Film Transistor Liquid Crystal Display Research Articles

Machine-learning-based sampling inspection under OQC capacity for real-time quality monitoring in the TFT-LCD industry

ABSTRACT In the thin film transistor-liquid crystal display (TFT-LCD) industry, the competition is fierce, and the external failure cost is high. To improve the quality of released products, sampling inspection under outgoing quality control (OQC) is conducted before products are released. Owing to the variety and complexity of inspection items, the task must be carried out manually while realising automation is difficult. Traditional random sampling inspection is limited by sampling quantity, manpower, and material resources. To effectively improve acceptable quality levels, this paper proposes a quality predictive monitoring framework tailored to the production and inspection characteristics of the TFT-LCD industry. This framework includes real-time automatic quality monitoring based on an optimal machine learning prediction model with two-phase feature creation, and it also addresses OQC inspection capacity constraints. Experimental results show the prediction performance based on proposed model is better than the traditional random sampling process. In practice, superior average outgoing quality improves by more than 70% at the same sampling level. In addition, the features of the process can be further controlled and managed based on the prediction model to monitor the key parameters in real time and respond to abnormalities quickly, thereby further improving the quality of released products.

A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction.

A detection and classification machine-learning model to inspect Thin Film Transistor Liquid Crystal Display (TFT-LCD) Mura is proposed in this study. To improve the capability of the machine-learning model to inspect panels' low-contrast grayscale images, piecewise gamma correction and a Selective Search algorithm are applied to detect and optimize the feature regions based on the Semiconductor Equipment and Materials International Mura (SEMU) specifications. In this process, matching the segment proportions to gamma values of piecewise gamma is a task that involves derivative-free optimization which is trained by adaptive particle swarm optimization. The detection accuracy rate (DAR) is approximately 93.75%. An enhanced convolutional neural network model is then applied to classify the Mura type through using the Taguchi experimental design method that identifies the optimal combination of the convolution kernel and the maximum pooling kernel sizes. A remarkable defect classification accuracy rate (CAR) of approximately 96.67% is ultimately achieved. The entire defect detection and classification process can be completed in about 3 milliseconds.

An integrated design strategy of zinc phthalocyanine dyes for LCD dye-based color filters

Currently, dyes are alternative colorants to traditional pigments in color filters (CFs) of thin film transistor liquid crystal display (TFT-LCD). However, the inferior thermal stability remains an unaddressed issue despite their promising applications. Herein, a series of novel zinc phthalocyanine (ZnPc) dyes with different substituent effects were designed and synthesized. The substituent effects on its optical properties, photo-thermal stabilities and solubility were systematically investigated. Among the newly prepared ZnPc dyes, 3-B4, which contains multiple chlorine atoms and bulky substituents decorated with two methoxy groups, showed the best performance in thermal stability and solubility tests. Furthermore, the color films fabricated by using 3-B4 also displayed the highest photo-thermal stability under simulated industrial manufacturing processes. It indicated the feasibility of 3-B4 as colorants for dye-based CFs. We hope that our design strategy for ZnPc dyes is meaningful for exploring dye-based CFs.

Reclaimed water in Taiwan: current status and future prospects

According to the Taiwan Water Resources Agency, Ministry of Economic Affairs, the average water demand shortage is 530.6 million m3 yr−1 during the period of 2011 to 2019, and the situation will worsen in the near future due to global climate change. Therefore, reclaimed water has been an important new water source in Taiwan, particularly for industrial consumers such as high-tech industries in Science Parks. In order to meet the targeted reclaimed water supply of 1.32 million m3 d−1 (CMD) in 2031, Taiwan is focusing on two major reclaimed water sources, including reclaimed water from high water-consuming industries and municipal wastewater treatment plants. This report reviews current technologies used for reclaimed water including units for pretreatment, desalting, polishing, and reclamation. Case studies in Taiwan including reclaimed water from high water-consuming industries such as thin film transistor-liquid crystal display (TFT-LCD) and semiconductor industries, as well as from municipal wastewater treatment plants are presented. The TFT-LCD company Innolux and semiconductor company Advaned Semiconductor Engineering have implemented total recycled water system to recycle and reclaim wastewater from manufacturing processes, achieving a total recycled water of 290 million m3 yr−1 with about 97% recovery and 3.5 million m3 yr−1 with 80% recovery, respectively. The Fengshan reclaimed water treatment plant produces 40,436 CMD reclaimed water from municipal wastewater for the China Steel Cooperation’s steel-making processes, at an overall operation and maintenance cost of 11.5 NT dollars m−3. Meanwhile the Yongkang plant produces 15,500 CMD of reclaimed water for semiconductor and TFT-LCD manufacturing processes at an overall operation and maintenance costs of 25.8 NT dollars m−3, which is due to low urea and boron limits requested by the user. Finally, challenges and future prospects for promoting the use of reclaimed water to meet the targeted supply in 2031 will be discussed.

