Hybrid intelligent approach for modeling and optimization of semiconductor devices and nanostructures

In this work, we present a hybrid intelligent approach for parameter extraction and design optimization of semiconductor nanoscale devices and nanostructures. Based on evolutionary algorithms, numerical methods, neural network scheme and parallel computing technique, the optimization methodology is developed and successfully implemented. In the hybrid approach, an evolutionary algorithm, such as genetic algorithm or particle swarm optimization, firstly searches the entire problem space to get a set of roughly estimated solutions. The numerical method, such as Levenberg–Marquardt method, then performs a local optima search and sets the local optima as the suggested values for the genetic algorithm to perform further optimizations. Meanwhile, the neural network is applied to investigate the influence of parameters on the optimized functions which thus guides the evolutionary direction of genetic algorithm. For solving real world problems, all functional blocks are performed under a PC-based Linux cluster system with message-passing interface libraries. This hybrid intelligent approach has experimentally been implemented and validated for different applications in semiconductor nanodevices and nanostructures. For semiconductor nanodevice parameter extraction, this approach shows its capability to automatically extract a set of global parameters among sixteen 90 nm complementary metal oxide semiconductor (CMOS) devices. Compared with the measured current–voltage (I–V) curves of fabricated CMOS samples, the optimized I–V results are within 3% of accuracy. The computational examinations including sensitivity, convergence property, and parallelization are discussed. For parameter extraction of organic light emitting diode (OLED), the approach also achieves good accuracy for red, green, blue OLEDs. For the third and fourth applications, optimal structure design of silicon photonic taper waveguide and photonic crystal are further advanced by integrating a simulation-based technique in the developed system. All of these experiments demonstrate interesting results and validate the optimization methodology. The concept of hybrid intelligent approach may benefit modeling and optimization in diverse science and engineering problems.