Data analytics and machine learning for continued semiconductor scaling

Although there has been a rapid and greatly publicized growth of data analytics and machine learning methodologies across many applications, and in virtually every industry, these developments seem to have almost completely been missed in the semiconductor integrated circuit (IC) space. With the 14nm process technology node currently in production, and both 10 and 7nm nodes at different stages of development, the IC'ecosystem' is being restructured and consolidated across its four traditional components (i.e., fabless design companies, electronic design automation and intellectual property suppliers, process and metrology tools suppliers, and silicon foundries). There are, however, intrinsic technology factors (e.g., the continual deceleration of geometric scaling and the delayed introduction of key patterning technologies) that are primary sources of disruption to this restructuring. There are also critical hidden gaps and bottlenecks in the design-to-manufacturing data information pipeline. The deployment of carefully selected data analytics techniques (with/without machine learning algorithms) therefore represents a strategic opportunity to enable a 2 year/node ('more-Moore') cycle at 10nm and below in the semiconductor industry.