2.5D heterogeneously integrated microsystem for high-density neural sensing applications

Po-Tsang Huang; Shang Lin Wu; Yu Chieh Huang; Lei Chun Chou; Teng Chieh Huang; Tang Hsuan Wang; Yu Rou Lin; Chuan An Cheng; Wen Wei Shen; Ching Te Chuang; Kuan-Neng Chen; Jin-Chern Chiou; Wei Hwang; Ho Ming Tong

doi:10.1109/TBCAS.2014.2385061

2.5D heterogeneously integrated microsystem for high-density neural sensing applications

Po-Tsang Huang, Shang Lin Wu, Yu Chieh Huang, Lei Chun Chou, Teng Chieh Huang, Tang Hsuan Wang, Yu Rou Lin, Chuan An Cheng, Wen Wei Shen, Ching Te Chuang, Kuan-Neng Chen, Jin-Chern Chiou, Wei Hwang, Ho Ming Tong

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8 Scopus citations

Abstract

Heterogeneously integrated and miniaturized neural sensing microsystems are crucial for brain function investigation. In this paper, a 2.5D heterogeneously integrated bio-sensing microsystem with μ-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of μ-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 × 24 μ-probe array with embedded TSVs is fabricated on a 5 × 5 mm² chip and bonded on the back side of the interposer. Thus, each channel contains 6 × 6 μ-probes with embedded TSVs. Additionally, the 4 dies are bonded on the front side of the interposer and designed for biopotential acquisition, feature extraction and classification via low-power analog front-end (AFE) circuits, area-power-efficient analog-to-digital converters (ADCs), configurable discrete wavelet transforms (DWTs), filters, and a MCU. An on-interposer bus (μ-SPI) is designed for transferring data on the interposer. Finally, the successful in-vivo test demonstrated the proposed 2.5D heterogeneously integrated bio-sensing microsystem. The overall power of this microsystem is only 676.3 μW for 16-channel neural sensing.

Original language	English
Article number	7001275
Pages (from-to)	810-823
Number of pages	14
Journal	IEEE Transactions on Biomedical Circuits and Systems
Volume	8
Issue number	6
DOIs	https://doi.org/10.1109/TBCAS.2014.2385061
State	Published - 1 Dec 2014

Keywords

2.5D heterogeneous integration
Analog front-end (AFE)
Configurable discrete wavelet transform (DWT)
Low-power ADC
Neural sensing microsystem
On-interposer bus
Through-silicon-via (TSV)
μ-probes

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Huang, P.-T., Wu, S. L., Huang, Y. C., Chou, L. C., Huang, T. C., Wang, T. H., Lin, Y. R., Cheng, C. A., Shen, W. W., Chuang, C. T., Chen, K.-N., Chiou, J.-C., Hwang, W., & Tong, H. M. (2014). 2.5D heterogeneously integrated microsystem for high-density neural sensing applications. IEEE Transactions on Biomedical Circuits and Systems, 8(6), 810-823. Article 7001275. https://doi.org/10.1109/TBCAS.2014.2385061

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title = "2.5D heterogeneously integrated microsystem for high-density neural sensing applications",

abstract = "Heterogeneously integrated and miniaturized neural sensing microsystems are crucial for brain function investigation. In this paper, a 2.5D heterogeneously integrated bio-sensing microsystem with μ-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of μ-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 × 24 μ-probe array with embedded TSVs is fabricated on a 5 × 5 mm2 chip and bonded on the back side of the interposer. Thus, each channel contains 6 × 6 μ-probes with embedded TSVs. Additionally, the 4 dies are bonded on the front side of the interposer and designed for biopotential acquisition, feature extraction and classification via low-power analog front-end (AFE) circuits, area-power-efficient analog-to-digital converters (ADCs), configurable discrete wavelet transforms (DWTs), filters, and a MCU. An on-interposer bus (μ-SPI) is designed for transferring data on the interposer. Finally, the successful in-vivo test demonstrated the proposed 2.5D heterogeneously integrated bio-sensing microsystem. The overall power of this microsystem is only 676.3 μW for 16-channel neural sensing.",

