Semiconductor
The present numerical work combines gain switching and external optical feedback to generate pulses with a high level of coherence across a large array of semiconductor lasers. The influence of several parameters such as modulation frequency, feedback strength and frequency detuning is analyzed and this dataset gathers the temporal traces generated with our code, that is adapted from the Lang-Kobayashi model to take into account decayed coupling in an array of lasers.
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Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal.
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These datasets contain bulk BTE simulation results for GaAs, InP, GaSb and InAs as a function of electric field at 300 K.
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