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Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing

Nature Materials volume 22, pages 1499–1506 (2023)Cite this article

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An Author Correction to this article was published on 02 January 2024

A Publisher Correction to this article was published on 12 October 2023

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Abstract

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff’s law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

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Fig. 1: Different sensory computing architectures and schematic of proposed IMSC architectures.
Fig. 2: Ferroelectric-defined reconfigurable 2D homojunctions.
Fig. 3: Optoelectronic properties of the weight-reconfigurable 2D homojunctions.
Fig. 4: Ferroelectric-defined weight-reconfigurable sensor array.
Fig. 5: Hardware implementation of image recognition using the ferroelectric-defined weight-reconfigurable sensor array.

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Data availability

Source data are available via Figshare at https://doi.org/10.6084/m9.figshare.24032001. The data that support the plots within this paper and other findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The code that supports the plots in this paper is available via GitHub at https://github.com/zxmbrain/In-sensor-computing-with-dual-gates-devices.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (grant no. 2021YFA1200700), National Natural Science Foundation of China (grant nos. T2222025, 62025405, 61825404, 61835012, 62104043, 62104044 and 62174053), the China National Postdoctoral Program for Innovative Talents (grant no. BX2021069), the China Postdoctoral Science Foundation (grant no. 2021M690649), Shanghai Science and Technology Innovation Action Plan (grant nos. 19JC141670, 21JC1402000 and 21520714100), Open Research Projects of Zhejiang Lab (2021MD0AB03) and Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDB44000000).

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Author notes

  1. These authors contributed equally: Guangjian Wu, Xumeng Zhang, Guangdi Feng.

Authors and Affiliations

  1. State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China

    Guangjian Wu, Xumeng Zhang, Jingli Wang, Keji Zhou, Fangduo Zhu, Mengru Zhang, Qi Liu, Jianlu Wang & Ming Liu

  2. Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China

    Guangjian Wu, Xumeng Zhang, Qi Liu, Jianlu Wang & Ming Liu

  3. Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China

    Guangjian Wu, Xumeng Zhang, Qi Liu, Jianlu Wang & Ming Liu

  4. Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China

    Guangdi Feng, Chenkai Yang, Xiaoming Zhao, Danni Gong, Bobo Tian, Chungang Duan & Junhao Chu

  5. State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China

    Jinhua Zeng & Junhao Chu

  6. Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China

    Danian Dong

  7. Institute of Optoelectronics, Shanghai Frontier Base of Intelligent Optoelectronics and Perception, Fudan University, Shanghai, China

    Jianlu Wang & Junhao Chu

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Contributions

G.W., X. Zhang, Q.L., B.T. and Jianlu Wang conceived the concept. Q.L, B.T. and Jianlu Wang supervised the research. G.W. and J.Z. fabricated the devices. Jingli Wang and M.Z. transferred the array Au electrode. G.W. and G.F. performed the electrical and optoelectronic measurements. X. Zhang, F.Z., C.Y. and K.Z. performed the edge detection and classification tasks. X. Zhang and D.D. designed the test program for the LTD and LTP processes. G.W., G.F., X. Zhao, D.G. and B.T. performed the chip–robot dog interaction video demo. C.D., J.C. and M.L. advised on the experiments and data analysis. All authors discussed the results and revised the manuscript.

Corresponding authors

Correspondence to Bobo Tian, Qi Liu or Jianlu Wang.

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Nature Materials thanks Yang Chai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–23.

Supplementary Video 1

The robot dog movement navigated by the IMSC chip.

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Wu, G., Zhang, X., Feng, G. et al. Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing. Nat. Mater. 22, 1499–1506 (2023). https://doi.org/10.1038/s41563-023-01676-0

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