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一种具有逐像素尖峰读出和嵌入式感受野处理功能的模仿指尖的1216 200μm分辨率电子皮肤像素读出芯片。

A Fingertip-Mimicking 1216 200 m-Resolution e-Skin Taxel Readout Chip With Per-Taxel Spiking Readout and Embedded Receptive Field Processing.

作者信息

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

出版信息

IEEE Trans Biomed Circuits Syst. 2024 Dec;18(6):1308-1320. doi: 10.1109/TBCAS.2024.3387545. Epub 2024 Dec 9.

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.

摘要

本文介绍了一款采用0.18μm CMOS技术的电子皮肤(e-skin)像素阵列读出芯片,其实现了高达200μm的空间分辨率,这是目前报道的最高水平,可与人类指尖相媲美。一项关键创新在于,芯片集成了基于12×16聚偏二氟乙烯(PVDF)的压电传感器阵列、每个像素的信号调理前端以及尖峰读出,并通过复感受野(CRF)进行局部嵌入式神经形态一阶处理。实验结果表明,基于脉冲神经网络(SNN)对芯片时空尖峰输出进行分类,以识别诸如纹理和颤动频率等输入触觉刺激,分别实现了高达97.1%和99.2%的优异准确率。尽管仅使用了一个小型的256神经元SNN分类器、3至5位的低等效尖峰编码分辨率、2.2kHz事件/秒的亚奈奎斯特群体尖峰率、每个像素12.33nW的先进功耗以及整个芯片75μW至5mW的功耗,但基于SNN对施加到片上PVDF传感器的压痕周期进行分类仍实现了95.5%的分类准确率。最后,对两种片上尖峰编码器输出之间的纹理分类准确率进行比较,结果表明,所提出的具有衰减阈值的神经形态过零采样(N-LCS)架构优于具有固定阈值的传统双极过零采样(LCS)架构。

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