Design Enablement and Design-Centric Assessment of Future Semiconductor Technologies
The semiconductor industry is likely to see several radical changes in the manufacturing, device and interconnect technologies in the next few years and decades. One of the most favorable options of manufacturing technologies is multiple-patterning lithography. This novel technology has serious implications on design, however, and its adoption will necessitate the application of "Design Enablement" methodologies to ensure the compatibility of design.
This dissertation contributes to the design enablement of multiple-patterning technology. We propose a general methodology for the automated adaptation of layout to multiple-patterning masking the complexity in dealing with its manufacturing constraints. We also study the impact of this technology on design and show the benefits of bringing the design perspective into making manufacturing-process decisions. Lastly, we propose a novel technique for DP that reduces cost and improve overlay/Critical-Dimension (CD) control in multiple-patterning.
Many technology choices are presented to achieve scaling to every next node and early technology assessment -- before the actual development of technologies -- has become more necessary than ever as a means to ensure faster adoption and manageable technology/design development costs. Technology assessment is currently a highly unsystematic procedure; it relies on small-scale experiments and manufacturing tests and much on speculations based on technologists/designers experience with previous technology generations.
This dissertation also addresses the problem of increasing complexity in making technological decisions. It aims at the development of a computation infrastructure for the systematic and early assessment of technologies and their impact on circuit design. The infrastructure is the first of its kind and is expected to have a lasting impact on technology development. The infrastructure allows for true exploration of design and technology choices, thereby redirecting research and development efforts toward options that are more likely to eventually see adoption. Finally, the infrastructure is applied to evaluate multiple-patterning process decisions and study their implications on design.