1 research outputs found
Silicon Dating
In order to service an ever-growing base of legacy electronics, both
government and industry customers must turn to third-party brokers for
components in short supply or discontinued by the original manufacturer.
Sourcing equipment from a third party creates an opportunity for unscrupulous
gray market suppliers to insert counterfeit devices: failed, knock-off, or
otherwise inferior to the original product. This increases the supplier's
profits at the expense of reduced performance/reliability of the customer's
system. The most challenging class of counterfeit devices to detect is recycled
counterfeits: recovered genuine devices which are re-sold as new. Such devices
are difficult to detect because they typically pass performance and parametric
tests but fail prematurely due to age-related wear.
To address the challenge of detecting recycled devices pre-deployment, we
develop Silicon Dating: a low-overhead classifier for detecting recycled
integrated circuits using Static Random-Access Memory (SRAM) power-on states.
Silicon Dating targets devices with no known-new record or purpose-built
anti-recycling hardware. We observe that over time, software running on a
device imprints its unique data patterns into SRAM through analog-domain
changes; we measure the level and direction of this change through SRAM
power-on state statistics. In contrast to highly symmetric power-on states
produced by variation during SRAM fabrication, we show that embedded software
data is generally highly asymmetric and that the degree of power-on state
asymmetry imprinted by software reveals device use. Using empirical results
from embedded benchmarks running on several microcontrollers, we show that
Silicon Dating identifies recycled devices with 84.1% accuracy with no
software-specific knowledge and with 92.0% accuracy by incorporating software
knowledge---without prior device enrollment or modification.Comment: 13 pages, 12 figure