A Hybrid Method of Frequency and Spatial Domain Techniques for TFT-LCD Circuits Defect Detection

Defect detection is a crucial but challenging task in thin film transistor liquid crystal display (TFT-LCD) manufacturing. Existing vision-based methods focus on either spatial domain or frequency domain with unsatisfactory detection. In view of that, this paper proposes a hybrid template matching method by drawing benefits from both frequency and spatial domain techniques. Under proposal, frequency domain template matching method and saliency detector method are firstly adopted separately to obtain two candidate frequency components associated with defects. In template matching process, a novel selection criterion is taken to improve identifying components associated with local spatial anomaly. Subsequently, the inverse Fourier transform is applied on the intersection of the two candidates to reconstruct the defect regions. In final steps, image entropy in the spatial domain is employed to filter out false detection regions to improve accuracy. To meet industry’s real-time inspection requirement, image partition and multi-threading calculation techniques are introduced into the proposed methodology. Experimental results have shown practical viability of the proposed approach to increase the yield rate of panels through robust defect detection in TFT-LCD manufacturing.

The Effects of Precursors on the Morphology and Chemical Mechanical Polishing Performance of Ceria-Based Abrasives.

Ceria-based abrasives are widely used in precision chemical mechanical polishing (CMP) fields, such as thin film transistor liquid crystal display (TFT-LCD) glass substrates and integrated circuits, because of their excellent physicochemical properties. Rare earth carbonates, as precursors of ceria-based abrasives, directly affect the morphology of ceria-based abrasives, which, in turn, affects the material removal rate (MRR) and the surface roughness (Ra) after polishing. Herein, rare earth carbonates with different morphologies were obtained by adjusting reaction parameters during precipitation, including flake, spindle, and spheroid. Moreover, the phase of precursors was analyzed, and the evolution process of morphology from precursors to ceria-based abrasives was investigated. Furthermore, the effect of precursors on the polishing performance of ceria-based abrasives was explored. The results show that the primary particles of ceria-based abrasives are near-spherical, but the morphology and dispersion of the secondary particles are obviously inherited from precursors. Among them, near-spherical ceria-based abrasives prepared by nearly monodisperse near-spherical precursors show better uniformity and higher dispersion, and they not only achieve the lowest Ra but also obtain a higher MRR of 555 nm/min (9 wt.%) for polishing TFT-LCD glass substrates. The result is significant for the further optimization and application of high-performance ceria-based abrasives.

Product-to-Product Virtual Metrology of Color Filter Processes in Panel Industry

The current thin film transistor liquid crystal display (TFT-LCD) panel industry evolves in transition from volume production to customized production service as a major competition strategy. Owing to the highly mixed production type of small batch sizes and high varieties, metrology data are usually collected too infrequently for building accurate virtual metrology (VM) models. In this paper, a new product-to-product (P2P) VM model to predict photoresist spacer heights in the color filter process of the array sector is developed. First, a position-based random forests model is proposed for the base product. Second, an ensemble model combining the base model and a biased estimation random forests model for the new product of small batch size is proposed to perform P2P VM between different products on the same tool. The real datasets of photoresist spacer heights for different products are used to illustrate the accuracy of the new P2P VM framework in the experimental study. Note to Practitioners—As global competition intensifies, production management needs to be adjusted to adapt swiftly to changes in the time-varying and competitive market. For manufacturing firms, how to operate the manufacturing system effectively and efficiently plays a crucial role in quality and yield improvements. The color filter process in the array sector is of critical importance in the TFT-LCD practice since it essentially dictates the quality level of subsequent processing steps in the cell and assembly sectors. Virtual metrology is a state-of-the-art measure that can be used for monitoring and controlling purposes to maintain the yield and productivity of the color filter process in the highly mixed production environment. An immediate challenge to be faced by the data scientists is how to build accurate virtual metrology models for a variety of product lines separately in color filter manufacturing. A new proposal of product-to-product virtual metrology is first addressed between products, enabling to build appropriate virtual metrology models for new products with small-sized production batches.

