Skip to main content
Log in

Implementation of 16 Boolean logic operations based on one basic cell of spin-transfer-torque magnetic random access memory

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

In-memory computing (IMC) systems based on emerging nonvolatile memories (NVMs) provide a thorough solution for memory wall issues and von Neumann bottlenecks. Massive IMC schemes have been proposed by utilizing ingenious structures and additional auxiliary components. However, these schemes are not compatible with the basic cell (one memory unit and one transistor) of emerging random-access memory (RAM), which goes against low-power and high-density requirements. In this paper, we propose a logic implementation scheme based on one magnetic tunnel junction and one transistor (1MTJ-1T), which is the basic cell of spin-transfer-torque magnetic RAM (STT-MRAM). With no other assistance, complete 16 logic operations can be accomplished in two steps with their logic outputs in-situ stored in the MTJ. The area (0.2 µm2) and energy consumption per logic operation (1.1–2.6 pJ) of the logic gates under 14 nm process node are evaluated using SPICE simulations, indicating its excellent performance. Our work exhibits a 1MTJ-1T-based logic operation implementation, which can bridge the gap between STT-MRAM and high-performance IMC applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Li Y, Zhou Y, Wang Z, et al. Memcomputing: fusion of memory and computing. Sci China Inf Sci, 2018, 61: 060424

    Article  Google Scholar 

  2. Cai H, Liu B, Chen J, et al. A survey of in-spin transfer torque MRAM computing. Sci China Inf Sci, 2021, 64: 160402

    Article  MathSciNet  Google Scholar 

  3. Jeong D S, Kim K M, Kim S, et al. Memristors for energy-efficient new computing paradigms. Adv Electron Mater, 2016, 2: 1600090

    Article  Google Scholar 

  4. Ney A, Pampuch C, Koch R, et al. Programmable computing with a single magnetoresistive element. Nature, 2003, 425: 485–487

    Article  Google Scholar 

  5. Wong H S P, Salahuddin S. Memory leads the way to better computing. Nat Nanotech, 2015, 10: 191–194

    Article  Google Scholar 

  6. Wang Z R, Su Y T, Li Y, et al. Functionally complete Boolean logic in 1T1R resistive random access memory. IEEE Electron Dev Lett, 2017, 38: 179–182

    Article  Google Scholar 

  7. Guo Z, Yin J, Bai Y, et al. Spintronics for energy-efficient computing: an overview and outlook. Proc IEEE, 2021, 109: 1398–1417

    Article  Google Scholar 

  8. Yuan R, Ma M, Xu L, et al. Efficient 16 Boolean logic and arithmetic based on bipolar oxide memristors. Sci China Inf Sci, 2020, 63: 202401

    Article  Google Scholar 

  9. Yao P, Wu H, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577: 641–646

    Article  Google Scholar 

  10. Zhong Y, Tang J, Li X, et al. Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing. Nat Commun, 2021, 12: 408

    Article  Google Scholar 

  11. Luo Q, Guo Z, Zhang S, et al. Crack-based complementary nanoelectromechanical switches for reconfigurable computing. IEEE Electron Dev Lett, 2020, 41: 784–787

    Article  Google Scholar 

  12. Guo Z, Guan Y, Luo Q, et al. Ferroelectric-nanocrack switches for memory and complementary logic with zero off-current and low operating voltage. Adv Electron Mater, 2021, 7: 2100023

    Article  Google Scholar 

  13. Borghetti J, Snider G S, Kuekes P J, et al. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature, 2010, 464: 873–876

    Article  Google Scholar 

  14. Cassinerio M, Ciocchini N, Ielmini D. Logic computation in phase change materials by threshold and memory switching. Adv Mater, 2013, 25: 5975–5980

    Article  Google Scholar 

  15. Wong H S P, Lee H Y, Yu S, et al. Metal-oxide RRAM. Proc IEEE, 2012, 100: 1951–1970

    Article  Google Scholar 

  16. Tehrani S, Slaughter J M, Deherrera M, et al. Magnetoresistive random access memory using magnetic tunnel junctions. Proc IEEE, 2003, 91: 703–714

    Article  Google Scholar 

  17. Slesazeck S, Mikolajick T. Nanoscale resistive switching memory devices: a review. Nanotechnology, 2019, 30: 352003

    Article  Google Scholar 

  18. Jeong H, Shi L. Memristor devices for neural networks. J Phys D-Appl Phys, 2019, 52: 023003

    Article  Google Scholar 

  19. Ikeda S, Hayakawa J, Lee Y M, et al. Magnetic tunnel junctions for spintronic memories and beyond. IEEE Trans Electron Dev, 2007, 54: 991–1002

