An in-memory computing architecture based on a duplex two-dimensional material structure for in situ machine learning
Source abstract
Abstract
The growing computational demand in artificial intelligence calls for hardware solutions that are capable of in situ machine learning, where both training and inference are performed by edge computation. This not only requires extremely energy-efficient architecture (such as in-memory computing) but also memory hardware with tunable properties to simultaneously meet the demand for training and inference. Here we report a duplex device structure based on a ferroelectric field-effect transistor and an atomically thin MoS2 channel, and realize a universal in-memory computing architecture for in situ learning. By exploiting the tunability of the ferroelectric energy landscape, the duplex building block demonstrates an overall excellent performance in endurance (>1013), retention (>10 years), speed (4.8 ns) and energy consumption (22.7 fJ bit-1 μm-2). We implemented a hardware neural network using arrays of two-transistors-one-duplex ferroelectric field-effect transistor cells and achieved 99.86% accuracy in a nonlinear localization task with in situ trained weights. Simulations show that the proposed device architecture could achieve the same level of performance as a graphics processing unit under notably improved energy efficiency. Our device core can be combined with silicon circuitry through three-dimensional heterogeneous integration to give a hardware solution towards general edge intelligence.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Ferroelectric and Negative Capacitance Devices · Advanced Memory and Neural Computing · Electronic and Structural Properties of Oxides
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
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1. Hutson, M. Has artificial intelligence become alchemy? Science 360, 478–478 (2018).10.1126/science.360.6388.478 ↗
2. Christensen, D. V. et al. 2022 roadmap on neuromorphic computing and engineering. Neuromorph. Comput. Eng. 2, 022501 (2022).10.1088/2634-4386/ac4a83 ↗
3. Mehonic, A. & Kenyon, A. J. Brain-inspired computing needs a master plan. Nature 604, 255–260 (2022).10.1038/s41586-021-04362-w ↗
4. Salahuddin, S., Ni, K. & Datta, S. The era of hyper-scaling in electronics. Nat. Electron. 1, 442–450 (2018).10.1038/s41928-018-0117-x ↗
5. Kendall, J. D. & Kumar, S. The building blocks of a brain-inspired computer. Appl. Phys. Rev. 7, 011305 (2020).10.1063/1.5129306 ↗
6. Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018).10.1038/s41586-018-0180-5 ↗
7. Yu, S. Neuro-inspired computing with emerging nonvolatile memory. Proc. IEEE 106, 260–285 (2018).10.1109/JPROC.2018.2790840 ↗
8. Zhou, Z. et al. Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107, 1738–1762 (2019).10.1109/JPROC.2019.2918951 ↗
9. Keshavarzi, A., Ni, K., Hoek, W. V. D., Datta, S. & Raychowdhury, A. Ferroelectronics for edge intelligence. IEEE Micro 40, 33–48 (2020).10.1109/MM.2020.3026667 ↗
10. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).10.1038/s41586-020-1942-4 ↗
11. Demasius, K.-U., Kirschen, A. & Parkin, S. Energy-efficient memcapacitor devices for neuromorphic computing. Nat. Electron. 4, 748–756 (2021).10.1038/s41928-021-00649-y ↗
12. Chen, W. et al. CMOS-integrated memristive non-volatile computing-in-memory for AI edge processors. Nat. Electron. 2, 420–428 (2019).10.1038/s41928-019-0288-0 ↗