TFT‐LCD mura defect visual inspection method in multiple backgrounds

AbstractThe defect of mura of thin film transistor liquid crystal display (TFT‐LCD) panel is small, and the gray change is unknown. In manual detection, mura is easily overlooked, which is time‐consuming and labor‐intensive. These factors resulted in our inability to properly assess and distinguish multiple mura defects on a single image in field inspections. Aiming at the above problems, this article proposes a multibackground TFT‐LCD mura defect visual inspection method. To obtain the optimal algorithm under high‐precision and high‐speed detection and to overcome the problem of missed detection of the YOLOV4‐tiny algorithm, the spatial pyramid pooling module and squeeze‐and‐excitation module are added on the basis of the original framework. On the one hand, the advantage of the improved algorithm in this article is to focus on the key information, so that the information before feature fusion is more abundant. On the other hand, adding spatial pyramid pooling in BackBone enables the network to perform multiscale pooling and fusion on the input feature layer, which greatly enhances the receptive field of the network and enables the network to extract richer feature information. Experimental results show that the improved YOLOV4‐tiny algorithm has an accuracy of 99.72% in TFT‐LCD mura detection, which is 0.99% higher than YOLOV4‐tiny, and the real‐time detection speed reaches 63.84 frames per second (FPS). At the same time, we overcome the problem of missed detection of YOLOV4‐tiny algorithm and meet the detection accuracy and real‐time requirements of TFT‐LCD mura detection tasks in multibackground environment.

Computationally efficient approximate dynamic programming for multi-site production capacity planning with uncertain demands

With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamic programming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based Approximate Dynamic Programming and the Rolling-Horizon-based Approximate Dynamic Programming are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to Stochastic Dynamic Programming.

A Novel Multicategory Defect Detection Method Based on the Convolutional Neural Network Method for TFT-LCD Panels

Defects on thin film transistor liquid crystal display (TFT-LCD) panel could be divided into either macro- or microdefects, depending on if they are easy to be detected by the naked eye or not. There have been abundant studies discussing the identification of macrodefects but very few on microones. This study proposed a multicategory classification model using a convolutional neural network model to work with automatic optical inspection (AOI) for identifying defective pixels on the TFT-LCD panel. Since the number of nondefective pixels outnumbered the defective ones, there exists a very serious class-imbalanced problem. To deal with that, this study designed a special training strategy that worked with data augmentation to increase the effectiveness of the proposed model. Actual panel images provided by a mobile manufacturer in Taiwan are used to demonstrate the efficiency and effectiveness of the proposed approach. After validation, the model constructed by this study had 98.9% total prediction accuracy and excellent specificity and sensitivity. The model could finish the detection and classification process automatically to replace the human inspection.

Automatic defect inspection of thin film transistor-liquid crystal display panels using robust one-dimensional Fourier reconstruction with non-uniform illumination correction.

Automatic inspection of micro-defects of thin film transistor-liquid crystal display (TFT-LCD) panels is a critical task in LCD manufacturing. To meet the practical demand of online inspection of a one-dimensional (1D) line image captured by the line scan visual system, we propose a robust 1D Fourier reconstruction method with the capability of automatic determination of the period Δx of the periodic pattern of a spatial domain line image and the neighboring length r of the frequency peaks of the corresponding frequency domain line image. Moreover, to alleviate the difficulty in the discrimination between the defects and the non-uniform illumination background, we present an effective way to correct the non-uniform background using robust locally weighted smoothing combined with polynomial curve fitting. As a proof-of-concept, we built a line scan visual system and tested the captured line images. The results reveal that the proposed method is able to correct the non-uniform illumination background in a proper way that does not cause false alarms in defect inspection but also preserves complete information about the defects in terms of the brightness and darkness as well as the shape, indicating its distinct advantage in defect inspection of TFT-LCD panels.

Using a mobile device for margin assessment of specimen mammography in breast-conserving surgery.

