Neural network detector with sparse codes for spin transfer torque magnetic random access memory

Abstract

This paper presents leveraging the neural network detector to improve the performance of a spin transfer torque magnetic random-access memory (STT-MRAM), where the sparse coding scheme is also applied to protect the user data for the asymmetric write failure. The STT-MRAM has recently emerged as a good candidate, attracting the attention of both academia and industry because of its unique features such as density, non-volatility, power consumption, and integration with CMOS technology. The reliability of STT-MRAM is degraded significantly by reading and writing failures. The asymmetric write failure is the primary contributor to write errors in STT-MRAM. This issue arises from a higher error rate when switching from 0 (low resistance) to 1 (high resistance), as compared to switching from 1 to 0. The sparse coding scheme can be employed, which ensures that the codeword weight is always less than half of the codeword length. By reducing the frequency of encountering “1,” this method minimizes the occurrence of 0 ➔ 1 switching, which in turn reduces the rate of writing failure for the STT-MRAM system. The neural network detector offers better performance than the threshold detector and significantly tackles the system’s unknown offset. Simulation results show the proposed scheme can provide improvements for the reliability of STT-MRAM under the effect of both write and read errors. For instance, the proposed model can handle a read error rate of approximately 9%, whereas the traditional thresholding solution only allows about 6%. Moreover, the proposal eliminates the error propagation in retrieving the original information.

Keywords:

• Neural network
• multilayer perceptron (MLP)
• sparse codes
• asymmetric write error rate
• spin-torque transfer magnetic random access memory (STT-MRAM).

Acknowledgments

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.04-2019.307.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) [102.04-2019.307].