Low-Frequency Noise in Silicon-Germanium BiCMOS Technology
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Low-frequency noise (LFN) is characterized using in-house measurement systems in a variety of SiGe HBT generations. As technology scales to improve the performance and integration level, a large low-frequency noise variation in small geometry SiGe HBTs is first observed in 90 GHz peak fT devices. The fundamental mechanism of this geometry dependent noise variation is thought to be the superposition of individual Lorentzian spectra due to the presence of G/R centers in the device. The observed noise variation is the result of a trap quantization effect, and is thus best described by number fluctuation theory rather than mobility fluctuation theory. This noise variation continues to be observed in 120 GHz and 210 GHz peak fT SiGe HBT BiCMOS technology. Interestingly, the noise variation in the 210 GHz technology generation shows anomalous scaling behavior below about 0.2-0.3um2 emitter geometry, where the noise variation rapidly decreases. Data shows that the collector current noise is no longer masked by the base current noise as it is in other technology generations, and becomes the dominant noise source in these tiny 210 GHz fT SiGe HBTs. The proton response of LFN in SiGe HBTs is also investigated in this thesis. The results show that the relative increase of LFN is minor in transistors with small emitter areas, but significant in transistors with large emitter areas after radiation. A noise degradation model is proposed to explain this observed geometry dependent LFN degradation. A 2-D LFN simulation is applied to SiGe HBTs for the first time in order to shed light on the physical mechanisms responsible for LFN. A spatial distribution of base current noise and collector current noise reveals the relevant importance of the physical locations of noise sources. The impact of LFN in SiGe HBTs on circuits is also examined. The impact of LFN variation on phase noise is demonstrated, showing VCOs with small geometry devices have relatively large phase noise variation across samples.