Bal, Ayşe Rana2024-12-272024-12-272024-122024-122024-12-24https://hdl.handle.net/11693/115939Cataloged from PDF version of article.Includes bibliographical references (leaves 81-85).Low Noise Amplifiers (LNAs) are crucial components in magnetic resonance imaging (MRI) receivers, primarily designed to amplify weak signals while minimizing the added noise. They have a special requirement of having an input impedance very close to a short-circuit. This requirement assures that there is no current in the MRI coil when the LNA is connected to the coil with a quarter-wave transmission line. By eliminating the current in the coil, the risk of coupling between coils also known as mutual coupling, within an array is reduced. This thesis explores designs of sub-1dB noise figure CMOS LNAs operating at 437 MHz and 477 MHz for 10.3 T and 10.5 T MRI systems, respectively, using variations of cascode amplifier structures while addressing the challenges and advancements in CMOS RF design. The first LNA designed for a singular 10.3 T MRI coil system demonstrates a power gain of 20.54 dB, a noise figure of 0.97 dB, an input return loss of −11.70 dB, and an IIP3 of −12.26 dBm with its fully modeled design. For a 10.5 T Array MRI system, one single-ended and one differential-ended LNA is designed. The single-ended demonstrates a power gain of 18.42 dB, a noise figure of 0.42 dB, an input return loss of −0.90 dB, an output return loss of −18.03 dB, and an IIP3 of −17.12 dBm, whereas the differential-ended demonstrates a power gain of 14.76 dB, a noise figure of 0.60 dB, an input return loss of −0.94 dB, an output return loss of −18.73 dB, and an IIP3 of −14.36 dBm both with their fully modeled designs. The differential amplifier also displays a significant improvement in PSRR, making it a good choice for array MRI applications.xvi, 85 leaves : illustrationscharts, charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessLow Noise Amplifiers (LNAs)CMOS technologyMagnetic resonance imagingNoise figureCascode amplifierNoise optimization methodThesisB159412