Measurement of inherent noise in EDA tools

With advancing semiconductor technology and exponentially growing design complexities, predictability of design tools becomes an important part of a stable top-down design process. Prediction of individual tool solution quality enables designers to use tools to achieve best solutions within prescribed resources, thus reducing design cycle time. However, as EDA tools become more complex, they become less predictable. One factor in the loss of predictability is inherent noise in both algorithms and how the algorithms are invoked. In this work, we seek to identify sources of noise in EDA tools, and analyze the effects of these noise sources on design quality. Our specific contributions are: (i) we propose new behavior criteria for tools with respect to the existence and management of noise; (ii) we compile and categorize possible perturbations in the tool use model or tool architecture that can be sources of noise; and (iii) we assess the behavior of industry place and route tools with respect to these criteria and noise sources. While the behavior criteria give some guidelines for and characterize the stability of tools, we are not recommending that tools be immune from input perturbations. Rather, the categorization of noise allows us to better understand how tools will or should behave; this may eventually enable improved tool predictors that consider inherent tool noise.