Machine learning-based semiconductor manufacturing yield prediction system and method
US11494636B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 29, 2017 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | Apr 2, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02P90/30
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Provided is a machine learning-based semiconductor manufacturing yield prediction system and method. A result prediction method according to an embodiment of the present invention comprises: learning different neural network models by classifying different types of data according to their types and respectively inputting the classified different types of data to the different neural network models; and predicting result values by classifying input data according to their types and respectively inputting the classified input data to different neural network models. Therefore, it is possible to apply different neural network models to respective data according to their types, thereby ensuring a neural network model having a structure appropriate for the characteristics of each type of data and thus accurately predicting a result value.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.