Indirect performance sensing for on-chip analog self-healing via Bayesian model fusion

Author

Sun, Sen ; Wang, F. ; Yaldiz, Soner ; Li, Xin ; Pileggi, Larry ; Natarajan, Arutselvan ; Ferriss, Mark ; Plouchart, J.-O. ; Sadhu, B. ; Parker, Brendon ; Valdes-Garcia, A. ; Sanduleanu, Mihai ; Tierno, Jose ; Friedman, Daniel

Author_Institution

Electr. & Comput. Eng. Dept., Carnegie Mellon Univ., Pittsburgh, PA, USA

fYear

2013

fDate

22-25 Sept. 2013

Firstpage

1

Lastpage

4

Abstract

On-chip analog self-healing requires low-cost sensors to accurately measure various performance metrics. In this paper we propose a novel approach of indirect performance sensing based upon Bayesian model fusion (BMF) to facilitate inexpensive-yet-accurate on-chip performance measurement. A 25GHz differential Colpitts voltage-controlled oscillator (VCO) designed in a 32nm 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 improved from 0% to 69.17% for a wafer after the proposed self-healing is applied.

Keywords

Bayes methods; CMOS analogue integrated circuits; MMIC oscillators; silicon-on-insulator; voltage-controlled oscillators; Bayesian model fusion; CMOS SOI process; VCO; differential Colpitts voltage-controlled oscillator; frequency 25 GHz; indirect performance sensing; low-cost sensors; on-chip analog self-healing; size 32 nm; Data models; Noise measurement; Phase measurement; Phase noise; Semiconductor device modeling; Sensors; Voltage-controlled oscillators;

fLanguage

English

Publisher

ieee

Conference_Titel

Custom Integrated Circuits Conference (CICC), 2013 IEEE

Conference_Location

San Jose, CA

Type

conf

DOI

10.1109/CICC.2013.6658489

Filename

6658489