Data Analytics and Machine Learning for Design-Process-Yield Optimization in Electronic Design Automation and IC semiconductor manufacturing

In response to the current challenges of end-of-Moore scaling, a systematic analysis of the data information flows in the Design-to-Manufacturing pipeline highlights opportunities for the introduction of (big) data analytics and machine learning solutions. In this paper we review the eco-system components and describe the fundamental data-flows in the IC Design-to-Manufacturing chain, highlighting both the well-established and functioning sub-systems, as well as the critical bottlenecks. A quantitative definition of physical design space coverage is proposed, as the unifying abstraction available for all components of the Design-to-Manufacturing flow, allowing for the construction of a computational framework where Data Analytics and Machine Learning methodologies and tools can be successfully applied. The juxtaposition of Design-Technology-Co-Optimization (DTCO) with the novel paradigm of DFM-as-Search and their necessary integration in the DFM computational toolkit, clearly exemplify how the all the advanced IC nodes (14, 10, 7 and 5nm) definitely require the adoption of a new class of correlation extraction algorithms for heterogeneous data sets.