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ECE PhD Dissertation Defense: Mehdi Nasrollahpourmotlaghzanjani

December 6, 2021 @ 8:00 am - 9:00 am

PhD Dissertation Defense: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems

Mehdi Nasrollahpourmotlaghzanjani

Location: Zoom Link

Abstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines, but also in a variety of applications such as neuroscience, COVID-19, etc. In this study, we are proposing an implantable device, handheld device for detecting different diseases and the RFIC design for the ME antenna and passive devices and sensor evaluations to diagnose different diseases.
First, we show a miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. The proposed rectangular ME antenna wireless power transfer efficiency is 0.304 %, which is considerably higher than that of micro-coils. Measurements results show that the maximum achievable power transfer of a ME antenna outperforms that of an on-silicon coil by approximately 7 times for a Tx-Rx distance of 0.76 cm and 3.3 times for a Tx-Rx distance of 2.16 cm.
In the second part we will go over the RFIC design for the bio-implant devices, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. A low-noise amplifier (LNA) topology with tunable input matching and noise cancellation utilized in a Bluetooth receiver frontend is introduced and described in this study, which was designed and optimized to interface with a magnetoelectric (ME) antenna in a 0.35 ┬Ám MEMS-compatible CMOS process. Input matching at the LNA-antenna interface is controlled with a circuit that varies the effective impedance of the gate inductor using a control voltage. Tunability of 455 MHz around 2.4 GHz is achieved for the optimum S11 frequency with a control voltage range of 0.3 V to 1.2 V. Besides, a miniaturized CMOS oscillator using microelectromechanical system (MEMS) resonating at 159 MHz frequency is designed and simulated to drive ME sensors. The proposed oscillator provides a phase noise as low as -131.3 dBc/Hz at 10 kHz and -137.9 dBc/Hz at 100 kHz offset frequencies while consuming 2.24 mW power.
For final part, we will discuss the handheld device design for early diagnosis of different diseases such as, lung cancer, Alzheimer, Covid-19, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors. A novel gas sensor has been developed that might be applied to diagnose Covid-19 from the exhaled breath instantly. The handheld device is designed to read the sensor activities and send the data to the android phone to show if the patient is at risk or not. For this purpose, a lock-in amplifier is designed to read the resistance in ac domain and transmit the digitized data through Bluetooth communication link.


December 6, 2021
8:00 am - 9:00 am


Electrical and Computer Engineering
MS/PhD Thesis Defense