Samsung Electronics Co., Ltd.

Elisha Halperin

Amit Berman

Evgeny Blaichman

Jonathan Zedaka

Dan Elbaz

Systems, devices, and methods for decoding information bits obtained from storage, including obtaining a plurality of data symbols; providing the plurality of data symbols to a neural network; obtaining a plurality of threshold voltage targets based on an output of the neural network; and programming the plurality of data symbols to a plurality of memory cells included in a storage device based on the plurality of threshold voltage targets.

Reviving faulty NAND by AI data modulation

A NAND memory device that includes a plurality of blocks, each block comprises a plurality of wordlines and an associated agent, and each wordline comprises a plurality of cells and a plurality of voltage levels and an associated agent, and each voltage level comprises an agent. A method of programming the NAND memory device includes receiving, by an agent at a given rank in the plurality of ranks, parameters from a higher rank agent in the hierarchy of ranks and a state from the memory device; determining, by the agent, an action from the parameters and the state; passing the action as parameters to a lower rank agent in the hierarchy of ranks; and updating the agent based on a reward output by the agent, wherein the reward measures a difference between the target voltage levels of the cells and the actual voltage levels programmed to the cells.

Reinforcement learning pulse programming

A method for determining an optimal threshold of a nonvolatile memory device, the method including: reading a page from a nonvolatile memory device with a default threshold and attempting to hard decode the page using the default threshold; reading the page two more times with a predetermined offset voltage when the hard decoding fails and attempting to soft decode the page using the default threshold; approximating an empirical distribution of successfully decoded bits with a Gaussian distribution for each level; finding an intersection of the Gaussian distributions; and setting the intersection as a new reading threshold and reading the page again with the new reading threshold.

Threshold estimation in NAND flash devices

A memory system includes a memory device, and a memory controller including a processor and an internal memory. A computer program including a neural network is stored in the memory system. The processor executes the computer program to extract a voltage level from each of a plurality of memory cells connected to one string select line (SSL), in which the memory cells and the SSL are included in a memory block of the memory device, provide the voltage levels as input to the neural network, and perform noise cancellation on the SSL, using the neural network, by changing at least one of the voltage levels from a first voltage level to a second voltage level. The first voltage level is classified into a first cluster of memory cells, and the second voltage level is classified into a second cluster of memory cells different from the first cluster.

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Ramat Gan, Israel

Elisha Halperin