| Autor: |
Alea, Mark Daniel, Safa, Ali, Giacomozzi, Flavio, Adami, Andrea, Temel, Inci Ruya, Rosa, Maria Atalaia, Lorenzelli, Leandro, Gielen, Georges |
| Zdroj: |
IEEE Transactions on Biomedical Circuits and Systems; December 2024, Vol. 18 Issue: 6 p1308-1320, 13p |
| Abstrakt: |
This paper presents an electronic skin (e-skin) taxel array readout chip in 0.18$\mu$m CMOS technology, achieving the highest reported spatial resolution of 200$\mu$m, comparable to human fingertips. A key innovation is the integration on chip of a 12$\times$16 polyvinylidene fluoride (PVDF)-based piezoelectric sensor array with per-taxel signal conditioning frontend and spiking readout combined with local embedded neuromorphic first-order processing through Complex Receptive Fields (CRFs). Experimental results show that Spiking Neural Network (SNN)-based classification of the chip's spatiotemporal spiking output for input tactile stimuli such as texture and flutter frequency achieves excellent accuracies up to 97.1$\%$ 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 75$\mu$W-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. |
| Databáze: |
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