System and method for non-parametric optimal read threshold estimation using deep neural network
A scheme for non-parametric optimal read threshold estimation of a memory system. The memory system includes a memory device including pages and a controller including a neural network. The controller performs read operations on a selected page using a read threshold set; obtain the read threshold set, a checksum value and an asymmetric ratio of ones count and zeros count which are associated with decoding of the selected page according to each of the read operations; provide the obtained read threshold set, the checksum value and the asymmetric ratio as input information to the neural network; and estimate, by the neural network, an optimal read threshold voltage based on the input information and weights including a combination of multiple matrices and bias vectors.
1. A memory system comprising:
a memory device including a plurality of pages; and
a controller including a neural network and configured to:
perform one or more read operations on a page selected from among the plurality of pages using a read threshold set including a plurality of read threshold voltages;
obtain the read threshold set, a checksum value and an asymmetric ratio of ones count and zeros count which are associated with decoding of the selected page according to each of the read operations;
provide the obtained read threshold set, the checksum value and the asymmetric ratio as input information to the neural network; and
estimate, by the neural network, an optimal read threshold voltage based on the input information and weights for the input information, the weights including a combination of multiple matrices and bias vectors.
2. The memory system of claim 1 , wherein the neural network is trained to obtain the weights such that a set loss function is minimized.
3. The memory system of claim 2 , wherein the set loss function includes a differentiable loss function to measure respective errors between outputs of the neural network and the optimal read threshold voltage.
4. The memory system of claim 1 , wherein the controller estimates the optimal read threshold voltage based on a combination of the input information, the multiple matrices, the bias vectors and an activation function.
5. The memory system of claim 4 , wherein the controller estimates the optimal read threshold voltage based on equation:
Opt Vt=A DNN,N ×R ( A DNN,N-1 ×R ( A DNN,N-2 ×R ( . . . R ( A DNN,0 ×[Vt,CS,AR]+b DNN,0 ) . . . )+ b DNN,N-2 )+ b DNN,N-1 )+ b DNN,N ,
where OptVt represents the optimal read threshold voltage, Vt represents the read threshold set, CS represents the checksum values, AR represents the asymmetric ratio of ones count and zeros count, A DNN,N represents a (N+1)th matrix, b DNN,N represents a (N+1)th bias vector, and R represents the activation function.
6. The memory system of claim 5 , wherein the asymmetric ratio of ones count and zeros count includes a ratio of the ones count divided by the zeros count.
7. The memory system of claim 4 , wherein the activation function includes a rectified linear unit (ReLU) activation function.
8. The memory system of claim 1 , wherein the selected page includes a least significant bit (LSB) page of a triple level cell (TLC).
9. The memory system of claim 8 , wherein the read threshold voltage set includes a) a first read threshold voltage for distinguishing an erased state from a first program state and b) a second read threshold voltage for distinguishing a second program state from a third program state.
10. A method for operating a memory system having a memory device including a plurality of pages and a controller including a neural network, the method comprising:
performing one or more read operations on a page selected from among the plurality of pages using a read threshold set including a plurality of read threshold voltages;
obtaining the read threshold set, a checksum value and an asymmetric ratio of ones count and zeros count which are associated with decoding of the selected page according to each of the read operations;
providing the obtained read threshold set, the checksum value and the asymmetric ratio as input information to the neural network; and
estimating, by the neural network, an optimal read threshold voltage based on the input information and weights for the input information, the weights including a combination of multiple matrices and bias vectors.
11. The method of claim 10 , wherein the neural network is trained to obtain the weights such that a set loss function is minimized.
12. The method of claim 11 , wherein the set loss function includes a differentiable loss function to measure respective errors between outputs of the neural network and the optimal read threshold voltage.
13. The method of claim 10 , wherein the controller estimates the optimal read threshold voltage based on a combination of the input information, the multiple matrices, the bias vectors and an activation function.
14. The method of claim 13 , wherein the controller estimates the optimal read threshold voltage based on equation:
Opt Vt=A DNN,N ×R ( A DNN,N-1 ×R ( A DNN,N-2 ×R ( . . . R ( A DNN,0 ×[Vt,CS,AR]+b DNN,0 ) . . . )+ b DNN,N-2 )+ b DNN,N-1 )+ b DNN,N ,
where OptVt represents the optimal read threshold voltage, Vt represents the read threshold set, CS represents the checksum values, AR represents the asymmetric ratio of ones count and zeros count, A DNN,N represents a (N+1)th matrix, b DNN,N represents a (N+1)th bias vector, and R represents the activation function.
15. The method of claim 14 , wherein the asymmetric ratio of ones count and zeros count includes a ratio of the ones count divided by the zeros count.
16. The method of claim 13 , wherein the activation function includes a rectified linear unit (ReLU) activation function.
17. The method of claim 10 , wherein the selected page includes a least significant bit (LSB) page of a triple level cell (TLC).
18. The method of claim 17 , wherein the read threshold voltage set includes a) a first read threshold voltage for distinguishing an erased state from a first program state and b) a second read threshold voltage for distinguishing a second program state from a third program state.