  Post-silicon validation is one of the most critical processes in modern
semiconductor manufacturing. Specifically, correct and deep understanding in
test cases of manufactured devices is key to enable post-silicon tuning and
debugging. This analysis is typically performed by experienced human experts.
However, with the fast development in semiconductor industry, test cases can
contain hundreds of variables. The resulting high-dimensionality poses enormous
challenges to experts. Thereby, some recent prior works have introduced
data-driven variable selection algorithms to tackle these problems and achieved
notable success. Nevertheless, for these methods, experts are not involved in
training and inference phases, which may lead to bias and inaccuracy due to the
lack of prior knowledge. Hence, this work for the first time aims to design a
novel conditional variable selection approach while keeping experts in the
loop. In this way, we expect that our algorithm can be more efficiently and
effectively trained to identify the most critical variables under certain
expert knowledge. Extensive experiments on both synthetic and real-world
datasets from industry have been conducted and shown the effectiveness of our
method.
