Volumetric thin film thickness measurement using spectroscopic imaging reflectometer and compensation of reflectance modeling error

Kim, Kwangrak; Kim, Seongryong; Kwon, Soonyang; Pahk, Heui Jae

doi:10.1007/s12541-014-0534-3

Cited by 27 publications

(12 citation statements)

References 15 publications

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“…In reflectometer, spectral reflectance ℜi ( ki ) sam according to wavelength ki is obtained at visible wavelength range using a spectrophotometer. Once the spectral reflectance is obtained, thin film thickness of a point on the substrate can be found by fitting analytical reflectance model ℜi ( t ; ki ) into the measured ℜi ( ki ) sam so as to minimize total sum of the squared errors of:

χ^{2} = \sum_{i = 1}^{n} {|ℜi (t; italicki) - ℜi (italicki) sam|}^{2}

…”

Section: Experimental and Resultsmentioning

confidence: 99%

Simulation of thin film thickness distribution for thermal evaporation process using a scanning linear source

Kim

Pahk

2017

Jnl Soc Info Display

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Thermal evaporation process is the main process involved in the production of OLED displays and with the trends toward larger substrate size and display resolution, film thickness uniformity must be carefully controlled in order to implement exact pixel data. To secure stable film thickness uniformity on the substrate area, thin films are deposited on large‐area glass substrates via thermal evaporation process using a linear source. We designed a linear source and mathematical model was developed to describe the system with a focus on the linear source. Then, system parameters were determined to guarantee uniform thickness using computer‐based simulation, replacing wasteful actual experiments, followed by carrying out experiments based on the determined parameters. After the deposition process, data from the mathematical model and experiments was compared and the resulting agreement was good, verifying the validity of the proposed method. Consequently, by applying the proposed method, display manufacturing process related to thermal evaporation can be controlled within a tight tolerance in order to maximize the production yield rate.

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χ^{2} = \sum_{i = 1}^{n} {|ℜi (t; italicki) - ℜi (italicki) sam|}^{2}

…”

Section: Experimental and Resultsmentioning

confidence: 99%

Simulation of thin film thickness distribution for thermal evaporation process using a scanning linear source

Kim

Pahk

2017

Jnl Soc Info Display

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“…Optical reflectometry is a method which analyzes the reflectance spectra of a sample to measure characteristics such as optical constants and thin film thickness and is a well-known and powerful technique for measuring film thickness quickly in several industries [ 12 , 74 , 75 , 76 , 77 ]. There are extensive variants of this method, which are widely available for review in the literature, but the key operating principle of this methodology is the illumination of a sample with an optical light source, detection of the reflected intensity and fitting this intensity as a function of coating thickness [ 76 ]. Most technologies utilizing this method measure a range of wavelengths to calculate the film thickness, and one of the key limitations to this measurement method in the past is that the technique is only able to measure one point at a time [ 76 ].…”

Section: Potential In-line Coating Thickness Test Methodsmentioning

confidence: 99%

“…There are extensive variants of this method, which are widely available for review in the literature, but the key operating principle of this methodology is the illumination of a sample with an optical light source, detection of the reflected intensity and fitting this intensity as a function of coating thickness [ 76 ]. Most technologies utilizing this method measure a range of wavelengths to calculate the film thickness, and one of the key limitations to this measurement method in the past is that the technique is only able to measure one point at a time [ 76 ]. Variants of this method have included volumetric detection using Charge Coupled Devices (CCD) to monitor an area of a sample, simultaneously, with the disadvantage of decreased detection speeds, but there is literature available into techniques which can reduce these detection times, such as direct phase extraction techniques [ 78 ].…”

Section: Potential In-line Coating Thickness Test Methodsmentioning

confidence: 99%

Continuous In-Line Chromium Coating Thickness Measurement Methodologies: An Investigation of Current and Potential Technology

Jones

Uggalla

et al. 2021

Sensors

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Coatings or films are applied to a substrate for several applications, such as waterproofing, corrosion resistance, adhesion performance, cosmetic effects, and optical coatings. When applying a coating to a substrate, it is vital to monitor the coating thickness during the coating process to achieve a product to the desired specification via real time production control. There are several different coating thickness measurement methods that can be used, either in-line or off-line, which can determine the coating thickness relative to the material of the coating and the substrate. In-line coating thickness measurement methods are often very difficult to design and implement due to the nature of the harsh environmental conditions of typical production processes and the speed at which the process is run. This paper addresses the current and novel coating thickness methodologies for application to chromium coatings on a ferro-magnetic steel substrate with their advantages and limitations regarding in-line measurement. The most common in-line coating thickness measurement method utilized within the steel packaging industry is the X-ray Fluorescence (XRF) method, but these systems can become costly when implemented for a wide packaging product and pose health and safety concerns due to its ionizing radiation. As technology advances, nanometer-scale coatings are becoming more common, and here three methods are highlighted, which have been used extensively in other industries (with several variants in their design) which can potentially measure coatings of nanometer thickness in a production line, precisely, safely, and do so in a non-contact and non-destructive manner. These methods are optical reflectometry, ellipsometry and interferometry.

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“…To increase the accuracy of the FDTD simulations, the refractive index for nitinol was measured using an ellipsometer (Note S1 and Figure S4, Supporting Information). [26] According to the geometry revealed by scanning electron microscopy (SEM) images, the nanoholes were modeled in three dimensions. Symmetric boundary conditions (i.e., a period of 700 nm) reduced the numerical Actuation stress (MPa) 0.005-0.05 [21,23] 0.75 [9] Diameter (µm) 1-5 25…”

Section: Tuning Light Absorptance At Spectrummentioning

confidence: 99%

Surface Nanopatterned Shape Memory Alloy (SMA)‐Based Photosensitive Artificial Muscle

Kim

Lee

Cho

et al. 2021

Advanced Optical Materials

Self Cite

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Light‐driven shape memory alloy (SMA)‐based microscale actuators show great promise for artificial muscle and biomedical applications, as they are actuated remotely and have a fast response speed. However, ultraviolet (UV) light is required for device actuation; thus, the operating environment has been limited. Here, an infrared (IR) light‐driven SMA actuator is proposed, in which the plasmonic effect is used to enhance IR light absorptance. A sub‐micrometer pattern is used to create an optical meta‐surface capable of tuning the light absorptance. Conical nanohole arrays are fabricated with a focused ion beam. The absorptance tuning effect is evaluated in terms of the optical characteristics and performance of the actuator. The nanopatterned surface increases the narrow‐band IR light absorption by up to 55%. Optics simulations are conducted to verify the experimental results. A pattern design method is proposed, based on the light wavelength of the stimulating source. Combining heterogeneous surfaces, both UV and IR light achieve decoupled microscale actuation. These actuators show a response similar to that of the iris muscle, which is responsible for the eye's pupillary reflex. It is expected that these actuators will broaden SMA applications in clinical devices and soft robotics.

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Volumetric thin film thickness measurement using spectroscopic imaging reflectometer and compensation of reflectance modeling error

Cited by 27 publications

References 15 publications

Simulation of thin film thickness distribution for thermal evaporation process using a scanning linear source

Simulation of thin film thickness distribution for thermal evaporation process using a scanning linear source

Continuous In-Line Chromium Coating Thickness Measurement Methodologies: An Investigation of Current and Potential Technology

Surface Nanopatterned Shape Memory Alloy (SMA)‐Based Photosensitive Artificial Muscle

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