Big data analytics is the latest spotlight with all the glare of fame ranging from media coverage to booming startup companies to eye-catching merges and acquisitions. On the contrary, the 336 billion industry of semiconductor was seen as an old-fashioned business, with fading interests from the best and brightest among young graduates and engineers. How will modern big data analytics help the semiconductor industry walk through this transition? This paper answers this question via a number of practical but challenging problems arising from semiconductor manufacturing process. We show that many existing machine learning algorithms are not well positioned to solve these problems, and novel techniques involving temporal, structural and hierarchical properties need to be developed to solve these problems.