3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Junctionless Poly-GeSn Ferroelectric TFTs with Improved Reliability by Interface Engineering for Neuromorphic Computing

      , , , ,
      ACS Applied Materials & Interfaces
      American Chemical Society (ACS)

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Ferroelectric HfZrO x (Fe-HZO) with a larger remnant polarization (Pr) is achieved by using a poly-GeSn film as a channel material as compared with a poly-Ge film because of the lower thermal expansion that induces higher stress. Then two-stage interface engineering of junctionless poly-GeSn (Sn of ∼5.1%) ferroelectric thin-film transistors (Fe-TFTs) based on HZO was employed to improve the reliability characteristics. With stage I of NH3 plasma treatment on poly-GeSn and subsequent stage II of Ta2O5 interfacial layer growth, the interfacial quality between Fe-HZO and the poly-GeSn channel is greatly improved, which in turn enhances the reliability performance in terms of negligible Pr degradation up to 106 cycles (±2.7 MV/1 ms) and 96% Pr after a 10 year retention at 85 °C. Furthermore, to emulate the synapse plasticity of the human brain for neuromorphic computing, besides manifesting the capability of short-term plasticity, the devices also exhibit long-term plasticity with the characteristics of analog conductance (G) states of 80 levels (>6 bit), small linearity for potentiation and depression of -0.83 and 0.62, high symmetry, and moderate Gmax/Gmin of 9.6. By employing deep neural network, the neuromorphic system with poly-GeSn Fe-TFT synaptic devices achieves 91.4% pattern recognition accuracy. In addition, the learning algorithm of spike-timing-dependent plasticity based on spiking neural network is demonstrated as well. The results are promising for on-chip training, making it possible to implement neuromorphic computing by monolithic 3D ICs based on poly-GeSn Fe-TFTs.

          Related collections

          Author and article information

          Journal
          ACS Applied Materials & Interfaces
          ACS Appl. Mater. Interfaces
          American Chemical Society (ACS)
          1944-8244
          1944-8252
          December 09 2019
          December 09 2019
          Article
          10.1021/acsami.9b16231
          31814384
          10ad5a2c-3904-4631-bf29-3e439540f9ac
          © 2019
          History

          Comments

          Comment on this article