On-chip ID generation for multi-node implantable devices using SA-PUF
File(s)
Accepted version
Author(s)
Gao, C
Ghoreishizadeh, S
Liu, Y
Constandinou, TG
Type
Conference Paper
Abstract
This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 m CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.
Date Issued
2017-09-28
Date Acceptance
2017-02-17
Citation
2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017, pp.678-681
Publisher
IEEE
Start Page
678
End Page
681
Journal / Book Title
2017 IEEE International Symposium on Circuits and Systems (ISCAS)
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Wellcome Trust
Engineering & Physical Science Research Council (EPSRC)
Imperial College London
Grant Number
BH134389
EP/M020975/1
Source
IEEE International Symposium on Circuits and Systems (ISCAS)
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Publication Status
Published
Start Date
2017-05-28
Finish Date
2017-05-31
Coverage Spatial
Baltimore, MD (USA)
Date Publish Online
2017-09-28