Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCSI.2014.2333311 | IEEE Trans. on Circuits and Systems |
Keywords | DocType | Volume |
process variation,CMOS analogue integrated circuits,parametric yield,frequency 25 GHz,nanoscale integrated circuit,voltage-controlled oscillators,RF performance metrics,Indirect performance sensing,indirect performance sensing,radiofrequency integrated circuits,on-chip self-healing,analog integrated circuit,silicon-on-insulator,VCO,size 32 nm,RF circuits,integrated circuit,self-healing,differential Colpitts voltage-controlled oscillator,CMOS SOI process | Journal | 61 |
Issue | ISSN | Citations |
8 | 1549-8328 | 5 |
PageRank | References | Authors |
0.49 | 0 | 14 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shupeng Sun | 1 | 66 | 5.35 |
| Fa Wang | 2 | 55 | 8.89 |
| Soner Yaldiz | 3 | 69 | 7.29 |
| Xin Li | 4 | 530 | 60.02 |
| Lawrence T. Pileggi | 5 | 9 | 2.71 |
| Arun Natarajan | 6 | 170 | 70.18 |
| Mark A. Ferriss | 7 | 64 | 9.69 |
| Jean-Olivier Plouchart | 8 | 106 | 20.36 |
| Bodhisatwa Sadhu | 9 | 99 | 15.09 |
| Benjamin D. Parker | 10 | 87 | 12.51 |
| Alberto Valdes-Garcia | 11 | 257 | 32.30 |
| Mihai A. T. Sanduleanu | 12 | 32 | 10.00 |
| José A. Tierno | 13 | 206 | 26.21 |
| Daniel J. Friedman | 14 | 291 | 48.52 |