Method and apparatus for reading a flash memory device

Patent metadata

Field	Value
Publication number	US-11514992-B2
Application number	US-202117234993-A
Country	US
Kind code	B2
Filing date	Apr 20, 2021
Priority date	Feb 25, 2021
Publication date	Nov 29, 2022
Grant date	Nov 29, 2022

Abstract

A method for reading a flash memory device includes storing configuration files of reliability-state Classification Neural Network (CNN) models and Regression Neural Network (RNN) inference models, and storing reliability-state tags corresponding to reliability states. The current number of P/E cycles is identified and a reliability-state CNN model is selected corresponding to the current number of P/E cycles. A neural network operation of the selected reliability-state CNN model is performed to identify a predicted reliability state. Corresponding reliability-state tags are identified and a corresponding RNN inference model is selected. A neural network operation of the selected RNN inference model is performed, using the reliability-state tags as input, to generate output indicating the shape of a threshold-voltage-shift read-error (TVS-RE) curve. Threshold Voltage Shift Offset (TVSO) values are identified corresponding to a minimum value of the TVS-RE curve and a read is performed using a threshold-voltage-shift read at the identified TVSO values.

First claim

What is claimed is:

1. A method for reading a flash memory device comprising:
storing configuration files of a plurality of reliability-state classification neural network (CNN) models, configuration files of a plurality of regression neural network (RNN) inference models and a plurality of sets of reliability-state tags, each set of reliability-state tags associated with one of a plurality of reliability states and each of the reliability-state CNN models associated with a range of program and erase (P/E) cycles;
monitoring the operation of the flash memory device to identify a current number of P/E cycles of the flash memory device;
selecting one of the reliability-state CNN models associated with a range of P/E cycles corresponding to the current number of P/E cycles;
performing, at a flash controller, a neural network operation of the selected reliability-state CNN model to identify a predicted reliability state;
identifying the set of reliability-state tags associated with the predicted reliability state;
selecting the one of the plurality of RNN inference models that corresponds to the predicted reliability state;
performing, at a flash controller, a neural network operation of the selected RNN inference model, that uses as input the identified reliability-state tags, to generate output values indicating the shape of a threshold-voltage-shift read-error (TVS-RE) curve;
identifying a threshold voltage shift offset (TVSO) value proximate to a minimum value of the TVS-RE curve;
repeating the selecting one of the RNN models, the performing of the neural network operation of the selected RNN inference model and the identifying to identify TVSO values for all threshold voltage regions required to read the flash memory device; and
performing a read of the flash memory device using a threshold-voltage-shift read at the identified TVSO values.

2. The method of claim 1 wherein the set of reliability-state tags associated with the predicted reliability state include a retention time tag indicating a retention time value, a read-disturb tag indicating a read-disturb value and a temperature tag indicating a temperature value.

3. The method of claim 1 wherein the performing a neural network operation of the identified reliability-state CNN model to identify a predicted reliability state uses as input to the neural network operation a wordline index and a block index.

4. The method of claim 1 wherein the performing a neural network operation of the identified reliability-state CNN model uses as input to the neural network operation a wordline index, a block index and a page index.

5. The method of claim 1 wherein the performing a network operation of the selected RNN inference model uses as input to the neural network operation a wordline index, a block index, a page index, a retention time tag, a read-disturb tag and a temperature tag.

6. The method of claim 1 wherein the output indicating the shape of the TVS-RE curve comprises a plurality of coefficients of the TVS-RE curve and wherein the identifying one or more TVSO values proximate to a minimum value of the generated TVS-RE curve comprises performing a minimum function on the coefficients to identify the TVSO value.

7. The method of claim 1 wherein the output indicating the shape of the TVS-RE curve comprises output values indicating the error corresponding to different TVSO values and wherein the identifying a TVSO value proximate to a minimum value of the generated TVS-RE curve comprises identifying the output value having the lowest error.

8. The method of claim 1 further comprising:
generating the plurality of RNN inference models, each of the RNN inference models for performing a regression neural network operation to identify coefficients of the TVS-RE curve.

9. The method of claim 1 further comprising:
generating the plurality of reliability-state CNN models, each of the plurality of reliability-state CNN models generated using data records corresponding to a different range of P/E cycle values.

10. A flash controller comprising:
a data storage module for storing configuration files of a plurality of reliability-state Classification Neural Network (CNN) models, configuration files of a plurality of Regression Neural Network (RNN) inference models and a plurality of sets of reliability-state tags, each set of reliability-state tags associated with one of a plurality of reliability states and each of the reliability-state CNN models associated with a range of program and erase (P/E) cycles;
a status module identify a current number of P/E cycles;
a neural processing module coupled to the data storage module and to the control module, the neural processing module to:
perform a neural network operation of the one of the stored reliability-state CNN models associated with a range of P/E cycles corresponding to the current number of P/E cycles to identify a predicted reliability state, and
perform neural network operations of the RNN inference models corresponding to the predicted reliability state, using as input to each neural network operation the set of reliability-state tags associated with the predicted reliability state, to generate output indicating the shape of TVS-RE curves for all threshold voltage regions required to read a flash memory device;

a minimum function module identify TVSO values for all threshold voltage regions required to read the flash memory device, each of the identified TVSO values proximate a minimum value of one of the TVS-RE curves; and
a read module to perform a read of the flash memory device by sending a threshold-voltage-shift read instruction to the flash memory device that includes the identified TVSO values.

11. The flash controller of claim 10 wherein the input for the neural network operation of the reliability-state CNN and the input for the neural network operation of the stored RNN inference model include a wordline index and a block index.

12. The flash controller of claim 10 wherein the input for the neural network operation of the reliability-state CNN and the input for the neural network operation of the stored RNN inference model include a wordline index, a block index and a page index.

13. The flash controller of claim 10 wherein the input for the neural network operation of the RNN inference model includes a wordline index, a block index, a page index, a retention time tag, a read-disturb tag and a temperature tag.

14. A Solid State Drive (SSD) comprising:
a flash memory device;
a flash controller coupled to the flash memory device, the flash controller including:
a data storage module storing configuration files of a plurality of reliability-state classification neural network (CNN) models, configuration files of a plurality of regression neural network (RNN) inference models and a plurality of sets of reliability-state tags, each set of reliability-state tags associated with one of a plurality of reliability states and each of the reliability-state CNN models associated with a range of program and erase (P/E) cycles;
a status module to identify a current number of P/E cycles;
a neural processing module coupled to the data storage module and to the control module, the neural processing module to:
perform a neural network operation of the one of the stored reliability-state CNN models associated with a range of P/E cycles corresponding to the current number of P/E cycles to identify a predicted reliability state, and
perform neural network operations of the RNN inference models corresponding to the predicted reliability state, using as input to each neural network operation the set of reliability-state tag

Assignees

• Microchip Tech Inc

Inventors

• Zuolo Lorenzo
• Micheloni Rino

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/063

  using electronic means · CPC title

• G06N3/08

  Learning methods · CPC title