-vacancy (NV) centre magnetometry have emerged in CMOS technology, which uses optically detected magnetic resonance (ODMR) for sensing magnetic field strengths from different environmental physical quantities. For ODMR based sensing, CMOS quantum sensors seek an on-chip 2.87 GHz microwave (MW) signal generator. Moreover, in order to sense smaller magnetic field strengths, these CMOS quantum sensors also require that MW signal should be swept with a sufficiently small step size near 2.87 GHz. It is also required that the PLLs should have low noise and low jitter for high stability and fast settling time. These requirements seek a low phase noise voltage controlled oscillator (VCO) with a small variation in its gain (KVCO) within the desired tuning range. In this thesis, a fractional-N synthesiser based 2.87 GHz MWgenerator (MWG) is presented with an extremely small programmable sweep step size for improved sensitivity of magnetic field strength measurements in CMOS NV magnetometry along with a technique for designing a low-phase noise VCO with lowKVCO and small KVCO variation is also presented.

Recent developments in advanced driver assistance systems (ADAS) used in the automotive industry have raised the demands of mmWave radars in 24 GHz and 77 GHz bands. For higher accuracy and precision, frequency modulated continuous wave (FMCW) technique has become very popular for mmWave radars, which requires low phase noise and high bandwidth chirp frequency synthesisers. Such high-frequency band radars require programmable dividers with large divide ratios and fine frequency resolution to obtain high-frequency chirps with sufficiently large bandwidth. In this thesis, the implementation of a low phase noise, transformer tank based mmWave voltage controlled oscillator (VCO) near 20 GHz for multiplier based 77 GHz FMCW chirp synthesiser is presented along with a low-power multi-modulus programmable frequency divider for a frequency synthesiser operating in the 19.25-20.25 GHz frequency band from a reference frequency of 40 MHz. The applications of such radars in road safety and driver assistance is further explored by presenting a novel proof-of-concept that can efficiently classify targets in real-time under multiple classes. Hardware realisation, using a prototype Frequency Modulated Continuous Wave (FMCW) radar system, of the same has also been demonstrated. 

Respiratory diseases contribute to a majority of deaths worldwide every year. Diseases such as asthma, bronchitis and pneumonia also adversely impact a person’s social and economic conditions. They can seriously threaten their health if left undiagnosed and untreated. Techniques such as auscultation are used in the diagnosis of most respiratory diseases. However, using such techniques requires an experienced physician and the diagnosis is subjective. To overcome these challenges, in this thesis, a portable handheld system has also been proposed and a proof of concept implemented to detect and classify respiratory diseases automatically through the use of convolutional neural networks (CNN) running on mobile platforms.


January 2024