Browsing by Author "Wang, Tang-Hsuan"

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2.5D Heterogeneously Integrated Microsystem for High-Density Neural Sensing Applications
(2014-12-01)
Huang, Po-Tsang
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Wu, Shang-Lin
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Huang, Yu-Chieh
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Chou, Lei-Chun
;
Huang, Teng-Chieh
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 mu-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of mu-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 x 24 mu-probe array with embedded TSVs is fabricated on a 5 x 5 mm2 chip and bonded on the back side of the interposer. Thus, each channel contains 6 x 6 mu-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 (mu-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 mu W for 16-channel neural sensing.
Energy-Efficient Configurable Discrete Wavelet Transform for Neural Sensing Applications
(2014-01-01)
Wang, Tang-Hsuan
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Huang, Po-Tsang
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Chen, Kuan-Neng
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Chiou, Jin-Chem
;
Chen, Kuo-Hua
Highly integrated neural sensing microsystems are crucial to capture accurate signals for brain function investigations. In this paper, an energy-efficient configurable lifting-based discrete wavelet transform (DWT) is proposed for a high-density neural sensing microsystems to extract the features of neural signals by filtering the signals into different frequency bands. Based on the lifting-based DWT algorithm, the area and power consumption can be reduced by decreasing the computation circuits. Additionally, both the time window and mother wavelets can be adjusted via the configurable datapth. Moreover, the power-gating and clock-gating techniques are utilized to further reduce the energy consumption for the energy-limited bio-systems. The proposed configurable DWT is designed and implemented using TSMC 65nm CMOS low power process with total area of 0.11 mm(2) and power consumption of 26 mu W. Moreover, this proposed DWT is also implemented in Lattice MachXO2-1200 FPGA and integrated in a 2.5D heterogeneously integrated high-density neural-sensing microsystem with the power consumption of 211.2 mu W.