De-noising using multiple threshold-expert machine learning models
US12046299B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 1, 2023 |
| Grant date | Jul 23, 2024 |
| Priority date | — |
| Expiry date | Mar 1, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG11C16/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods of the present disclosure may be used to improve equalization module architectures for NAND cell read information. For example, embodiments of the present disclosure may provide for de-noising of NAND cell read information using a Multiple Shallow Threshold-Expert Machine Learning Models (MTM) equalizer. An MTM equalizer may include multiple shallow machine learning models, where each machine learning model is trained to specifically solve a classification task (e.g., a binary classification task) corresponding to a weak decision range between two possible read information values for a given NAND cell read operation. Accordingly, during inference, each read sample with a read value within a weak decision range is passed through a corresponding shallow machine learning model (e.g., a corresponding threshold expert) that is associated with (e.g., trained for) the particular weak decision range.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.