Evolutionary Techniques Applied to Mask - layout Synthesis in Micro - Mechanical - Electronic Systems ( MEMS )

This thesis reports an automatic method for synthesizing MEMS mask-layouts. This method incorporates a forward simulation of fabrication into a general evolutionary algorithm loop. An initial random population of mask-layouts is generated. The fabrication of each layout is simulated through a digital process simulator to produce a 3D fabricated shape, which is compared to a user-specified desired shape. Each evolutionary loop governs the stochastic searching behavior such that the mask-layouts whose simulated shapes are closer to the desired shape are more likely to survive. More importantly, the “better” masks are more likely to be evolved among those survived mask-layouts for the next loop. Through such evolutionary iterations, a near global “optimum” mask-layout is likely to be found. By using this evolutionary approach, we are able to take use of existing simulations of fabrication processes to achieve those mask-layout synthesis where reversing a fabrication process simulation (so that a 2D mask-layout might be produced) appears not to be possible. The general evolutionary loop mainly consists of a mask genetics module, an evolutionary technique module and a MEMS simulation module. The mask genetics module provides heuristic genetic operations on mask-layouts, which includes mask coding scheme, random mask generation, random crossovers and mutations. The evolutionary technique module contains stochastic selection schemes and genetic operation schemes to control the searching convergence. The MEMS simulation module is the user input module, which requires a MEMS fabrication simulation and user-specified desired shape. A test loop is constructed