The development of a machine learning-based excimer laser performance simulator for engineering maintenance decisions

Abstract

In excimer laser operation, the maintenance choices by the field service engineer are critical to maximize laser performance while minimizing laser downtime, part replacement expenses, and overall touches to the instrument. To optimize maintenance choices, the engineer must estimate future internal performance of the laser, the impact of each consumable part and their interactions, the impact of operational settings and their interactions, and the optimal timing for each maintenance event. To aid engineers’ decision-making, a deep learning-based laser simulator was developed. The simulator forecasts and plots laser performance under one or multiple maintenance scenarios where each scenario may each have different maintenance timing and multiple maintenance operations such as parts replacement and other operational choices. The simulator is based on a deep recurrent neural network (RNN) with a seq2seq encoder-decoder architecture. Through the encoder, this architecture leverages model inputs that include historical laser performance and configuration data in a temporal dependence structure. Through the decoder, the architecture also captures temporally specific information about future laser operation. By adjusting the decoder inputs, the model forecasts can be altered to reflect future laser maintenance scenarios under consideration. The RNN is deployed in a software plugin for Fabscape® which provides a graphical user interface with interactive elements for field service engineers to forecast, compare maintenance operations, and compare maintenance timing on future laser performance. Ultimately, by simulating the impact of maintenance through the deep learning model and GUI, field service engineers can gain insights to enhance proactive maintenance and plan upcoming service events.