With new technology development and adoption in AI, electrical vehicle, telecommunication, IoT and etc, the semiconductor industry is expected to have 8% CAGR from 2022 to 2030 [1]. As a result, design starts and corresponding photomasks are also expected to experience a significant growth. In the past a few years, the mask making industry has experienced severe capacity shortage. The mask lead time became 2 ro 3 months, which is 4 to 6 times longer than previous years. The lead time is largely limited by the challenge to increase mask supply capacity in a short time. Moreover, the obsolescence of legacy mask inspection tools makes it more difficult to support semiconductor growth in near future. The industry has been paying more effort to address the legacy tool obsolescence by offering new models or upgrades. Mask inspection tools play a critical role in mask shops and fabs in mask quality control. The mask inspection tools made in 20 to 30 years ago, such as KLA 3XX/SLF tools, are still the workhorse in our mask shop for mature node mask manufacturing. In this work, we’ve evaluated KLA’s large pixel upgrade on the existing TeraScanTM 5XX platform. It supports all existing inspection modes including die-to-die, die-to-database and SL. We’ve demonstrated that the large pixel P186 upgrade is able to meet our sensitivity and false requirement of our mature mode manufacturing. More importantly, the upgrade provides throughput improvement to replace 3XX/SLF capability in our mask shop. Each 5XX equipped with P186 is able to replace up to two 3XX/SLF tools for our mask shop use cases.
KEYWORDS: Data conversion, Machine learning, Inspection, Electronic design automation, Manufacturing, Photomasks, Data modeling, Education and training, Image classification, Data processing
In the photomask manufacturing industry, photomask source design data needs to be converted into several different target formats, such as MEBES fracture, writer file, die-to-database inspection data etc. Due to the various conversion needs in the manufacturing flow, different EDA tools from different software vendors are employed during conversion. Two different EDA tools that are given the same input can result in slight differences in the output pattern and this will lead to causation of CD errors relative to the underlying pattern tolerances and/or specifications. During the photomask production process, it is very challenging to identify and classify these small differences in the output pattern caused by the conversion of data. In this study, we developed a novel solution to alert on pattern discrepancy by utilizing the classification generated by the application of machine leaning techniques and Smart-MRC tools. A Convolutional Neural Network (CNN) model is being introduced in this study and is trained by learning pre-classified data and classification result would be generated after inputting data to the CNN model. This new Mask Data Preparation (MDP) technique is expected to reduce the use of human resources in the classification process and will bring significant enhancement to our data validation steps to ensure pattern integrity across the entire photomask manufacturing tool chain. Furthermore, the risk of anomalies introduced by updating EDA software tools and their respective version is also mitigated.
ALTA 4700DP, a new design laser mask pattern generator (LMPGs), is constructed with multi-core CPU server. Different from the traditional hardware-based data path LMPGs, the integrated software features of ALTA 4700DP provide the compensation function for critical dimension (CD) variation which caused from the post-exposure processing of the plate. The process includes the post-exposure bake, the developing of the photoresist, and the etching of the chrome. Through the correction of the density-dependent errors and process-footprint errors, the global CD uniformity can be improved.
KEYWORDS: Transmission electron microscopy, Photomasks, 3D modeling, Metrology, 3D metrology, Scanning electron microscopy, 3D image processing, Industry, 3D mask effects, Semiconducting wafers
Explore a method for measurement the sidewall angle of photomask patterns using the CD-SEM (critical dimension scanning electron microscope) device. This tool is widely used in the semiconductor industry for metrology CD measurements and is one of the most common inspection methods. The CDSEM tool ZX has advanced techniques that not only measure line width but also build a 3D model of the scan. The CDSEM tool measures the sidewall angle by determining the top and bottom positions of the photomask pattern, and using the data obtained from these positions, the distance of the horizontal and vertical can be calculated. These data are then used in an algorithmic equation to simulate the slope value and calculate the sidewall angle. To verify the correctness of the sidewall angle value obtained through CDSEM measurements, TEM (transmission electron microscopy) is used. TEM is an intuitive method that uses a high-energy electron beam to capture highresolution cross-sectional images of larger materials such as photomasks. TEM is a common method for analyzing the sidewall angle and thickness of thin films and is widely used in material science and nanotechnology. However, TEM's cross-section implementation is a destructive method and is not an ideal method for testing product photomasks. In this study, the simulated data from CDSEM and actual cross-sectional data from TEM are collected and integrated for crosscomparison to obtain the corresponding relationship.
Deep-ultraviolet (DUV) laser writer ALTA4700DP upgraded from ALTA4700. The new design laser mask pattern
generator (LMPGs), advance electronic design automation (EDA), and multi-core CPU server (up to hundreds of core)
are all constructed on the machine. The effective of machine (exposure time) and CD performance (uniformity, thr-pitch and corner rounding) of products are obvious reduced and improved, respectively.
In research, not only the CD performance of mask was obtained by CD-SEM but also the CD performance of wafer
was defined by AIMS simulation. The effect of corner rounding was proved by average CD and exposure intensity of
contact structure by both CD-SEM measurement and AIMS simulation.
Mask Data Preparation, MDP contains comprehensive contents such as reticle layout, mask fracturing, manufacture rule check, etc. We have demonstrated a MDP application on KLA DB inspection. In order to calculate the rendering compensation, Pre-Swath Calibration, PSC is performed before DB inspection. We have built a rule on PSC point searching function which combines the density function of Smart MRC with algorithm. The result shows that this function could save the PSC selection time and also improvement the fail rate of PSC selection. A well-prepared inspection recipe could be done by automation tool. Therefore, the setup time on DB inspection could be minimized, also could save inspection resource as well.
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