  Predicting the minimum operating voltage ($V_{min}$) of chips is one of the
important techniques for improving the manufacturing testing flow, as well as
ensuring the long-term reliability and safety of in-field systems. Current
$V_{min}$ prediction methods often provide only point estimates, necessitating
additional techniques for constructing prediction confidence intervals to cover
uncertainties caused by different sources of variations. While some existing
techniques offer region predictions, but they rely on certain distributional
assumptions and/or provide no coverage guarantees. In response to these
limitations, we propose a novel distribution-free $V_{min}$ interval estimation
methodology possessing a theoretical guarantee of coverage. Our approach
leverages conformalized quantile regression and on-chip monitors to generate
reliable prediction intervals. We demonstrate the effectiveness of the proposed
method on an industrial 5nm automotive chip dataset. Moreover, we show that the
use of on-chip monitors can reduce the interval length significantly for
$V_{min}$ prediction.
