| dc.description.abstract | With the continuous shrinking of CMOS technologies, even a single IC can
perform complex computations in a tiny chip area. However, along with the
downscale of the circuit area, aging effects become a non-negligible reliability
threat. Amount all aging effects, Negative Bias Temperature Instability (NBTI) is
one of the most serious agine effects in nanoscale technology. The NBTI will
increases the threshold voltage of pMOS transistors along with the continuous
“ON” stress, and therefore potentially increase the propagation delay. If the
propagation delay on a critical path violates the timing requirements in the
specification, it may lead to timing failure or even malfunction. In order to avoid
the unacceptable situation, it is important to monitor the aging situation during
circuit operation and provide necessary calibrations. Therefore, in previous works
the concept of using aging sensors to provide real-time monitoring as well as the
applying appropriate tolerance mechanism when the aging occurs has been
proposed. However, the number of aging sensors can be placed in a chip is limited
due to the area overhead. In the previous works, aging monitors are usually
deployed on the end of the critical paths to ensure the worst-case aging situation
can be successfully captured. However, the critical path may vary after circuit
aging. Simply deploying aging sensors with respect to the critical paths obtained
from health circuit analysis may be unable to reflect the real aging situation. One
of the possible approaches to accurately deploy the aging sensors is to perform
detailed aging simulation under different aging situation at design time, and figure
out the potential critical paths under different aging situations. After that, the
aging sensors are deployed based on the above information. Although the
proposed approach can successfully catch the aging situation, the unacceptable
simulation time makes the method impractical for larger circuits. Therefore, an
efficient aging sensor deployment methodology is in demand.
V
To solve the above problem, in this dissertation, we propose a machine
learning based aging monitor deployment framework to efficiently deploy aging
sensors. In out framework we employ the similar concept that deploying aging
sensors based on detailed aging simulation under different aging situations, but
we apply Generative Adversarial Network (GAN) which replaces tedious detailed
simulations and generates a large amount of simulations results to significantly
reduce the execution time. To translate the aging information to and out of GAN,
we propose a data transform method to image the aging information back and
forth. Finally, we propose an aging sensor placement algorithm based on the aging
information provided by GAN. Experimental results show that our framework can
efficiently and accurately deploy aging sensors by reaching 100% timing failure
detection rate, a 30.77% improvement compare to a previous work. Moreover, a
330x speed up can also be conducted compare to a previous work. | en_US |