Alea Mark Daniel, Safa Ali, Giacomozzi Flavio, Adami Andrea, Temel Inci Ruya, Rosa Maria Atalaia, Lorenzelli Leandro, Gielen Georges
IEEE Trans Biomed Circuits Syst. 2024 Dec;18(6):1308-1320. doi: 10.1109/TBCAS.2024.3387545. Epub 2024 Dec 9.
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.
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。