13. Cheng, C. et al. In-memory computing with emerging nonvolatile memory devices. Sci. China Inf. Sci. 64, 221402 (2021).10.1007/s11432-021-3327-7 ↗
14. Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).10.1038/s41928-017-0002-z ↗
15. Müller, J. et al. Ferroelectricity in simple binary ZrO2 and HfO2. Nano Lett. 12, 4318–4323 (2012).10.1021/nl302049k ↗
16. Böscke, T. S., Müller, J., Bräuhaus, D., Schröder, U. & Böttger, U. Ferroelectricity in hafnium oxide thin films. Appl. Phys. Lett. 99, 102903 (2011).10.1063/1.3634052 ↗
17. Cheema, S. S. et al. Enhanced ferroelectricity in ultrathin films grown directly on silicon. Nature 580, 478–482 (2020).10.1038/s41586-020-2208-x ↗
18. Cheema, S. S. et al. Emergent ferroelectricity in subnanometer binary oxide films on silicon. Science 376, 648–652 (2022).10.1126/science.abm8642 ↗
19. Gao, Z. et al. Identification of ferroelectricity in a capacitor with ultra-thin (1.5-nm) Hf0.5Zr0.5O2 film. IEEE Electron Device Lett. 42, 1303–1306 (2021).10.1109/LED.2021.3097332 ↗
20. Khan, A. I., Keshavarzi, A. & Datta, S. The future of ferroelectric field-effect transistor technology. Nat. Electron. 3, 588–597 (2020).10.1038/s41928-020-00492-7 ↗
21. Schroeder, U., Park, M. H., Mikolajick, T. & Hwang, C. S. The fundamentals and applications of ferroelectric HfO2. Nat. Rev. Mater. 7, 653–669 (2022).10.1038/s41578-022-00431-2 ↗
22. Jerry, M. et al. Ferroelectric FET analog synapse for acceleration of deep neural network training. In 2017 IEEE International Electron Devices Meeting (IEDM) 6.2.1–6.2.4. (IEEE, 2017).10.1109/IEDM.2017.8268338 ↗
23. Ni, K. et al. SoC logic compatible multi-bit FeMFET weight cell for neuromorphic applications. In 2018 IEEE International Electron Devices Meeting (IEDM) 13.2.1–13.2.4. (IEEE, 2018).10.1109/IEDM.2018.8614496 ↗
24. Sun, X., Wang, P., Ni, K., Datta, S. & Yu, S. Exploiting hybrid precision for training and inference: a 2T-1FeFET based analog synaptic weight cell. In 2018 IEEE International Electron Devices Meeting (IEDM) 3.1.1–3.1.4. (IEEE, 2018).10.1109/IEDM.2018.8614611 ↗
25. Tong, L. et al. 2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware. Science 373, 1353–1358 (2021).10.1126/science.abg3161 ↗
26. Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).10.1038/s41928-020-0435-7 ↗
27. Luo, Q. et al. A highly CMOS compatible hafnia-based ferroelectric diode. Nat. Commun. 11, 1391 (2020).10.1038/s41467-020-15159-2 ↗
28. Radisavljevic, B. et al. Single-layer MoS2 transistors. Nat. Nanotechnol. 6, 147–150 (2011).10.1038/nnano.2010.279 ↗
29. Akinwande, D. et al. Graphene and two-dimensional materials for silicon technology. Nature 573, 507–518 (2019).10.1038/s41586-019-1573-9 ↗
30. Liu, C. et al. Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15, 545–557 (2020).10.1038/s41565-020-0724-3 ↗
31. Marega, M. et al. Logic-in-memory based on an atomically thin semiconductor. Nature 587, 72–77 (2020).10.1038/s41586-020-2861-0 ↗
32. Chung, Y.-Y. et al. High-accuracy deep neural networks using a contralateral-gated analog synapse composed of ultrathin MoS2 nFET and nonvolatile charge-trap memory. IEEE Electron Device Lett. 41, 1649–1652 (2020).10.1109/LED.2020.3026931 ↗
33. Chen, L., Pam, M. E., Li, S. & Ang, K.-W. Ferroelectric memory based on two-dimensional materials for neuromorphic computing. Neuromorph. Comput. Eng. 2, 022001 (2022).10.1088/2634-4386/ac57cb ↗
