Data Storage Device and Method for Using Modular Models for Inferring a Read Threshold
A model can be used to infer a read threshold for reading a memory of a data storage device. In some situations, such as when the memory has an open wordline or an open block, the model may not provide an accurate read threshold. In such situations, one or more additional models can be used as modular add-ons to the original model to provide a more-accurate read threshold, which can result in a reduced bit error rate, as well as improved throughput, quality of service, and power consumption.
1 . A data storage device comprising:
a memory; and
one or more processors, individually or in combination, configured to:
determine whether a condition exists in the data storage device that triggers use of at least one add-on model as a modular addition to a base model;
in response to determining that the condition does not exist:
use the base model to infer a first read threshold; and
read the memory using the first read threshold; and
in response to determining that the condition exists:
use the at least one add-on module and the base model to infer a second read threshold; and
read the memory using the second read threshold.
2 . The data storage device of claim 1 , wherein the condition comprises an open wordline.
3 . The data storage device of claim 1 , wherein the condition comprises an open block.
4 . The data storage device of claim 1 , wherein the condition comprises a read/write temperature above a threshold.
5 . The data storage device of claim 1 , wherein the condition comprises a program-erase count (PEC) above a threshold.
6 . The data storage device of claim 1 , wherein the condition comprises a specific system state status.
7 . The data storage device of claim 1 , wherein two of the at least one add-on modules are mutually exclusive.
8 . The data storage device of claim 1 , wherein the base model comprises a subset of trees in a forest-based model and the at least one add-on model comprise a remaining subset of trees in the forest-based model.
9 . The data storage device of claim 1 , wherein the at least one add-on model comprises at least one table.
10 . The data storage device of claim 1 , wherein the at least one add-on model is trained on predictions of previous models.
11 . The data storage device of claim 1 , wherein the at least one add-on model is trained independently.
12 . The data storage device of claim 1 , wherein input features of the base model comprise one or more of: addressing, temperature data, data retention (DR) data, program-erase count (PEC), bit error rate (BER) measurements, BER estimates or proxies such as ECC Syndrome Weight (SW), previous read thresholds, samples or estimations of read thresholds in offline calibrated state tables, wordline number, plane number, string number, and logical page number.
13 . The data storage device of claim 1 , wherein the memory comprises a three-dimensional memory.
14 . In a data storage device comprising a memory, a method comprising:
determining to use at least one add-on model in addition to a base model;
inferring a read threshold using the at least one add-on model in addition to the base model; and
using the read threshold to read the memory.
15 . The method of claim 14 , wherein an open wordline triggers use of the at least one add-on model.
16 . The method of claim 14 , wherein an open block triggers use of the at least one add-on model.
17 . The method of claim 14 , wherein the at least one add-on model is stored in the data storage device.
18 . The method of claim 14 , wherein the at least one add-on model is stored in a host memory buffer in a host.
19 . The method of claim 14 , wherein the at least one add-on model and the base model comprise forest-based models.
20 . A data storage device comprising:
a memory; and
means for:
using at least one add-model in addition to a base model to infer a read threshold; and
using the read threshold to read the memory.