Taiwan Semiconductor Manufacturing had one patents in big data during Q2 2024. The patent describes a machine learning system for optical critical dimension measurement using regression analysis and feature functions to generate predictor variables. The system analyzes wafers using metrology tools, calculates critical dimension inferences, compares measured spectra to theoretical spectra, and outputs results in human readable form. GlobalData’s report on Taiwan Semiconductor Manufacturing gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Taiwan Semiconductor Manufacturing had no grants in big data as a theme in Q2 2024.
Recent Patents
Application: Semiconductor metrology system and method (Patent ID: US20240193473A1)
The patent filed by Taiwan Semiconductor Manufacturing Co Ltd describes a machine learning system and method for optical critical dimension measurement. The system involves extracting features from a training set of spectra and references, performing regression analysis to generate predictor variables, and using feature functions, inverse feature functions, a machine-learning predictor component, and masks to create a machine-learning optical critical dimension explainer. This explainer is used to analyze wafers, calculate critical dimension inferences, generate theoretical spectra, compare measured spectra to theoretical spectra, and output results in human-readable form.
The method outlined in the patent includes retrieving a training set of optical spectra and references, extracting features, performing regression analysis, generating predictor variables, and outputting a visual representation of optical critical dimension measurements. It also involves applying feature functions, generating masks, and using machine learning algorithms such as neural networks, FFT, PCA, SVM, and others. Additionally, the method includes performing optical measurements on wafers, calculating critical dimension inferences, generating theoretical spectra, evaluating fitting, and outputting acceptance indicators based on the evaluation. The system further allows for simulating modifications in critical dimensionality, comparing symbolic spectra to theoretical spectra, and evaluating fitting quality for simulated changes.
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