    Article  Google Scholar 

  20. Lyle A, Harms J, Patil S, et al. Direct communication between magnetic tunnel junctions for nonvolatile logic fan-out architecture. Appl Phys Lett, 2010, 97: 152504

    Article  Google Scholar 

  21. Gao S, Yang G, Cui B, et al. Realisation of all 16 Boolean logic functions in a single magnetoresistance memory cell. Nanoscale, 2016, 8: 12819–12825

    Article  Google Scholar 

  22. Zhang K, Cao K, Zhang Y, et al. Rectified tunnel magnetoresistance device with high on/off ratio for in-memory computing. IEEE Electron Dev Lett, 2020, 41: 928–931

    Article  Google Scholar 

  23. Kent A D, Worledge D C. A new spin on magnetic memories. Nat Nanotech, 2015, 10: 187–191

    Article  Google Scholar 

  24. Chen A. A review of emerging non-volatile memory (NVM) technologies and applications. Solid-State Electron, 2016, 125: 25–38

    Article  Google Scholar 

  25. Wang M, Cai W, Zhu D, et al. Field-free switching of a perpendicular magnetic tunnel junction through the interplay of spin-orbit and spin-transfer torques. Nat Electron, 2018, 1: 582–588

    Article  Google Scholar 

  26. Apalkov D, Khvalkovskiy A, Watts S, et al. Spin-transfer torque magnetic random access memory (STT-MRAM). J Emerg Technol Comput Syst, 2013, 9: 1–35

    Article  Google Scholar 

  27. Cao K, Cai W, Liu Y, et al. In-memory direct processing based on nanoscale perpendicular magnetic tunnel junctions. Nanoscale, 2018, 10: 21225–21230

    Article  Google Scholar 

  28. Wang M, Cai W, Cao K, et al. Current-induced magnetization switching in atom-thick tungsten engineered perpendicular magnetic tunnel junctions with large tunnel magnetoresistance. Nat Commun, 2018, 9: 671

    Article  Google Scholar 

  29. Peng S, Zhu D, Zhou J, et al. Modulation of heavy metal/ferromagnetic metal interface for high-performance spintronic devices. Adv Electron Mater, 2019, 5: 1900134

    Article  Google Scholar 

  30. Zhao Y, Yang J, Li B, et al. NAND-SPIN-based processing-in-MRAM architecture for convolutional neural network acceleration. Sci China Inf Sci, 2022. doi: https://doi.org/10.1007/s11432-021-3472-9

  31. Cai W, Shi K, Zhuo Y, et al. Sub-ns field-free switching in perpendicular magnetic tunnel junctions by the interplay of spin transfer and orbit torques. IEEE Electron Dev Lett, 2021, 42: 704–707

    Article  Google Scholar 

  32. Zhao W, Moreau M, Deng E, et al. Synchronous non-volatile logic gate design based on resistive switching memories. IEEE Trans Circ Syst I, 2014, 61: 443–454

    Google Scholar 

  33. Li Y, Zhong Y P, Deng Y F, et al. Nonvolatile “AND,” “OR,” and “NOT” Boolean logic gates based on phase-change memory. J Appl Phys, 2013, 114: 234503

    Article  Google Scholar 

  34. Cai W, Wang M, Cao K, et al. Stateful implication logic based on perpendicular magnetic tunnel junctions. Sci China Inf Sci, 2022, 65: 122406

    Article  Google Scholar 

  35. Chen Y. ReRAM: history, status, and future. IEEE Trans Electron Dev, 2020, 67: 1420–1433

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 51901008), International Mobility Project (Grant No. B16001), National Key Technology Program of China (Grant No. 2017ZX01032101), National Key R&D Program of China (Grant No. 2018YFB0407602), National Natural Science Foundation of China (Grant No. 92164206), International Collaboration Project (Grant No. B16001), and VR Innovation Platform from Qingdao Science and Technology Commission and Magnetic Sensor Innovation Platform from Laoshan District.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Kun Zhang or Weisheng Zhao.

Additional information

Supporting information

Appendixes A–C. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

Supplementary File

11432_2021_3562_MOESM1_ESM.pdf

Implementation of 16 Boolean Logic Operations Based on One Basic Cell of Spin-Transfer-Torque Magnetic Random Access Memory

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Y., Cao, K., Zhang, K. et al. Implementation of 16 Boolean logic operations based on one basic cell of spin-transfer-torque magnetic random access memory. Sci. China Inf. Sci. 66, 162402 (2023). https://doi.org/10.1007/s11432-021-3562-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-021-3562-8

Keywords

Navigation