Artificially Intelligent Tactile Ferroelectric Skin

Kyuho Lee, Seonghoon Jang, Kang Lib Kim, Min Koo, Chanho Park, Seokyeong Lee, Junseok Lee, Gunuk Wang, Cheolmin Park

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications in emerging wearable and human-interactive artificially intelligent neuromorphic electronics. In this study, an integrated artificially intelligent tactile learning electronic skin (e-skin) based on arrays of ferroelectric-gate field-effect transistors with dome-shape tactile top-gates, which can simultaneously sense and learn from a variety of tactile information, is introduced. To test the e-skin, tactile pressure is applied to a dome-shaped top-gate that measures ferroelectric remnant polarization in a gate insulator. This results in analog conductance modulation that is dependent upon both the number and magnitude of input pressure-spikes, thus mimicking diverse tactile and essential synaptic functions. Specifically, the device exhibits excellent cycling stability between long-term potentiation and depression over the course of 10 000 continuous input pulses. Additionally, it has a low variability of only 3.18%, resulting in high-performance and robust tactile perception learning. The 4 × 4 device array is also able to recognize different handwritten patterns using 2-dimensional spatial learning and recognition, and this is successfully demonstrated with a high degree accuracy of 99.66%, even after considering 10% noise.

Original languageEnglish
Article number2001662
JournalAdvanced Science
Volume7
Issue number22
DOIs
Publication statusPublished - 2020 Nov 18

Bibliographical note

Funding Information:
K.L. and S.J. contributed equally to this work. This work was supported by the National Research Foundation of Korea (Grant Nos. 2019R1A2C2003704, 2018M3D1A1058536, and 2020R1A2B5B03002697), the KU-KIST Research Fund (Grant No. R1828382), and the Korea University Grant. G.W. acknowledges the support from Samsung Electronics (Grant No. Q1825362).

Funding Information:
K.L. and S.J. contributed equally to this work. This work was supported by the National Research Foundation of Korea (Grant Nos. 2019R1A2C2003704, 2018M3D1A1058536, and 2020R1A2B5B03002697), the KU‐KIST Research Fund (Grant No. R1828382), and the Korea University Grant. G.W. acknowledges the support from Samsung Electronics (Grant No. Q1825362).

Publisher Copyright:
© 2020 The Authors. Published by Wiley-VCH GmbH

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Chemical Engineering(all)
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Materials Science(all)
  • Engineering(all)
  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Artificially Intelligent Tactile Ferroelectric Skin'. Together they form a unique fingerprint.

Cite this