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.
The light source continues to play an important role in the evolution of semiconductor lithography - helping chip manufacturers to usher in their next generation nodes. Over the years, Gigaphoton has introduced innovative solutions to meet the various demands of the industry, including Injection Lock technology, active bandwidth control, and green technologies for conserving gas and electricity. Today, the needs of chip manufacturer have grown to become highly diverse and dynamic, because the set of challenges faced by each manufacturer is equally diverse and dynamic. This makes developing software solutions very challenging - as they must address a very broad scope of needs. At the same time, it is becoming increasingly important to gain a much deeper understanding of all aspects of how and to what level the light source is actively affecting each chip manufacturer’s wafer output - both in terms of yield and cost - for solutions and technologies to be effective and meaningful. However, there are many challenges to achieving this.
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