Electronic energy disorder associated within amorphous and polycrystaline molecular organic thin film structures strongly affects the macroscopic observable behavior of organic field effect transistors (OFET) and poses practical challenges to implementing OFET circuits. It has been convenient to ignore the detailed physical processes associated with this disorder and model OFET behavior as equivalent to silicon FETs, but such simplifications limit our ability to develop integrated circuit technology as they fail to predict the true integrated OFET behavior. This talk will highlight the evolution of the organic electronic circuit technology and the challenges that lay ahead, emphasizing the need for physically accurate models of device behavior as the cornerstone of any future circuit advancements.

As the design-manufacturing interface becomes increasingly complicated with IC technology scaling, the corresponding process variability poses great challenges for nanoscale analog/RF design. Design optimization based on the enumeration of process corners has been widely used , but can suffer from inefficiency and overdesign. In this paper we propose to formulate the analog and RF design with variability problem as a special type of robust optimization problem, namely robust geometric programming. The statistical variations in both the process parameters and design variables are captured by a pre-specified confidence ellipsoid. Using such optimization with ellipsoidal uncertainy approach, robust design can be obtained with guaranteed yield bound and lower design cost, and most importantly, the problem size grows linearly with number of uncertain parameters. Numerical examples demonstrate the efficiency and reveal the trade-off between the design cost versus the yield requirement. We will also demonstrate significant improvement in the design cost using this approach compared with corner-enumeration optimization.