The DFM (design for manufacturing) methods are used during technology alignment and adoption processes in the semiconductor industry (SI) for manufacturability and yield assessments. These methods have worked well till 250nm technology for the transformation of systematic variations into rules and/or models based on the single-source data analyses, but beyond this technology they have turned into ineffective R&D efforts. The reason for this is our inability to capture newly emerging spatial variations. It has led an exponential increase in technology lead times and costs that must be addressed; hence, objectively in this thesis we are focused on identifying and removing causes associated with the DFM ineffectiveness. The fabless, foundry and traditional integrated device manufacturer (IDM) business models are first analyzed to see coherence against a recent shift in business objectives from time-to-market (T2M) and time-to-volume towards (T2V) towards ramp-up rate. The increasing technology lead times and costs are identified as a big challenge in achieving quick ramp-up rates; hence, an extended IDM (e-IDM) business model is proposed to support quick ramp-up rates which is based on improving the DFM ineffectiveness followed by its smooth integration. We have found (i) single-source analyses and (ii) inability to exploit huge manufacturing data volumes as core limiting factors (failure modes) towards DFM ineffectiveness during technology alignment and adoption efforts within an IDM. The causes for single-source root cause analysis are identified as the (i) varying metrology reference frames and (ii) test structures orientations that require wafer rotation prior to the measurements, resulting in varying metrology coordinates (die/site level mismatches). A generic coordinates mapping and alignment model (MAM) is proposed to remove these die/site level mismatches, however to accurately capture the emerging spatial variations, we have proposed a spatial positioning model (SPM) to perform multi-source parametric correlation based on the shortest distance between respective test structures used to measure the parameters. The (i) unstructured model evolution, (ii) ontology issues and (iii) missing links among production databases are found as causes towards our inability to exploit huge manufacturing data volumes. The ROMMII (referential ontology Meta model for information integration) framework is then proposed to remove these issues and enable the dynamic and efficient multi-source root cause analyses. An interdisciplinary failure mode effect analysis (i-FMEA) methodology is also proposed to find cyclic failure modes and causes across the business functions which require generic solutions rather than operational fixes for improvement. The proposed e-IDM, MAM, SPM, and ROMMII framework results in accurate analysis and modeling of emerging spatial variations based on dynamic exploitation of the huge manufacturing data volumes.
