Maria Malik, Rajiv V. Joshi, et al.
IEEE Transactions on VLSI Systems
The advent of the nanoscale integrated circuit (IC) technology makes high performance analog and RF circuits increasingly susceptible to large-scale process variations. On-chip self-healing has been proposed as a promising remedy to address the variability issue. The key idea of on-chip self-healing is to adaptively adjust a set of on-chip tuning knobs (e.g., bias voltage) in order to satisfy all performance specifications. One major challenge with on-chip self-healing is to efficiently implement on-chip sensors to accurately measure various analog and RF performance metrics. In this paper, we propose a novel indirect performance sensing technique to facilitate inexpensive-yet-accurate on-chip performance measurement. Towards this goal, several advanced statistical algorithms (i.e., sparse regression and Bayesian inference) are adopted from the statistics community. A 25 GHz differential Colpitts voltage-controlled oscillator (VCO) designed in a 32 nm CMOS SOI process is used to validate the proposed indirect performance sensing and self-healing methodology. Our silicon measurement results demonstrate that the parametric yield of the VCO is significantly improved for a wafer after the proposed self-healing is applied. © 2014 IEEE.