Abstract
A harmonic-mixer-based dual-feedback loop architecture for fractional frequency synthesis is proposed in this letter. By performing frequency subtraction instead of frequency division by using the harmonic mixer in the feedback path, the proposed phase-locked loop (PLL) achieves a unity feedback coefficient, which avoids ΔΣ noise amplification by the loop. This leads to low phase noise and low spurs without the use of complex calibration schemes. The proposed architecture is demonstrated with a 3.2-to-3.8GHz fractional-N PLL prototype in a 65-nm bulk CMOS process that achieves a worst-case in-band fractional spur of -65 dBc.
Highlights
Fractional-N frequency synthesizers are commonly used in applications such as wireless communications where high resolution control of the carrier frequency is essential
The design of fractional-N phase locked loops (PLLs) generally faces two issues: phase noise peaking due to the shaped quantization noise from the ∆Σ modulator (DSM) in the multi-modulus divider (MMD), and in-band fractional spurs caused by the quantization noise interacting with the charge-pump (CP) nonlinearity [1]
Many different approaches have been proposed to tackle these issues. Works such as [2]–[4] attempt to cancel the deterministic noise from the ∆Σ modulator by using a current DAC [2] or by using a digital-to-time converter [3], [4]. This approach requires gain matching as well as high linearity for the circuit components, which leads to complex calibration schemes such as a least mean square (LMS) algorithm
Summary
Fractional-N frequency synthesizers are commonly used in applications such as wireless communications where high resolution control of the carrier frequency is essential. Many different approaches have been proposed to tackle these issues Works such as [2]–[4] attempt to cancel the deterministic noise from the ∆Σ modulator by using a current DAC [2] or by using a digital-to-time converter [3], [4]. This approach requires gain matching as well as high linearity for the circuit components, which leads to complex calibration schemes such as a least mean square (LMS) algorithm.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
