A survey-based, empirical study that benchmarks the productivity of photomask manufacturers has led to some
significant conclusions. Firstly, the wide variation in the productivity indicators from company to company suggests
that all participants may have significant cost-reduction opportunities within their operations. Secondly, the high
downtime of pattern generation tools is limiting productivity. Thirdly, producing smaller feature sizes is correlated to
an investment in engineering and experimentation capacity. It could not be confirmed that photomask manufacturers
are successfully taking advantage of economies of scale.
This paper summarizes three studies of the semiconductor industry conducted at SEMATECH and MIT's Sloan School of Management. In conjunction they lead to the conclusion that rapid problem solving is an essential component of profitability in the semiconductor industry, and that metrology-based control is instrumental to rapid problem solving. The studies also identify the need for defect attribution. Once a source of a defect has been identified, the appropriate resources--human and technological--need to be brought into the physically optimal location for corrective action. The Internet is likely to enable effective defect attribution by inducing collaboration between different companies.
A model based on information theory, which allows yield managers to choose the optimal strategies for yield management in microelectronic manufacturing, is presented. The data reduction rate per experimentation cycle and data reduction rate per unit time serve as benchmarking metrics for yield learning. These newly defined metrics enable managers to make objective comparisons of apparently unrelated technologies. Four yield analysis tools -- electrical testing, automatic defect classification, spatial signature analysis and wafer position analysis -- are examined in detail to suggest an optimal yield management strategy for both the R and D and volume production environments.