Current best practices for the extraction of critical dimensions (CDs) from microscopic images requires semiconductor process engineers to analyze images one by one, which is tedious, prone to human bias, time-consuming and expensive. Automated CD extraction using machine learning methodologies is an approach to accelerate and improve the accuracy of this process. Deep learning convolutional neural nets specifically can be used effectively for image segmentation and identification of different material regions; however, providing enough annotated data for training and testing is an ongoing challenge. Here, we demonstrate a method where only one sample image is needed for the neural net to learn how to extract the CDs of interests. The methodology is specifically demonstrated for extracting CDs from a metal assisted chemical etching process. Each experimental SEM image is automatically measured in about 45 seconds. The extracted CD measurements are within 4 nm (<5% error) of the human measured CDs. This automated extraction enables process engineers to improve the accuracy of their metrology workflow, reduce their total metrology costs, and accelerate their process development.
Semiconductor process engineers currently spend almost 10% of their time extracting critical dimensions from microscope images. Images are analyzed one by one, which is tedious, prone to human bias, time-consuming and expensive. Accurate, automated detection of edges and different materials in a stack are the key technical challenges for computer-extracted critical dimensions (CDs). Here we demonstrate the performance of a method for edge detection and material detection via segmentation methods embodied in the software tool Weave™. This-approach uses optimized thresholding via a level set method to identify multiple edges and materials without the need of extensive, annotated, experimental training data. The method is evaluated based on accuracy (prediction of CDs) and materials identification (ability to identify the different materials in an image). Based on evaluation of the method with 20 test SEM images, the method’s performance is excellent. Ninety percent of the CDs measured from the automated analysis are within 2% of the actual values. The errors for the remaining 10% of measurements range from 4-9%.
KEYWORDS: Process modeling, Etching, Optimization (mathematics), Model-based design, 3D modeling, Calibration, Statistical modeling, Statistical analysis, Space mirrors, Process engineering
A method for automated creation and optimization of multistep etch recipes is presented. Here we demonstrate how an automated model-based process optimization approach can cut the cost and time of recipe creation by 75% or more as compared with traditional experimental design approaches. Underlying the success of the method are reduced-order physics-based models for simulating the process and performing subsequent analysis of the multi-dimensional parameter space. SandBox Studio™ AI is used to automate the model selection, model calibration and subsequent process optimization. The process engineer is only required to provide the incoming stack and experimental measurements for model calibration and updates. The method is applied to the optimization of a channel etch for 3D NAND devices. A reduced-order model that captures the physics and chemistry of the multistep reaction is automatically selected and calibrated. A mirror AI model is simultaneously and automatically created to enable nearly instantaneous predictions across the large process space. The AI model is much faster to evaluate and is used to make a Quilt™, a 2D projection of etch performance in the multidimensional process parameter space. A Quilt™ process map is then used to automatically determine the optimal process window to achieve the target CDs.
KEYWORDS: Etching, Calibration, 3D modeling, Process modeling, Solid modeling, Silicon, Model-based design, Data modeling, 3D acquisition, Visual process modeling
We present a model-based experimental design methodology for accelerating 3D etch optimization with demonstration on 3D NAND structures. The design and optimization of etch recipes for such 3D structures face significant challenges requiring costly and time-consuming experiments in order to achieve the required tolerances. 3D NAND memory devices additionally require accurate nanofabrication of high aspect ratio trenches and isolation slits, which are challenging to manufacture reliably within specifications. Our model efficiently captures the relevant physical and chemical processes, which allows them to be calibrated using a limited number of experimental samples and can reproduce realistic 3D etch of multilayer materials, including bowing, necking, and tapering. Since our GPU-powered simulations run in a matter of minutes, the relevant process parameter space can be explored extensively in a short amount of time. The calibrated physics-based model can be used to train adaptive machine-learning-based heuristics which enable near-instant queries, for example for data visualization and analytics. With this approach, we show a rapid methodology for locating optimal windows in the process parameter space for etching 3D structures. Optimality metrics under consideration include both conformances to specified tolerances as well as robustness against process parameter variations. These techniques can reduce cost and time to market for complex multi-layer three-dimensional device designs and improve semiconductor device yields.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.