Electronic Skin with Energy Autonomy and Distributed Neural Data Processing

Dahiya, R. , Navaraj, W. T., García Núñez, C. , Shakthivel, D. and Liu, F. (2018) Electronic Skin with Energy Autonomy and Distributed Neural Data Processing. 4th Annual Innovations in Large Area Electronics Conference (innoLAE 2018), Cambridge, UK, 23-24 Jan 2018. (Unpublished)

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Abstract

Harnessing technological advances to develop nature-inspired systems has led to many interesting solutions such as electronic skin (e-skin) with features mimicking human skin, as well as, imparting new functionalities beyond human skin’s sensory level [1]. The major focus of e-skin research so far has been on the development of various types of sensors (e.g. contact, pressure, temperature, humidity, etc.) and their integration on large-area and flexible substrates. In this regard, two key challenges lie in realizing largearea e-skin: (1) processing of a large amount of data distributed over large areas, and (2) powering a large array of sensors. As an example, an estimated 45k mechanoreceptors (MRs) will be needed in about 1.5 m2 area to develop human inspired e-skin for robots. These sensory receptors process tactile data locally and require significant energy. Accordingly, flexible distributed tactile data processing and energy harvesting solutions are needed for an effective e-skin. Photovoltaics have shown one of the best performance for generating energy per unit area and are a promising candidate for e-skin [2]. Likewise, a neuromimicking approach could help to acquire and process sensors data locally as it leads to a significant downstream reduction in the numbers of neurons transmitting stimuli in the early sensory pathways in humans [3]. In this work, we show our recent research on e-skin (Figure 1) addressing above challenges through the development of a nanowire (NW) based neural field effect transistor (ν-NWFET) as a basic building block for neural-mimicking data processing (Figure 1(a)) and an energy-autonomous e-skin achieved by integrating graphene based transparent touch sensors to photovoltaic cells (Figure 1(d)). The heterogeneous integration of various materials led to achieving such functionalities. Nanomaterials such as graphene and Si NWs are considered as good candidates for flexible electronics due to their excellent mechanical flexibility, printability in large-area as well as outstanding electrical performance. Here, we present a low-cost method to transfer and pattern single layer graphene on large-area flexible and transparent substrates, resulting in a co-planar interdigitated capacitive structure. In terms of the sensing performance, our sensors can detect minimum pressures down to 0.11 kPa with a uniform sensitivity of 4.3 Pa−1 along a broad pressure range. Thanks to the transparency of graphene, the integration of touch sensors atop a photovoltaic cell is possible, which paves a new way for energy-autonomous, flexible, and tactile e-skin (Figure 1(d)). Using ν-NWFET to realize hardware neural network is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a flexible tactile e-skin with distributed neural elements (for local data processing, as in biological skin) in the backplane. Given the previously demonstrated metalassisted chemical etching NW synthesis method and contact printing for large-area assembling of NWs, the ν-NWFET presented here is promising for large-area and low-cost flexible electronics. Modeling, simulation and fabrication of ν-NWFET shows that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network. Further, proof-of-concept is shown by interfacing it with a transparent tactile e-skin prototype integrated on the palm of a 3D printed robotic hand and performing coding of touch gesture. The research finds place in numerous futuristic applications such as prosthetics, robotics and electroceuticals, and this presentation will show the interesting progress made in this direction

Item Type:Conference or Workshop Item
Status:Unpublished
Refereed:Yes
Glasgow Author(s) Enlighten ID:Garcia Nunez, Dr Carlos and Dahiya, Professor Ravinder and Shakthivel, Dr Dhayalan and Liu, Fengyuan and Navaraj, Mr William
Authors: Dahiya, R., Navaraj, W. T., García Núñez, C., Shakthivel, D., and Liu, F.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
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