Optimizing operational parameters for Z-pinch EUV source by artificial neural network (ANN)

In our studies, EUV radiation from xenon-filled fast capillary Z-pinch discharge has been made. The urgent need for modeling and optimizing such xenon plasma sources requires theoretical efforts in atomic and plasma physics. However, the physics of the plasma-produced processes, which lead to EW emission, are intrinsically complicated. Many simplifying assumption is inevitable with numerical simulation, resulting in suspicious outcomes. ANN can learn complex plasma data both adaptively and nonlinearly without any formation on the causal relationship between the input and output patterns. With the learning and generalization abilities, ANN is utilized to model and optimize Z-pinch piasma source, which is Characterized with a experimental design at vaned operationaJ parameters including electric power input, applied voltageicurrent, pulse repetition, MPC parameters, electrode geometry, Xe flow rate as well as convention efficiency, EUV source size, radiation power etc.