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
Mark Daniel Alea, Ali Safa, Flavio Giacomozzi, Andrea Adami, Inci Rüya Temel, Maria Atalaia Rosa, Leandro Lorenzelli, Georges Gielen
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.