Regulating the microenvironment of active sites is crucial to boost the performance of direct propene epoxidation with H2 and O2. Herein, the enhanced surface transfer of H2O2 intermediates was first achieved by sodium‐decorated Au‐Ti bifunctional active sites. Combined with multi‐techniques (e.g., operando UV–vis–NIR system, DFT studies and quantitative model calculations), it is found that the sodium‐decorated silanols (Si‐ONa species) on the TS‐1 support enhance the formation of Ti‐OOH intermediates by restraining H2O2 decomposition. In addition, sodium‐decorated silanols improves the desorption of propylene oxide, suppressing its ring‐opening and formation for the carbonaceous deposits. Moreover, the sodium‐decorated Au nanoparticles with smaller diameter and higher electron density further strengthen O2 adsorption and boost the H2O2 formation. This work not only provides fundamental understandings on the microenvironment of Au‐Ti bifunctional catalysts but also sheds new light on enhancing the performance by boosting the surface molecular transfer.

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SUMMARYBy monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter (FAUKF)-based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong tracking concept and fuzzy logic idea. Similar to the standard adaptive unscented Kalman filter (AUKF) that employs an adaptive parameter to correct the estimation error covariance, a Takagi-Sugeno fuzzy logic system is designed to provide a better adaptive parameter for smoothing this regulation. Compared with the standard AUKF, the proposed FAUKF has the same strong tracking ability but does not suffer from the drawback of serious tracking fluctuation. Two simulation examples demonstrate the effectiveness of the proposed predictor.

The challenge for the upcoming full-chip CD uniformity (CDU) control at 32nm and 22nm nodes is unprecedented with expected specifications never before attempted in semiconductor manufacturing. To achieve these requirements, OPC models not only must be accurate for full-chip process window characterization for fine-tuning and matching of the existing processes and exposure tools, but also be trust-worthy and predictive to enable processes to be developed in advance of next-generation photomasks, exposure tools, and resists. This new OPC requirement extends beyond the intended application scope for behavior-lumped models. Instead, separable OPC models are better suited, such that each model stage represents the physics and chemistry more completely in order to maintain reliable prediction accuracy. The resist, imaging tool, and mask models must each stand independently, allowing existing resist and mask models to be combined with new optics models based on exposure settings other than the one calibrated previously.In this paper, we assess multiple sets of experimental data that demonstrate the ability of the Tachyon™ FEM (focus and exposure modeling) to separate the modeling of mask, optics, and resists. We examine the predictability improvements of using 3D mask models to replace thin mask model and the use of measured illumination source versus top-hat types. Our experimental wafer printing results show that OPC models calibrated in FEM to one optical setting can be extrapolated to different optical settings, with prediction accuracy commensurate with the calibration accuracy. We see up to 45% improvement with the measured illumination source, and up to 30% improvement with 3D mask. Additionally, we observe evidence of thin mask resist models that are compensating for 3D mask effect in our wafer data by as much as 60%.