MACHINE LEARNING (ML)-BASED PROCESS PROXIMITY CORRECTION (PPC) METHOD AND SEMICONDUCTOR DEVICE MANUFACTURING METHOD INCLUDING THE SAME: abstract simplified (17990900)

• This abstract for appeared for patent application number 17990900 Titled 'MACHINE LEARNING (ML)-BASED PROCESS PROXIMITY CORRECTION (PPC) METHOD AND SEMICONDUCTOR DEVICE MANUFACTURING METHOD INCLUDING THE SAME'

Simplified Explanation

This abstract describes a machine learning-based method for correcting patterns in the manufacturing of semiconductor devices. The method involves analyzing a layout of the device, extracting features from a specific pattern, and using machine learning to create a prediction model. The model is then used to generate a target layout that maximizes the process margin. The original layout is corrected to match the target, and the prediction model is used to predict the outcome of the manufacturing process based on the corrected layout.

Original Abstract Submitted

A machine learning (ML)-based process proximity correction (PPC) method includes receiving a first layout of an after clean inspection (ACI) including patterns for manufacturing a semiconductor device, extracting features of a first pattern from the first layout, generating a prediction model through ML based on the features of the first pattern, generating an ACI target having a maximum process margin by comparing an upper limit value and a lower limit value of the ACI for at least one condition, generating a second layout of an after development inspection (ADI) by correcting the first layout to correspond to the ACI target, and predicting the ACI through the prediction model, based on the second layout of the ADI.