TY - GEN
T1 - A Fingertip-Mimicking 12×16 200μm-Resolution e-skin Taxel Readout Chip with per-Taxel Spiking Readout and Embedded Receptive Field Processing
AU - Alea, Mark Daniel
AU - Safa, Ali
AU - Giacomozzi, Flavio
AU - Adami, Andrea
AU - Temel, Inci Rüya
AU - Lorenzelli, Leandro
AU - Gielen, Georges
N1 - Publisher Copyright:
© 2023 JSAP.
PY - 2023
Y1 - 2023
N2 - This work describes an electronic skin (e-skin) taxel readout chip in 0.18 μ m CMOS technology, achieving the highest reported spatial resolution of 200 μ m, comparable to human fingertips. A key innovation is the integration on chip of a12 × 16 taxel array with per-taxel signal conditioning frontend and spiking readout combined with embedded neuromorphic first-order processing through Complex Receptive Fields (CRFs). The chip has been designed to incorporate a polyvinylidene fluoride (PVDF)-based piezoelectric sensor layer. 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% of classification accuracy, respectively. This is despite using only a small 256-neuron SNN classifier, a low equivalent spike encoding resolution of 3-4 bits, a sub-Nyquist 2.2kHz population spiking rate, and a state-of-the-art per-taxel (12.33nW) and system (75 μ W -5mW) power consumption.
AB - This work describes an electronic skin (e-skin) taxel readout chip in 0.18 μ m CMOS technology, achieving the highest reported spatial resolution of 200 μ m, comparable to human fingertips. A key innovation is the integration on chip of a12 × 16 taxel array with per-taxel signal conditioning frontend and spiking readout combined with embedded neuromorphic first-order processing through Complex Receptive Fields (CRFs). The chip has been designed to incorporate a polyvinylidene fluoride (PVDF)-based piezoelectric sensor layer. 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% of classification accuracy, respectively. This is despite using only a small 256-neuron SNN classifier, a low equivalent spike encoding resolution of 3-4 bits, a sub-Nyquist 2.2kHz population spiking rate, and a state-of-the-art per-taxel (12.33nW) and system (75 μ W -5mW) power consumption.
UR - https://www.scopus.com/pages/publications/85167569749
U2 - 10.23919/VLSITechnologyandCir57934.2023.10185346
DO - 10.23919/VLSITechnologyandCir57934.2023.10185346
M3 - Conference contribution
AN - SCOPUS:85167569749
T3 - Digest of Technical Papers - Symposium on VLSI Technology
BT - 2023 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2023
Y2 - 11 June 2023 through 16 June 2023
ER -