This paper presents novel Neural Nanowire Field Effect Transistors (ν-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 ν-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using ν-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 ν-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 ν-NWFETs as the building block for HNN. The simulation has been further extended to ν-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 ν-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 ν-NWFET devices confirm the dependence of turn-off voltages on the (synaptic) weight of each gate. The presented ν-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.
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