4.8 Article

Spin-Torque Memristors Based on Perpendicular Magnetic Tunnel Junctions for Neuromorphic Computing

期刊

ADVANCED SCIENCE
卷 8, 期 10, 页码 -

出版社

WILEY
DOI: 10.1002/advs.202004645

关键词

chiral spin vortices; magnetic tunnel junctions; memristors; neuromorphic computing; spintronics

资金

  1. National Natural Science Foundation of China [12004024, 62001019, 61627813, 61571023, 61901019]
  2. International Collaboration Project [B16001]
  3. National Key Technology Program of China [2017ZX01032101]
  4. VR innovation platform from Qingdao Science and Technology Commission
  5. Magnetic Sensor innovation platform from Laoshan District

向作者/读者索取更多资源

Spin-torque memristors offer fast, low-power, and infinite memristive behavior for neuromorphic computing and non-volatile memory applications. However, the physical implementation faces challenges in combining high magnetoresistance, stable domain wall pinning, and current-induced switching. Researchers have experimentally demonstrated a nanoscale spin-torque memristor with a unique composite free layer structure, achieving high tunneling magnetoresistance and memristive behavior through spin-transfer torque switching.
Spin-torque memristors are proposed in 2009, and can provide fast, low-power, and infinite memristive behavior for neuromorphic computing and large-density non-volatile memory. However, the strict requirements of combining high magnetoresistance, stable domain wall pinning and current-induced switching in a single device pose difficulties in physical implementation. Here, a nanoscale spin-torque memristor based on a perpendicular-anisotropy magnetic tunnel junction with a CoFeB/W/CoFeB composite free layer structure is experimentally demonstrated. Its tunneling magnetoresistance is higher than 200%, and memristive behavior can be realized by spin-transfer torque switching. Memristive states are retained by strong domain wall pinning effects in the free layer. Experiments and simulations suggest that nanoscale vertical chiral spin textures can form around clusters of W atoms under the combined effect of opposite Dzyaloshinskii-Moriya interactions and the Ruderman-Kittel-Kasuya-Yosida interaction between the two CoFeB free layers. Energy fluctuation caused by these textures may be the main reason for the strong pinning effect. With the experimentally demonstrated memristive behavior and spike-timing-dependent plasticity, a spiking neural network to perform handwritten pattern recognition in an unsupervised manner is simulated. Due to advantages such as long endurance and high speed, the spin-torque memristors are competitive in the future applications for neuromorphic computing.

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