Invention Application
- Patent Title: DE-NOISING USING MULTIPLE THRESHOLD-EXPERT MACHINE LEARNING MODELS
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Application No.: US17197617Application Date: 2021-03-10
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Publication No.: US20220293192A1Publication Date: 2022-09-15
- Inventor: AMIT BERMAN , Evgeny Blaichman , Ron Golan , Sergey Gendel
- Applicant: SAMSUNG ELECTRONICS CO., LTD.
- Applicant Address: KR SUWON-SI
- Assignee: SAMSUNG ELECTRONICS CO., LTD.
- Current Assignee: SAMSUNG ELECTRONICS CO., LTD.
- Current Assignee Address: KR SUWON-SI
- Main IPC: G11C16/26
- IPC: G11C16/26 ; G11C16/04 ; G06F11/10 ; G06F3/06

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.
Public/Granted literature
- US11626168B2 De-noising using multiple threshold-expert machine learning models Public/Granted day:2023-04-11
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