Artificially Intelligent Tactile Ferroelectric Skin

• Published in: Advanced science Vol. 7; no. 22; pp. 2001662 - n/a
• Main Authors: Lee, Kyuho, Jang, Seonghoon, Kim, Kang Lib, Koo, Min, Park, Chanho, Lee, Seokyeong, Lee, Junseok, Wang, Gunuk, Park, Cheolmin
• Format: Journal Article
• Language: English
• Published: Germany John Wiley & Sons, Inc 01.11.2020; John Wiley and Sons Inc; Wiley
• Subjects: artificial tactile learning electronic‐skin; Communication; Communications; Electrodes; ferroelectric artificial synapses; Ferroelectrics; ferroelectric‐gate field‐effect transistor sensing memory; Neural networks; Sensors; tactile sensory synapses; Transistors; wearable neuromorphic electronic devices; tactile sensory synapses; artificial tactile learning electronic‐skin; ferroelectric artificial synapses; ferroelectric‐gate field‐effect transistor sensing memory; wearable neuromorphic electronic devices
• ISSN: 2198-3844; 2198-3844
• DOI: 10.1002/advs.202001662

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. An artificially intelligent electronic skin that is capable of sensing and learning tactile stimuli is presented. This ferroelectric field effect transistor platform can implement spatial sensory synaptic functions through electrical and/or tactile spikes without complicated integration. These unique characteristics enable the demonstrated device array to recognize different handwriting patterns with a high degree of accuracy, similar to biological neural networks.