Flash Read Channel Parameter Estimation Using Machine Learning
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Solution Overview
Problem
Existing solid-state storage devices face challenges in optimizing read channel parameters, such as reference threshold voltage and log-likelihood ratio (LLR) values, which affect bit error rate (BER) and error correction decoding performance, due to limited information usage and inefficient estimation methods.
Innovation Solution
A machine learning apparatus is employed to estimate read channel parameters using signal count metrics and syndrome weights as inputs, allowing for optimized reference threshold voltage and LLR value determination for improved read channel performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional parameter estimation methods are used for read channel, then device complexity is reduced, but bit error rate increases and read performance deteriorates
Solution Approach 1:
The patent introduces a machine learning apparatus as an intermediary component between the read channel and error correction decoder. This apparatus receives signal count metrics and syndrome weights as inputs, processes them through trained machine learning models, and outputs optimized read channel parameters. The intermediary enables sophisticated parameter estimation without requiring complex custom circuitry, thus improving BER while managing device complexity through software-based solutions.
Solution Approach 2:
The patent dynamically changes read channel parameters (such as reference threshold voltage and LLR values) based on real-time signal conditions. The machine learning apparatus estimates optimal parameter values by analyzing signal count metrics and syndrome weights, then adjusts parameters accordingly. This parameter adaptation allows the system to optimize read performance for different storage conditions, wear levels, and signal qualities, significantly reducing bit error rate compared to fixed parameter approaches.
2Measurement precision
If limited information is used for parameter estimation, then processing speed is maintained, but parameter accuracy deteriorates and read performance decreases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using extensive datasets that capture various storage conditions, wear patterns, and signal characteristics. The trained models are then deployed in the read channel apparatus, enabling fast real-time parameter estimation without requiring complex online computations. This preliminary preparation allows the system to achieve high parameter estimation accuracy while maintaining processing speed during actual read operations.
Solution Approach 2:
The patent uses copying by training machine learning models on comprehensive datasets that replicate various storage conditions and then using these trained models to estimate parameters for actual read operations. The model learns from copied training data patterns and applies this knowledge to real-time parameter estimation, achieving high accuracy without processing all possible condition variations during operation.
3Adaptability or versatility
If generic parameter values are used for read channel, then device complexity is minimized, but adaptability to different storage conditions deteriorates and performance is suboptimal
Solution Approach 1:
The patent implements dynamics by transitioning from static generic parameter values to dynamic parameter estimation. The machine learning apparatus continuously estimates optimized parameters based on real-time signal count metrics and syndrome weights, allowing the read channel to adapt to varying storage conditions, wear levels, and signal qualities. This dynamic approach enables the system to maintain optimal performance across different operating conditions without requiring complex hardware modifications.
Solution Approach 2:
The patent applies self-service by enabling the read channel apparatus to automatically estimate and adjust its own parameters using machine learning. The system processes its own signal count metrics and syndrome weights to generate optimized parameter values, eliminating the need for external calibration or manual adjustment. This self-service capability provides adaptability to different storage conditions while keeping the system architecture relatively simple.
Data Source
AI summary
Estimation of read parameters for a read channel of a solid-state storage device using a machine learning apparatus. The machine learning apparatus may be provided with signal count metrics from multiple regions of the memory cell signal space and syndrome weights from an error correction code. Other inputs may also be provided comprising metrics of the memory or read operations. In an example, the read parameters may include one or more reference threshold voltage values for read voltages applied to a memory cell and/or log-likelihood ratio (LLR) values for the memory cell.