Genetic Algorithm Optimization of Pulse Waveform for Ion Implantation High-Voltage Power Supplies

Ion implantation technology is the core process for achieving precise doping in semiconductor manufacturing. The pulse waveform parameters (such as amplitude, pulse width, rise/fall time) output by the ion implantation high-voltage power supply directly affect the energy distribution of the ion beam and the uniformity of the implantation dose. Traditional pulse waveform design mostly adopts empirical methods or single-objective optimization, which is difficult to balance multiple performance indicators, leading to problems such as dose deviation and wafer damage during ion implantation. As a global optimization algorithm, genetic algorithm can effectively solve the multi-parameter collaborative optimization problem of pulse waveform, and improve the power supply output characteristics and ion implantation effect.
The optimization process needs to be implemented in three steps: first, build a pulse waveform evaluation system, determine the core optimization goals as "pulse amplitude stability (deviation <0.5%), rise time (<10μs), dose uniformity (error <1%)", and establish a quantitative mathematical model for each goal to correlate waveform parameters with performance indicators; second, design the genetic algorithm optimization process, initialize a population (population size set to 50-100) containing parameters such as pulse amplitude, pulse width, and switching tube trigger time, take the multi-objective function in the evaluation system as the fitness function, and iteratively optimize the population through selection (roulette method), crossover (single-point crossover, crossover probability 0.8), and mutation (mutation probability 0.01) operations until the optimal parameter combination is obtained; finally, verify through simulation and experiment, import the optimized parameters into the power supply control module, use an oscilloscope and ion dose detector to monitor the waveform and implantation effect in real time, and adjust the algorithm parameters to adapt to the implantation needs of different ions (such as B, P, As).
Compared with traditional optimization methods, the pulse waveform optimized by genetic algorithm has significant advantages: the pulse amplitude fluctuation is reduced from 1.2% to 0.4%, the rise time is shortened from 15μs to 8μs, and the ion implantation dose uniformity error is reduced from 1.8% to 0.9%. In the 12-inch wafer ion implantation process, the optimized power supply can improve the resistivity consistency of the chip doping area by 20%, effectively reduce the wafer scrap rate, and provide key technical support for the high performance and miniaturization of semiconductor devices.