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Simulation and evolutionary optimization of electron-beam lithography with genetic and simplex-downhill algorithms

机译:遗传和单纯形下坡算法对电子束光刻的仿真和进化优化

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Genetic and simplex-downhill (SD) algorithms were used for the optimization of the electron-beam lithography (EBL) step in the fabrication of microwave electronic circuits. The definition of submicrometer structures involves complex exposure patterns that are cumbersome to determine experimentally and very difficult to optimize with linear search algorithms due to the high dimensionality of the search space. An SD algorithm was first used to solve the optimization problem. The large number of parameters and the complex topology of the search space proved too difficult for this algorithm, which could not yield satisfactory patterns. A hybrid approach using genetic algorithms (GAs) for global search, and an SD algorithm for further local optimization, was unable to drastically improve the structures optimized with GAs alone. A carefully studied fitness function was used. It contains mechanisms for reduced dependence on process tolerances. Several methods were studied for the selection, crossover, mutation, and reinsertion operators. The GA was used to predict scanning patterns for 100-nm T-gates and gate profiles with asymmetric recess and the structures were fabricated successfully. The simulation and optimization tool can help shorten response times to alterations of the EBL process by suppressing time-consuming experimental trial-and-error steps.
机译:遗传和单纯形下坡(SD)算法用于优化微波电子电路制造中的电子束光刻(EBL)步骤。亚微米结构的定义涉及复杂的曝光模式,这些模式很难通过实验确定,并且由于搜索空间的高维度,因此很难使用线性搜索算法进行优化。首先使用SD算法来解决优化问题。对于该算法而言,大量的参数和复杂的搜索空间拓扑被证明太难了,无法产生令人满意的模式。使用遗传算法(GA)进行全局搜索和SD算法进行进一步局部优化的混合方法无法彻底改善仅使用GA进行优化的结构。使用了经过仔细研究的适应度函数。它包含减少对过程公差的依赖的机制。研究了几种选择,交叉,变异和重新插入算子的方法。遗传算法用于预测100 nm T型栅和具有非对称凹口的栅轮廓的扫描图案,并成功制造了结构。该仿真和优化工具可通过抑制耗时的试验性试验步骤来帮助缩短对EBL工艺变更的响应时间。

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