Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics

• Main Authors: John, Rohit Abraham, Tiwari, Naveen, Muhammad Iszaki Patdillah, Kulkarni, Mohit Rameshchandra, Tiwari, Nidhi, Basu, Joydeep, Bose, Sumon Kumar, Ankit, Yu, Chan Jun, Nirmal, Amoolya, Vishwanath, Sujaya Kumar, Bartolozzi, Chiara, Basu, Arindam, Mathews, Nripan
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Materials; Engineering::Mechanical engineering; Action Potentials; Signal Processing
• Online Access: https://hdl.handle.net/10356/152883
• Institution: Nanyang Technological University

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and -memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.