A Fingertip-Mimicking 12$\times$16 200 $\mu$m-Resolution <i>e</i>-Skin Taxel Readout Chip With Per-Taxel Spiking Readout and Embedded Receptive Field Processing

Abstract

This paper presents an electronic skin (e-skin) taxel array readout chip in 0.18m CMOS technology, achieving the highest reported spatial resolution of 200m, comparable to human fingertips. A key innovation is the integration on chip of a 1216 polyvinylidene fluoride (PVDF)-based piezoelectric sensor array with per-taxel signal conditioning frontend and spiking readout combined with local embedded neuromorphic first-order processing through Complex Receptive Fields (CRFs). Experimental results show that Spiking Neural Network (SNN)-based classification of the chip's spatiotemporal spiking output for input tactile stimuli such as texture and flutter frequency achieves excellent accuracies up to 97.1 and 99.2, respectively. SNN-based classification of the indentation period applied to the on-chip PVDF sensors achieved 95.5 classification accuracy, despite using only a small 256-neuron SNN classifier, a low equivalent spike encoding resolution of 3-5 bits, and a sub-Nyquist 2.2kevent/s population spiking rate, a state-of-the-art power consumption of 12.33nW per-taxel, and 75W-5mW for the entire chip is obtained. Finally, a comparison of the texture classification accuracies between two on-chip spike encoder outputs shows that the proposed neuromorphic level-crossing sampling (N-LCS) architecture with a decaying threshold outperforms the conventional bipolar level-crossing sampling (LCS) architecture with fixed threshold.