Development of high-power ArF/F 2 laser platform for VUV microlithography

Kakizaki, Kouji; Fujimoto, Junichi; Yamazaki, Taku; Suzuki, Toru; Mutoh, T.; Kawasuji, Yasufumi; Watanabe, Yukio; Kaminishi, Masashi; Inoue, Toyoharu; Mizoguchi, Hideaki; Kumazaki, Tatsuo; Ariga, Tatsuya; Watanabe, Takayuki; Yabu, Takayuki; Sasano, Koutarou; Hori, Tsukasa; Wakabayashi, Osamu; Sumitani, Akira

doi:10.1117/12.536846

Cited by 8 publications

(2 citation statements)

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“…By nature of operating with high voltage, high gas pressures, reactive halogen gas, and high energy laser light, excimer lasers require maintenance such as consumable part replacements to stay within tight performance requirements. While advancements in hardware and software reduce the need for human intervention, ultimately, to maximize laser uptime and performance, it requires proactive planning of laser maintenance needs and operational settings by field service engineers 1,2,3 . For the engineer to make optimal maintenance choices, it often requires estimating 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.…”

Section: Introductionmentioning

confidence: 99%

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

Strock¹,

Jordan²,

Minegishi³

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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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.

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Section: Introductionmentioning

confidence: 99%

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

Strock¹,

Jordan²,

Minegishi³

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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“…In the field of electronic industry, technologies like the precision manufacturing of modern semiconductor devices and high-resolution lithography (such as 193,157, and 13.5 nm lithography) have become the main driving force for VUV detectors' development (Kakizaki et al, 2004;Li et al, 2017;Richter et al, 2002;Smith and Suzuki, 2018). Precise control of the exposure dose is the key to achieving high-quality lithography, so an excellent VUV detector is required to monitor the real-time energy of single laser pulse accurately (Richter et al, 2002) (Figure 1D).…”

Section: Introductionmentioning

confidence: 99%

Vacuum-Ultraviolet Photon Detections

2020

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Vacuum-ultraviolet (VUV) photon detection technology is an effective means for the exploration in the field of space science (monitoring the formation and evolution of solar storms), high-energy physics (dark matter detection), large-scale scientific facility (VUV free electron lasers) and electronic industry (high-resolution lithography). The advancement of this technology mainly depends on the performance optimization of VUV photodetectors. In this review, we introduced the research progress on the typical VUV photodetectors based on scintillator, photomultiplier tube, semiconductor, and gas, with their unique advantages and optimal performance indicators in different applications summarized. In particular, during recent years, thanks to the advances in ultra-wide bandgap semiconductors, economical VUV photodetectors with low power consumption and small size have been encouragingly developed. Finally, we pointed out the remaining challenges for each type of VUV detector, with the aim of maximizing the performance in a variety of applications in the future.

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Excimer Lasers for Microlithography

Pätzel¹,

Stamm²

Excimer Laser Technology

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Development of high-power ArF/F 2 laser platform for VUV microlithography

Cited by 8 publications

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

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

Vacuum-Ultraviolet Photon Detections

Excimer Lasers for Microlithography

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