Mobile Memory Controller Encoding for Noisy Multi-Bit NAND
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Solution Overview
Problem
NAND flash memory devices storing multiple bits per cell face increased susceptibility to noise, which requires higher computational power to compensate, hindering performance in mobile devices.
Innovation Solution
A mobile electronic device with a memory controller that includes an error correction code encoder, constrained channel encoder, reinforcement learning pulse programming component, expectation maximization signal processing component, constrained channel decoder, and ECC decoder to encode and decode data while mitigating noise in multi-bit memory cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple bits are stored per memory cell, then manufacturing cost and storage density are improved, but noise susceptibility increases and dynamic voltage range decreases
Solution Approach 1:
The patent segments the memory system into multiple components: multi-level memory cells for high-density storage, constraint channel encoders for noise mitigation, and expectation maximization signal processing for error correction. This segmentation allows each component to specialize in addressing specific aspects of the contradiction.
Solution Approach 2:
The patent introduces intermediary processing components (constraint channel encoders and expectation maximization signal processing) that act as mediators between the high-density memory cells and the data readout, filtering out noise and correcting errors without compromising storage density.
2Reliability
If computational power is increased to compensate for noise, then data reliability is improved, but power consumption and performance in mobile devices deteriorate
Solution Approach 1:
The patent applies preliminary action by performing constraint channel encoding before data is written to memory cells. This preprocessing step prepares the data in advance to be more resistant to noise, reducing the need for heavy computational power during read operations and thereby lowering overall power consumption.
Solution Approach 2:
The expectation maximization signal processing component implements feedback by iteratively refining data estimates based on detected errors and noise patterns. This feedback mechanism improves data reliability through multiple passes of correction rather than requiring excessive computational power in a single pass.
3Reliability
If computational power is increased to compensate for noise, then error correction capability is improved, but device performance and speed are hindered
Solution Approach 1:
The error correction functionality is segmented into specialized components (constraint channel decoders and expectation maximization signal processing) that operate independently and efficiently, avoiding the need for general-purpose high-power processing that would slow down device performance.
Solution Approach 2:
The patent changes parameters by using optimized encoding schemes and signal processing algorithms that achieve high error correction capability with reduced computational complexity, thereby maintaining fast device performance while improving reliability.
Data Source
AI summary
A mobile electronic device may include a memory device and a memory controller including an error correction code (ECC) encoder to encode data, a constrained channel encoder configured to encode an output of the ECC encoder based on one or more constraints, a reinforcement learning pulse programming (RLPP) component configured to identify a programming algorithm for programming the data to the memory device, an expectation maximization (EM) signal processing component configured to receive a noisy multi-wordline voltage vector from the memory device and classify each bit of the vector with a log likelihood ration (LLR) value, a constrained channel decoder configured to receive a constrained vector from the EM signal processing component and produce an unconstrained vector, and an ECC decoder configured to decode the unconstrained vector. A machine learning interference cancellation component may operate based on or independent of input from the EM signal processing component.