Memory Read Voltage Estimation for Threshold Variation Control
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Solution Overview
Problem
Existing memory systems face challenges in accurately determining the read levels for memory cell transistors due to variations in threshold voltages, leading to increased fail bit counts and reduced quality of service.
Innovation Solution
A memory system that utilizes a trained machine learning model to estimate and adjust read levels by employing a first and second acquisition operation, incorporating a first estimator and a second estimator to enhance accuracy and reduce fail bit counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predetermined read levels are applied to memory cell transistors for sensing operations, then data can be read from memory cells, but variations in threshold voltages lead to inaccurate determination of ON/OFF states and increased fail bit counts
Solution Approach 1:
The system performs preliminary actions by acquiring a data set corresponding to the distribution of threshold voltages before actual data reading operations. This preliminary data acquisition enables the system to estimate optimal read levels in advance, adjusting for threshold voltage variations and thereby improving both measurement precision and reading reliability in subsequent operations.
Solution Approach 2:
The system implements feedback mechanisms by using acquired data sets to continuously estimate and adjust read levels. The controller uses the data set information about threshold voltage distributions to refine read level selections, creating a closed-loop system where reading accuracy is continuously improved based on observed performance and threshold characteristics.
2Measurement precision
If multiple acquisition operations are used to estimate read levels, then accuracy of read level determination is improved, but system complexity increases
Solution Approach 1:
The system segments the read level estimation process into multiple distinct acquisition operations, each targeting specific aspects of threshold voltage distribution. By dividing the complex estimation task into manageable segments (first acquisition operation for initial estimation, second acquisition operation for refinement), the system achieves high accuracy while maintaining operational clarity and control.
Solution Approach 2:
The system changes parameters by employing different acquisition operations with varying characteristics to estimate read levels. Each acquisition operation uses different parameters or methods to gather data about threshold voltage distributions, allowing the system to adaptively select the most appropriate estimation approach based on current memory cell characteristics and operating conditions.
Data Source
AI summary
A memory system includes a plurality of memory cells and a controller. The controller is configured to acquire a data set corresponding to a distribution of threshold voltages of the plurality of memory cells by reading the plurality of memory cells using a reference read voltage; select one from a plurality of acquisition operations of acquiring an actual read voltage for reading data stored in the plurality of memory cells based on the data set, wherein the plurality of acquisition operations include a first acquisition operation of acquiring the actual read voltage from the data set using a first trained machine learning model and a second acquisition operation different from the first acquisition operation; and acquire the actual read voltage using the selected acquisition operation and read the plurality of memory cells using the acquired actual read voltage.