To compare the performance of margin assessment of specimen mammography (SM) in patients with breast-conserving surgery (BCS) on mobile devices and 5-megapixel (5M) thin film transistor liquid crystal display (TFT-LCD) monitors based on the safety margin for pathologic results.This retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. A total of 105 consecutive breast cancer SM samples from 104 women who underwent BCS were included in the study. The SM were independently reviewed by two radiologists using mobile devices and by two additional radiologists using 5M TFT-LCD monitor. Each reader was asked to measure the shortest distance between the lesion and the lesion margin. The interpretation time was recorded. The sensitivity, specificity, and interobserver agreement were analyzed.In total, 19% (20/105) breast specimens had a positive surgical margin (<1 mm). The mean absolute difference from the pathologic margin was 0.60 ± 0.57 cm and 0.54 ± 0.47 cm using the 5 M TFT-LCD monitor and the mobile device, respectively (without any statistical significance, P = .273). The mean interpretation time was 49.5 and 47.6 s for the 5M TFT-LCD monitor and the mobile device, respectively (P = .012). The pooled sensitivity and specificity were 60% and 74% for 5M TFT-LCD monitor, and 60% and 69% for the mobile device (P = 1.00 and P = .190, respectively). The kappa coefficient indicated moderate agreement for both the displays.The diagnostic performance for margin assessment of SM in BCS patients on mobile devices and 5M TFT-LCD monitors are showed not statistically difference. The findings of the study provide evidence of the benefit of the mobile device for SM interpretation in patients who underwent BCS. However, a large sample size study is warranted before using a mobile device for margin evaluation on SM.The mobile device showed comparable diagnostic performance with 5M TFT-LCD monitor in the evaluation of SM margin in patients with BCS and could be used as a display tool for immediate assessment when a dedicated LCD monitor is unavailable.

P‐6.3: Analysis and Research of Pad Corrosion of COG LCD

As for the TFT‐LCD (Thin Film Transistor Liquid Crystal Display),pads corrosion of COG (Chip on Glass) are incident to happen in product line. And if the Q‐time (queue time) is over‐time between cleaning and bonding process, the pad area of single panel which kept in air may have pad corrosion problems. On the analysis of pads corrosion, we found that the corrosion layers are mainly ITO and Copper of source /gate, which are associated with the pad surface's residual water and particle. In addition, we also found that cleaning water and the detergent have a great relevance with the corrosion by simulation experiments, and the corrosion can be accelerated by ions in water stains. Meanwhile, we put forward some countermeasures to solve this problem.

크리스탈 사이클과 수요, 기술의 공진화: TFT-LCD 산업의 역사 친화적 모형

This paper aims at building a history-friendly model which replicates the history of the Thin Film Transistor-Liquid Crystal Display (TFT-LCD) industry focusing on the industrial business cycle(crystal cycle) and co-evolution of demand and technology. The model is distinctive compared to existing models addressing industrial business cycles in two points: 1) endogenous demand and technological change are incorporated 2) industrial cycle affects evolution of demand and technology as well as are caused by the change of demand and technology, that is, we consider industrial business cycle as a medium of co-evolution of demand and technology. The simulation results show that it can successfully replicate the crystal cycle, and investment-production delay, price delay and endogenous change of demand are factors of the crystal cycle. Moreover, we can see positive feedback between demand expansion and technological advance through medium of the cycle.

Comparison of the melting properties of glass substrates for thin film transistor liquid crystal display and organic light-emitting diode

ABSTRACT The melting properties of two kinds of glass substrates for thin film transistor liquid crystal display (TFT-LCD) and organic light-emitting diode (OLED) were investigated. Chemical reaction process of the batches was similar for two substrates determined by DSC-TG test. The ascending behavior of bubbles with different fining agents of two substrates were studied by High-temperature imaging observation (HTO) test. Viscosity and surface tension for the glass melt were investigated. The results showed that tin dioxide (SnO2) exhibited better fining effect than stannous oxide (SnO), both for TFT-LCD and OLED substrates. For OLED substrate, it took more time to approximate the steady state for fining process due to its higher viscosity and higher surface tension. The molten resistance of these two substrates was also determined, which could provide some reference for the design of electrode in mass production. And the effect of grain size distribution of main raw material silica sand on the melting quality of bulk glass samples was also developed. Silica sand with 150–200 mesh exhibited the best melting and fining effect.

P‐79: An Ultra Low Power Optimization Design for TFT‐LCD

In order to further reduce the total power consumption of thin film transistor liquid crystal display (TFT‐LCD) and improve the endurance level of notebook, this paper describes an ultra low power module based on the design of Oxide high transmittance, low power circuit driver and high brightness backlights. It has successfully passed the low‐power certification of Intel LPDT 2.1 and is measured at 0.93@200nit This product can meet the production requirements of 8.5G factory and has passed the reliability test. It has the capacity of mass production and has a high guiding significance for the low‐power products of TFT‐LCD.

