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Mentor Investigates Using Neural Networks for CMP Modeling

Jan. 12, 2018, Jan. 12, 2018 – I recently read a new white paper release by Mentor, a Siemens Business, that delved into the intricacies of Chemical Mechanical Polishing (CMP) and I got a sense of Deja vu. My professional career in the IC industry started at Texas instruments and the white paper made me think of a conversation I had with one of my colleagues over lunch. We were experiencing some yield issues and he told a story of getting home late and trying to explain to his spouse the problems we were encountering. We often take for granted the miracle that the manufacturing of ICs is, and my friend's wife brought that fact into perspective when she quipped, "Forget about yield, you should be happy any of them work at all!".

One of the true innovations that helped make the IC industry what it is, has been the use CMP. CMP is responsible for the "leveling" of the wafer layers that makes for a good planar process. The CMP process is fascinating as it's roughly akin to running a disc sander with a chemical slurry over the wafer to "polish" it smooth. This polishing is highly dependent upon the materials being polished and the density and shapes of the materials in any given location of the chip. To get consistent (and therefore flat) polishing, it's important to maintain a constant density balance across the chip to prevent bumps and dishing that can cause shorts and opens.

With the introduction of softer copper interconnect at the 130nm node (vs harder aluminum), and then the introduction of high-k metal-gate technologies with costlier lithographic patterning schemes, it has become increasingly important to have higher accuracy CMP models that can be used to identify possible design "CMP hot-spots" before going to manufacturing. As mentioned, Mentor recently released a new white paper with a novel approach that uses machine learning and neural networks to accelerate the generation of good post-deposition (pre-CMP) profiles to be used with CMP manufacturing simulation models.
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