34. Meng, W. et al. Three-dimensional monolithic micro-LED display driven by atomically thin transistor matrix. Nat. Nanotechnol. 16, 1231–1236 (2021).10.1038/s41565-021-00966-5 ↗
35. Schram, T., Sutar, S., Radu, I. & Asselberghs, I. Challenges of wafer‐scale integration of 2D semiconductors for high‐performance transistor circuits. Adv. Mater. 34, 2109796 (2022).10.1002/adma.202109796 ↗
36. Waltl, M. et al. Perspective of 2D integrated electronic circuits: scientific pipe dream or disruptive technology? Adv. Mater. 34, 2201082 (2022).10.1002/adma.202201082 ↗
37. Chai, Y. In-sensor computing for machine vision. Nature 579, 32–33 (2020).10.1038/d41586-020-00592-6 ↗
38. Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020).10.1038/s41586-020-2038-x ↗
39. Li, T. et al. Epitaxial growth of wafer-scale molybdenum disulfide semiconductor single crystals on sapphire. Nat. Nanotechnol. 16, 1201–1207 (2021).10.1038/s41565-021-00963-8 ↗
40. Müller, J. et al. Ferroelectric hafnium oxide: a CMOS-compatible and highly scalable approach to future ferroelectric memories. In 2013 IEEE International Electron Devices Meeting (IEDM) 10.8.1–10.8.4 (IEEE, 2013).10.1109/IEDM.2013.6724605 ↗
41. Gong, N. & Ma, T.-P. A study of endurance issues in HfO2-based ferroelectric field effect transistors: charge trapping and trap generation. IEEE Electron Device Lett. 39, 15–18 (2018).10.1109/LED.2017.2776263 ↗
42. Y. Liu et al. 4.7 A 65nm ReRAM-enabled nonvolatile processor with 6× reduction in restore time and 4× higher clock frequency using adaptive data retention and self-write-termination nonvolatile logic. In 2016 IEEE International Solid-State Circuits Conference (ISSCC) 84–86 (IEEE, 2016).10.1109/ISSCC.2016.7417918 ↗
43. International Roadmap for Devices and Systems (IRDSTM) 2021 Edition (IEEE, 2021); https://irds.ieee.org/editions/2021
44. Krivokapic, Z. et al. 14nm ferroelectric FinFET technology with steep subthreshold slope for ultra low power applications. In 2017 IEEE International Electron Devices Meeting (IEDM) 15.1.1–15.1.4 (IEEE, 2017).10.1109/IEDM.2017.8268393 ↗
45. Dünkel, S. et al. A FeFET based super-low-power ultra-fast embedded NVM technology for 22nm FDSOI and beyond. In 2017 IEEE International Electron Devices Meeting (IEDM) 19.7.1–19.7.4 (IEEE, 2017).10.1109/IEDM.2017.8268425 ↗
46. Zhao, C., Sun, Q., Zhang, C., Tang, Y. & Qian, F. Monocular depth estimation based on deep learning: an overview. Sci. China Technol. Sci. 63, 1612–1627 (2020).10.1007/s11431-020-1582-8 ↗
47. Alhashim, I. & Wonka, P. High quality monocular depth estimation via transfer learning. Preprint at https://arxiv.org/abs/1812.11941 (2018).
48. Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 234–241 (Springer, 2015).10.1007/978-3-319-24574-4_28 ↗
49. Geiger, A., Lenz, P. & Urtasun, R. Are we ready for autonomous driving? The KITTI vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3354–3361 (IEEE, 2012).10.1109/CVPR.2012.6248074 ↗
50. Huang, G., Liu, Z., Maaten, L. V. D. & Weinberger, K. Q. Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2261–2269 (IEEE, 2017).10.1109/CVPR.2017.243 ↗
51. Deng, J. et al. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 248–255 (IEEE, 2009).10.1109/CVPR.2009.5206848 ↗
52. Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).10.1109/TIP.2003.819861 ↗
53. Eigen, D., Puhrsch, C. & Fergus, R. Depth map prediction from a single image using a multi-scale deep network. In 28th Conference on Neural Information Processing Systems (NIPS) (NIPS Foundation, 2014).