Feasibility of Controlling Gas Concentration and Temperature Distributions in a Semiconductor Chamber with CT-TDLAS

The feasibility to control the gas concentration and temperature distributions in a semiconductor process chamber by measuring them was investigated. Gas concentration and temperature distributions for various flow rates were measured with the computed tomography-tunable diode laser absorption spectroscopy (CT-TDLAS). The infrared absorption spectra of multiple laser paths passing through the measured area were collected and the distributions of methane concentration and temperature in the chamber were reconstructed with the computed tomography (CT) calculations. The measured results indicated that the distributions can be independently controlled by measuring with the CT-TDLAS and adjusting the flow rates and the susceptor temperature

A direct comparison of high-speed methods for the numerical Abel transform

The Abel transform is a mathematical operation that transforms a cylindrically symmetric three-dimensional (3D) object into its two-dimensional (2D) projection. The inverse Abel transform reconstructs the 3D object from the 2D projection. Abel transforms have wide application across numerous fields of science, especially chemical physics, astronomy, and the study of laser-plasma plumes. Consequently, many numerical methods for the Abel transform have been developed, which makes it challenging to select the ideal method for a specific application. In this work eight transform methods have been incorporated into a single, open-source Python software package (PyAbel) to provide a direct comparison of the capabilities, advantages, and relative computational efficiency of each transform method. Most of the tested methods provide similar, high-quality results. However, the computational efficiency varies across several orders of magnitude. By optimizing the algorithms, we find that some transform methods are sufficiently fast to transform 1-megapixel images at more than 100 frames per second on a desktop personal computer. In addition, we demonstrate the transform of gigapixel images.Comment: 9 pages, 5 figure

Relative Entropy Regularised TDLAS Tomography for Robust Temperature Imaging

Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for in situ combustion diagnostics, yielding images of both species concentration and temperature. The temperature image is generally obtained from the reconstructed absorbance distributions for two spectral transitions, i.e. two-line thermometry. However, the inherently ill-posed nature of tomographic data inversion leads to noise in each of the reconstructed absorbance distributions. These noise effects propagate into the absorbance ratio and generate artefacts in the retrieved temperature image. To address this problem, we have developed a novel algorithm, which we call Relative Entropy Tomographic RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation is introduced for high-fidelity temperature image retrieval from jointly reconstructed two-line absorbance distributions. We have carried out numerical simulations and proof-of-concept experiments to validate the proposed algorithm. Compared with the well-established Simultaneous Algebraic Reconstruction Technique (SART), the RETRO algorithm significantly improves the quality of the tomographic temperature images, exhibiting excellent robustness against TDLAS tomographic measurement noise. RETRO offers great potential for industrial field applications of TDLAS tomography, where it is common for measurements to be performed in very harsh environments.Comment: Preprint submitted to IEEE Transactions on Instrumentation and Measuremen

Two-dimensional temperature measurement in a high temperature and high pressure combustor using CT-TDLAS with a wide scanning laser at 1335-1375nm

Tunable diode laser absorption spectroscopy (TDLAS) technology is a developing method for temperature and species concentration measurements with the features of non-contact, high precision, high sensitivity, etc. The difficulty of two-dimensional (2D) temperature measurement in actual combustors has not yet been solved because of pressure broadening of absorption spectra, optical accessibility, etc. In this study, the combination of computed tomography (CT) and TDLAS with a wide scanning laser at 1335-1375nm has been applied to a combustor for 2D temperature measurement in high temperature of 300-2000K and high pressure of 0.1-2.5MPa condition. An external cavity type laser diode with wide wavelength range scanning at 1335-1375nm was used to evaluate the broadened H2O absorption spectra due to the high temperature and high pressure effect. The spectroscopic database in high temperature of 300-2000K and high pressure of 0.1-5.0MPa condition has been revised to improve the accuracy for temperature quantitative analysis. CT reconstruction accuracy was also evaluated in different cases, which presented the consistent temperature distribution between CT reconstruction and assumed distributions. The spatial and temporal distributions of temperature in the high temperature and high pressure combustor were measured successfully by CT-TDLAS using the revised spectroscopic database

Hierarchical temperature imaging using pseudoinversed convolutional neural network aided TDLAS tomography

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows. Compared with the computational tomographic algorithms, Convolutional Neural Networks (CNNs) have been proofed to be more robust and accurate for image reconstruction, particularly in case of limited access of laser beams in the Region of Interest (RoI). In practice, flame in the RoI that requires to be reconstructed with good spatial resolution is commonly surrounded by low-temperature background. Although the background is not of high interest, spectroscopic absorption still exists due to heat dissipation and gas convection. Therefore, we propose a Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses efficiently the training and learning resources for temperature imaging in the RoI with good spatial resolution, and (b) reconstructs the less spatially resolved background temperature by adequately addressing the integrity of the spectroscopic absorption model. In comparison with the traditional CNN, the newly introduced pseudo inversion of the RoI sensitivity matrix is more penetrating for revealing the inherent correlation between the projection data and the RoI to be reconstructed, thus prioritising the temperature imaging in the RoI with high accuracy and high computational efficiency. In this paper, the proposed algorithm was validated by both numerical simulation and lab-scale experiment, indicating good agreement between the phantoms and the high-fidelity reconstructions.Comment: Submitted to IEEE Transactions on Instrumentation and Measuremen