Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137092
Title: An in-pixel gain amplifier based event-driven physical unclonable function for CMOS dynamic vision sensors
Authors: Wang, Biyin
Zhao, Xiaojin
Zheng, Yue
Chang, Chip-Hong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Wang, B., Zhao, X., Zheng, Y., & Chang, C.-H. (2019). An in-pixel gain amplifier based event-driven physical unclonable function for CMOS dynamic vision sensors. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). doi:10.1109/ISCAS.2019.8702472
Abstract: In this paper, a novel in-pixel event-driven physical unclonable function (PUF) is presented for the rapidly developed CMOS dynamic vision sensor (DVS). Different from traditional widely reported PUF implementations with additional dedicated silicon area, power consumption and peripheral circuitries, the proposed implementation extracts PUF based on the original gain amplifier existing in the mainstream DVS pixel, which is necessary to amplify the front-end logarithmic photoreceptor’s relatively weak output signal, according to the ratio of the in- pixel capacitor pair. With any ON/OFF event generated and the corresponding DVS pixel fired asynchronously, the DVS pixel’s own gain amplifier will be reset in order to capture the next possible event. Due to the inevitable variation of the semiconductor fabrication process, the reset voltages of different DVS pixels’ gain amplifiers are slightly different. A bidirectional counter based analog-to-digital converter is customized to digitize the successively fired pixel pair with the sign bit representing the PUF bit (i.e. the reset voltages’ difference). Moreover, the proposed implementation is validated using a standard 0.18μm CMOS process in Cadence. According to the extensive post-layout simulation results, the uniqueness is calculated to be 49.97%. With the operating temperature varying from −40◦C to 120◦C and supply voltage varying from 1.7V to 2.1V, the worst-case reliability is reported to be 96.48% and 97.27%, respectively. Meanwhile, its superior randomness is also verified using the NIST test suite.
URI: https://hdl.handle.net/10356/137092
DOI: 10.1109/ISCAS.2019.8702472
Rights: © 2019 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. The published version is available at: https://doi.org/10.1109/ISCAS.2019.8702472
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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