Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin.

William Taube Navaraj,Ravinder Dahiya,Fabrice Labeau,Vincenzo Vinciguerra,Duncan H Gregory,Dhayalan Shakthivel,Carlos García Núñez

doi:10.3389/fnins.2017.00501

William Taube Navaraj, Ravinder Dahiya + Show 5 more

Open Access

https://doi.org/10.3389/fnins.2017.00501

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Abstract

This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs) based hardware-implementable neural network (HNN) approach for tactile data processing in electronic skin (e-skin). The viability of Si nanowires (NWs) as the active material for υ-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using υ-NWFETs to realize HNNs is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a bendable tactile skin with distributed neural elements (for local data processing, as in biological skin) in the backplane. The modeling and simulation of υ-NWFET based devices show that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network - thus validating the working of υ-NWFETs as the building block for HNN. The simulation has been further extended to υ-NWFET based circuits and neuronal computation system and this has been validated by interfacing it with a transparent tactile skin prototype (comprising of 6 × 6 ITO based capacitive tactile sensors array) integrated on the palm of a 3D printed robotic hand. In this regard, a tactile data coding system is presented to detect touch gesture and the direction of touch. Following these simulation studies, a four-gated υ-NWFET is fabricated with Pt/Ti metal stack for gates, source and drain, Ni floating gate, and Al2O3 high-k dielectric layer. The current-voltage characteristics of fabricated υ-NWFET devices confirm the dependence of turn-off voltages on the (synaptic) weight of each gate. The presented υ-NWFET approach is promising for a neuro-robotic tactile sensory system with distributed computing as well as numerous futuristic applications such as prosthetics, and electroceuticals.

Highlights

NEURO-MIMICKING TACTILE SENSINGHumans and other biological organisms use tactile feedback to interact with the environment (Dahiya et al, 2010)
This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs) based hardware-implementable neural network (HNN) approach for tactile data processing in electronic skin (e-skin)
With the recent shift in the focus of tactile skin research in robotics from hands to whole-body tactile feedback, a need has arisen for new techniques to manage the tactile data

Summary

Introduction

NEURO-MIMICKING TACTILE SENSINGHumans and other biological organisms use tactile feedback to interact with the environment (Dahiya et al, 2010). To develop human inspired e-skin for robotic and prosthetic limbs, an estimated 45 K mechanoreceptors (MRs) will be needed in about 1.5 m2 area, as shown in Figure 1 (details in Supplementary Section 1; Johansson and Vallbo, 1979; Boniol et al, 2008; Mancini et al, 2014; Goldstein and Brockmole, 2016) This number of sensors will be much higher if we consider e-skin to have equivalents of thermo-receptors and nociceptors (Goldstein and Brockmole, 2016). Due to the lack of large-scale parallel processing, the software-programmed neural networks are slower and less energy-efficient (Ananthanarayanan et al, 2009; Misra and Saha, 2010) and the HNN implementations will be interesting

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