Exposure to Indoor Volatile Organic Compounds and Hypertension among Thin Film Transistor Liquid Crystal Display Workers

This cross-sectional study aimed to determine the concentration of indoor volatile organic compounds (VOCs) and to investigate the association between indoor VOCs exposure and the prevalence of hypertension among thin film transistor liquid crystal display (TFT-LCD) workers. A total of 20 canisters were used to collect VOCs samples in the array, cell and module areas over 12 hours and VOCs concentrations were analyzed by the gas chromatography with mass spectrum. Individual information of health examination and lifestyles by self-administrated questionnaire were provided by 155 volunteers. The multivariate regression models were used to evaluate the associations between VOCs exposure, blood pressure and the prevalence of hypertension. The four dominant VOCs were ethanol (1870.8 ± 1754.0 ppb), acetone (689.9 ± 587.4 ppb), isopropyl alcohol (177.1 ± 202.3 ppb) and propylene glycol monomethyl ether acetate (98.2 ± 100.8 ppb), which were identified with the highest level in the module area for ethanol and acetone and in the array area for the others. Subjects exposed to a total level of ethanol, cyclohexanone and toluene ≥ 2500 ppb had an increased systolic blood pressure of 5.95 mmHg (95% confidence interval: 0.20–11.71; p = 0.043) compared with those exposed to &lt;2500 ppb. Exposure to mixed VOCs in the indoor environment might be associated with elevated blood pressure among TFT-LCD workers.

Analysis and improvement of the color anomaly in a‐Si TFT channel with new 4‐mask process

In new 4‐mask manufacturing process of the thin film transistor (TFT) liquid crystal display (LCD), the copper in the channel would be damaged by plasma gas, which would cause a negative impact on the subsequent processes, and lead to abnormal channel color. This paper investigates the formation mechanism of the abnormal channel color. In the 1st dry etching, the photo resist (PR) reactes with Cl2, and produced some residual after the 2nd wet etching, which is hard to etch or wash. As a result, the content of SiOx on the surface where there is no residue is higher than the place where the residue exists in O2 plasma process, hence leading to the differences of the silicon etching rate and forming the line‐shape pits at the edge of the channel. Therefore, TFTs under optical microscope show abnormal channel color. Through removing Cl2 or skipping the plasma treatment before 2nd dry etching we could modify this issue.

Exploring Industrial Knowledge Flow for Identifying Technological Development Strategy: The Case of Korea’s TFT-LCD Industry

Korea’s technological achievement in the thin film transistor liquid crystal display (TFT-LCD) industry became possible primarily through a catch-up strategy in technological development. Through qualitative and quantitative analyses of the Korean TFT-LCD industry’s knowledge management, the researchers highlight the strategies that key Korean firms utilized to enable a successful technological catch-up. On a national level, patent citation information is analyzed to evaluate the Korean industry’s technological knowledge position compared to other Asian countries. Results from this analysis indicate that Korea plays a central role within the knowledge network, as evidenced by the highest centrality value. On a firm level, Samsung and LG Display are investigated in the areas of technological development and manufacturing strategies. Samsung and LG Display show strength in the technological specialization of devices’ arrangements/optical operation and arrangement/circuits of control indicating devices. In addition, these two firms show more diversification to allow for new business opportunities. By integrating quantitative and qualitative methods to analyze the TFT-LCD industry on national and firm levels, this research demonstrates the industry’s trajectory with depth and precision. In addition, this study contributes a meaningful strengthening of analysis methods in technological knowledge management and provides successful management factors for new latecomers in the industry.

Detection of Conductive Particles in TFT-LCD Circuit Using Generative Adversarial Networks

The inspection of conductive particles is a crucial step in the Thin Film Transistor Liquid Crystal Display (TFT-LCD) circuit detection process since only high-quality deformed particles have the conductive effect in the circuit. The main task of detecting conduction particles is to locate and count the valid particles accurately, which is a high challenge due to various difficulties such as the uneven illumination, different sizes to aggregation and overlap between particles, etc. Traditional detection algorithms need to manually set a large number of artificial thresholds, which limits their adaptability. As a result, effective automatic detection of conductive particles is strongly motived in industry. In this paper, a novel particle detection algorithm based on generative adversarial networks (GAN) is proposed for TFT-LCD circuit inspection system. The backbone architecture of the generator is based on a compact end-to-end neural network with multi-scale convolution blocks for well utilizing the multiscale spatial features. And the discriminator is designed to detect and correct high-order inconsistencies for real-fake images. Moreover, Coarse to Fine training strategy and Loss functions Coordination strategy are further proposed to improve the detection quality. The experiments on the real dataset demonstrate the effectiveness of the proposed methods for the detection of valid conductive particles compared to the state-of-the-art methods.