keywords = "2.5D heterogeneous integration, Analog front-end (AFE), Configurable discrete wavelet transform (DWT), Low-power ADC, Neural sensing microsystem, On-interposer bus, Through-silicon-via (TSV), μ-probes",

author = "Po-Tsang Huang and Wu, {Shang Lin} and Huang, {Yu Chieh} and Chou, {Lei Chun} and Huang, {Teng Chieh} and Wang, {Tang Hsuan} and Lin, {Yu Rou} and Cheng, {Chuan An} and Shen, {Wen Wei} and Chuang, {Ching Te} and Kuan-Neng Chen and Jin-Chern Chiou and Wei Hwang and Tong, {Ho Ming}",

note = "Publisher Copyright: {\textcopyright} 2007-2012 IEEE.",

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Huang, P-T, Wu, SL, Huang, YC, Chou, LC, Huang, TC, Wang, TH, Lin, YR, Cheng, CA, Shen, WW, Chuang, CT, Chen, K-N , Chiou, J-C, Hwang, W & Tong, HM 2014, '2.5D heterogeneously integrated microsystem for high-density neural sensing applications', IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 6, 7001275, pp. 810-823. https://doi.org/10.1109/TBCAS.2014.2385061

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AU - Wu, Shang Lin

AU - Huang, Yu Chieh

AU - Chou, Lei Chun

AU - Huang, Teng Chieh

AU - Wang, Tang Hsuan

AU - Lin, Yu Rou

AU - Cheng, Chuan An

AU - Shen, Wen Wei

AU - Chuang, Ching Te

AU - Chen, Kuan-Neng

AU - Chiou, Jin-Chern

AU - Hwang, Wei

AU - Tong, Ho Ming

PY - 2014/12/1

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N2 - Heterogeneously integrated and miniaturized neural sensing microsystems are crucial for brain function investigation. In this paper, a 2.5D heterogeneously integrated bio-sensing microsystem with μ-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of μ-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 × 24 μ-probe array with embedded TSVs is fabricated on a 5 × 5 mm2 chip and bonded on the back side of the interposer. Thus, each channel contains 6 × 6 μ-probes with embedded TSVs. Additionally, the 4 dies are bonded on the front side of the interposer and designed for biopotential acquisition, feature extraction and classification via low-power analog front-end (AFE) circuits, area-power-efficient analog-to-digital converters (ADCs), configurable discrete wavelet transforms (DWTs), filters, and a MCU. An on-interposer bus (μ-SPI) is designed for transferring data on the interposer. Finally, the successful in-vivo test demonstrated the proposed 2.5D heterogeneously integrated bio-sensing microsystem. The overall power of this microsystem is only 676.3 μW for 16-channel neural sensing.

AB - Heterogeneously integrated and miniaturized neural sensing microsystems are crucial for brain function investigation. In this paper, a 2.5D heterogeneously integrated bio-sensing microsystem with μ-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of μ-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 × 24 μ-probe array with embedded TSVs is fabricated on a 5 × 5 mm2 chip and bonded on the back side of the interposer. Thus, each channel contains 6 × 6 μ-probes with embedded TSVs. Additionally, the 4 dies are bonded on the front side of the interposer and designed for biopotential acquisition, feature extraction and classification via low-power analog front-end (AFE) circuits, area-power-efficient analog-to-digital converters (ADCs), configurable discrete wavelet transforms (DWTs), filters, and a MCU. An on-interposer bus (μ-SPI) is designed for transferring data on the interposer. Finally, the successful in-vivo test demonstrated the proposed 2.5D heterogeneously integrated bio-sensing microsystem. The overall power of this microsystem is only 676.3 μW for 16-channel neural sensing.

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KW - Configurable discrete wavelet transform (DWT)

KW - Low-power ADC

KW - Neural sensing microsystem

KW - On-interposer bus

KW - Through-silicon-via (TSV)

KW - μ-probes

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JF - IEEE Transactions on Biomedical Circuits and Systems

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ER -

2.5D heterogeneously integrated microsystem for high-density neural sensing applications

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Keywords

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