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<p>Recent years have seen remarkable progress in three-dimensional (3D) integration of non-silicon materials, enabling the convergence of diverse functionalities such as sensing, storage, and computing beyond mere transistor scaling. This advancement accelerates edge intelligence by enabling more efficient information processing at the source with reduced latency and power consumption. In this work, we contribute to this rapidly evolving landscape by demonstrating reservoir computing through 3D integration of In<sub>2</sub>Se<sub>3</sub>-based photodetectors with MoS<sub>2</sub>-based memtransistors. Our top tier exploits the variation in photoresponse of an optical reservoir constructed using flakes of different thicknesses of In<sub>2</sub>Se<sub>3</sub>.&#xa0;The bottom tier deploys programmable MoS<sub>2</sub> memtransistors to convert the photocurrent into photovoltages which are subsequently processed by a trained readout circuit that is&#xa0;also based on MoS<sub>2</sub> memtransistors. Notably, the physical proximity between sensors and computing elements is less than 50 nm, surpassing current state-of-the-art packaging solutions. We also demonstrate the benefits of near-sensor information processing for better photoresponse calibration and to achieve higher photoresponse speed. Overall, our 3D stack, with its near-sensor and in-memory compute capability, marks a significant milestone in vertically stacked functional layers composed of heterogeneous materials beyond silicon for edge applications.</p>

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3D Integration of functionally diverse 2D materials for optoelectronic reservoir computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Anirban Chowdhury,
  • Anshul Rasyotra,
  • Harikrishnan Ravichandran,
  • Denesh Kumar Manoharan,
  • Yongwen Sun,
  • Chen Chen,
  • Joan M. Redwing,
  • Yang Yang,
  • Saptarshi Das

摘要

Recent years have seen remarkable progress in three-dimensional (3D) integration of non-silicon materials, enabling the convergence of diverse functionalities such as sensing, storage, and computing beyond mere transistor scaling. This advancement accelerates edge intelligence by enabling more efficient information processing at the source with reduced latency and power consumption. In this work, we contribute to this rapidly evolving landscape by demonstrating reservoir computing through 3D integration of In2Se3-based photodetectors with MoS2-based memtransistors. Our top tier exploits the variation in photoresponse of an optical reservoir constructed using flakes of different thicknesses of In2Se3. The bottom tier deploys programmable MoS2 memtransistors to convert the photocurrent into photovoltages which are subsequently processed by a trained readout circuit that is also based on MoS2 memtransistors. Notably, the physical proximity between sensors and computing elements is less than 50 nm, surpassing current state-of-the-art packaging solutions. We also demonstrate the benefits of near-sensor information processing for better photoresponse calibration and to achieve higher photoresponse speed. Overall, our 3D stack, with its near-sensor and in-memory compute capability, marks a significant milestone in vertically stacked functional layers composed of heterogeneous materials beyond silicon for edge applications.