DTC Linearization via Mismatch-Noise Cancellation for Digital Fractional-N PLLs

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 69; no. 12; pp. 4993 - 5006
• Main Authors: Helal, Eslam, Eissa, Amr I., Galton, Ian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: background calibration; Calibration; Capacitors; Convergence; Delays; digital PLL; digital-to-time converter (DTC); dynamic element matching (DEM); Fractional-N PLL; Inverters; least-mean-square algorithm (LMS); Linearization; Measurement uncertainty; mismatch-noise cancellation (MNC); Phase locked loops; Phase measurement; Quantization (signal); quantization noise cancellation (QNC)
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2022.3200047

Digital-to-time converter (DTC) based quantization noise cancellation (QNC) has recently been shown to enable excellent fractional-<inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> PLL performance, but it requires a highly-linear DTC. Known DTC linearization strategies include analog-domain techniques which involve performance tradeoffs and digital predistortion techniques which converge slowly relative to typical required PLL settling times. Alternatively, a DTC implemented as a cascade of 1-bit DTC stages can be made highly linear without special techniques, but such DTCs typically introduce excessive error from component mismatches which has so far hindered their use in low-jitter PLLs. This paper presents a background calibration technique that addresses this issue by adaptively canceling error from DTC component mismatches. The technique is entirely digital, is compatible with a large class of digital fractional-<inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> PLLs, and has at least an order of magnitude lower convergence time than the above-mentioned predistortion techniques. The paper presents a rigorous theoretical analysis closely supported by simulation results which quantifies the calibration technique's convergence time and noise performance.