Application of a novel local and automatic PCA algorithm for diffraction pattern denoising in TEM-ASTAR analysis in microelectronics.

This paper introduces a novel denoising method for TEM-ASTAR™ Diffraction Pattern (DP) datasets, termed LAT-PCA (Local Automatic Thresholding - Principal Component Analysis). This approach enhances the established PCA algorithm by partitioning the 4D dataset (a 2D map of 2D DPs) into localized windows. Within these windows, PCA identifies a basis where the physical signal predominantly resides in the higher-order principal components. By thresholding lower-order components, the method effectively reduces noise while retaining the essential features of the DPs. The locality of the approach, focusing on small windows, enhances computational efficiency and aligns with the localized nature of the crystallographic grain signals in ASTAR. The automatic aspect of the method employs a theoretical pure noise distribution, i.e. a Marchenko-Pastur Distribution, to set a threshold, beyond which the components are mostly noise. The LAT-PCA method offers significant reductions in acquisition and post-processing times. With denoised data, selecting the correct parameters for accurate phase maps and grain orientations becomes more straightforward, facilitating robust quantitative grain analysis. Experiments performed on a silicon-germanium-carbon sample validate the method's efficacy. The sample was analyzed with varying acquisition times to produce a high signal-to-noise ratio reference dataset and a lower ratio test dataset. The LAT-PCA algorithm's denoising results on the test dataset were benchmarked against the reference, demonstrating considerable improvements and reliability. In summary, LAT-PCA is an effective, automatic solution for denoising TEM DP datasets. Its adaptability to different noise levels and local processing capability makes it a valuable tool for enhancing dataset quality and reducing the time required for data acquisition and analysis. This method can minimize acquisition time, conserve microscope usage, and reduce sample drift and deterioration, leading to more accurate characterizations with fewer deformation artifacts.

Low loss poly-silicon for high performance capacitive silicon modulators.

Optical properties of poly-silicon material are investigated to be integrated in new silicon photonics devices, such as capacitive modulators. Test structure fabrication is done on 300 mm wafer using LPCVD deposition: 300 nm thick amorphous silicon layers are deposited on thermal oxide, followed by solid phase crystallization anneal. Rib waveguides are fabricated and optical propagation losses measured at 1.31 µm. Physical analysis (TEM ASTAR, AFM and SIMS) are used to assess the origin of losses. Optimal deposition and annealing conditions have been defined, resulting in 400 nm-wide rib waveguides with only 9.2-10 dB/cm losses.

Combining Accurate O2 and Li2O2 Assays to Separate Discharge and Charge Stability Limitations in Nonaqueous Li-O2 Batteries.

Li-air batteries have generated enormous interest as potential high specific energy alternatives to existing energy storage devices. However, Li-air batteries suffer from poor rechargeability caused by the instability of organic electrolytes and carbon cathodes. To understand and address this poor rechargeability, it is essential to elucidate the efficiency in which O2 is converted to Li2O2 (the desired discharge product) during discharge and the efficiency in which Li2O2 is oxidized back to O2 during charge. In this Letter, we combine many quantitative techniques, including a newly developed peroxide titration, to assign and quantify decomposition pathways occurring in cells employing a variety of solvents and cathodes. We find that Li2O2-induced electrolyte solvent and salt instabilities account for nearly all efficiency losses upon discharge, whereas both cathode and electrolyte instabilities are observed upon charge at high potentials.