New method for determination of arsenic content in tail gases during semiconductor processing

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Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

Optically monitoring and controlling nanoscale topography during semiconductor etching

We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact Mach–Zehnder interferometer to recover quantitative amplitude and phase maps of the field reflected by the sample.The low temporal noise of 0.6 nm per pixel at 8.93 frames per second enabled us to collect a three-dimensional movie showing the dynamics of wet etching and thereby accurately quantify non-uniformities in the etch rate both across the sample and over time.By displaying a gray-scale digital image on the sample with a computer projector,we performed photochemical etching to define arrays of microlenses while simultaneously monitoring their etch profiles with epi-DPM.

Performance improvement by enhancing the well-barrier hole burning in a quantum well semiconductor optical amplifier

In this paper, we demonstrated a novel physical mechanism based on the well-barrier hole burning enhancement in a quantum well （QW） semiconductor optical amplifier （SOA） to improve the operation performance. To completely characterize the physical mechanism, a complicated theoretical model by combining QW band structure calculation with SOA＇s dynamic model was constructed, in which the carrier transport, interband effects and intraband effects were all taken into account. The simulated results showed optimizing the thickness of the separate confinement heterostructure （SCH） layer can effectively enhance the well-barrier hole burning, further enhance the nonlinear effects in SOA and reduce the carrier recovery time. At the optimal thickness, the SCH layer can store enough carrier numbers, and simultaneously the stored carriers can also be fast and effectively injected into the QWs.

Highly controllable ICP etching of GaAs based materials for grating fabrication

Highly controllable ICP etching of GaAs based materials with SiCl;/Ar plasma is investigated.A slow etching rate of 13 nm/min was achieved with RFl = 10 W,RF2 = 20 W and a high ratio of Ar to SiCl;flow.First order gratings with 25 nm depth and 140 nm period were fabricated with the optimal parameters.AFM analysis indicated that the RMS roughness over a 10 x 10μm;area was 0.3 nm,which is smooth enough to regrow high quality materials for devices.