Mechanical and Civil Engineering Seminar
Hybrid PhD Thesis Defense
Abstract
Detection and processing of microwave signals is of substantial scientific importance in fields ranging from radio astronomy to quantum computing. An essential component of the signal processing chain is the microwave amplifier, which adds gain to the signal so that it may be processed by subsequent microwave components. However, the amplifier itself adds its own internally generated noise into the measurement chain. As a result, amplifiers which add a minimal amount of noise are crucial to any high precision measurement scheme. A device which is commonly employed for this task is the high electron mobility transistor (HEMT) amplifier. Understanding the fundamental limits of the microwave noise performance of HEMT amplifiers is highly desirable. Noise temperatures in these devices as low as 3 times the quantum limit have been observed in the last decade, but the lack of understanding of the origin of the excess noise has hindered further improvements. Noise in HEMTs is attributed to a generator at the output, known as drain noise; and a generator at the input, which is attributed to thermal noise of the gate. At cryogenic temperatures of ~4 K, thermal noise is predicted to be negligible. However, a plateau in noise temperature has been observed at physical temperatures below ~20 K, with a negligible improvement in noise performance upon further cooling.
The primary noise mechanism responsible for this plateau is believed to be ohmic heating of the HEMT structure induced by current in the active device channel, a process known as self-heating. At room temperature the ambient thermal noise dominates the amplifier's overall noise performance, but at the cryogenic temperatures required to achieve low-noise performance the self-heating effect produces thermal noise at the input of the HEMT gate which contributes significantly to the total noise. A potential mechanism to mitigate self-heating is to provide an additional thermal dissipation path for the Joule heating in the channel. However, given the sub-micron length scales and buried gate structure of HEMTs, thermal management is challenging. The primary heat conduction pathway, that of phonons travelling through the bulk HEMT substrate, decreases rapidly in magnitude at cryogenic temperatures. An alternative option is to submerge the HEMT in a cryogenic fluid, thereby presenting an alternate thermal conduction route through the HEMT surface into the fluid. This technique, while commonly employed in cryogenic thermal management of superconducting magnets, has not been investigated for HEMTs.
In this work we explore the use of liquid cryogenic cooling to directly mitigate the effect of HEMT self-heating. We test in particular the effectiveness of cooling using superfluid helium-4, which has the highest known thermal conductivity of any known substance. We report a systematic experimental investigation of the noise performance of a cryogenic packaged two-stage HEMT low-noise amplifier over a wide range of biases in a 4.0-5.5 GHz frequency band, with the device immersed in a variety of cryogenic baths including helium-4 vapor, liquid helium-4, superfluid helium-4, and vacuum. We present the details of the experimental apparatus which was constructed to perform microwave noise measurements of the low-noise amplifier when submerged in a liquid cryogen environment. We interpret our results using a small-signal model of the amplifier and compare our findings with the predictions of a phonon radiation model of heat dissipation. We find that liquid cryogenic cooling is unable to mitigate the thermal noise associated with self-heating. Considering this finding, we examine the implications for the lower bounds of cryogenic noise performance in HEMTs by incorporating the effects of self-heating into the existing noise modelling of HEMT amplifiers. Our analysis supports the general design principle for cryogenic HEMTs of maximizing gain at the lowest possible power.
Zoom Link:
https://caltech.zoom.us/j/2122928086?pwd=Mlg5eW4zTUt5L215eWZ0Q3owY3RyQT09