Overview

Calibre Stress Correction

Calibre Stress Correction uses both machinelearning and traditional physical compact models to efficiently predict the shift values on each of the trenches and correct for the trench distortion to ensure manufacturability for 3D memory devices.  


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silicon wafer reflecting different colors.

Key Features

Calibre Stress Correction analyzes and corrects distortion of the deep trenches cut through 3D stacks for advanced memory devices.  

Full-chip trench shift prediction for stress correction

Calibre Stress Correction conducts full-chip trench shift prediction and executes the required shape alignment. Both the machine learning engine and the stress correction modeling engine can handle billions of trench shifts for full-chip usage. 

Full-chip prediction capability for trench shift

Machine learning intelligence for complex resolution

Calibre Stress Correction is equipped with a neural network machine learning (ML) model that learns historical trench shift data from previous tape-out regardless of the complicated cause of the shift. ML model learning provides a straightforward way to predict the shift values for stress correction on a new layout with similar design rules and the same process.

An application of Neural Network Machine Learning

Model-based prediction without needing historical data

Calibre Stress Correction performs shift prediction by using a compact physical model when the previous shifting information is unavailable. The shift values can be predicted based on the physical parameters in the model and the surrounding layout information without any requirement from the previous shift measurements. 

Optional compact physical model for stress correction

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