An energy-efficient application-specific integrated circuit (ASIC) featured with a work-on-demand protocol is designed for wireless body sensor networks (WBSNs) in medical applications. Dedicated for ultra-low-power wireless sensor nodes, the ASIC consists of a low-power microcontroller unit (MCU), a power-management unit (PMU), reconfigurable sensor interfaces, communication ports controlling a wireless transceiver, and an integrated passive radio-frequency (RF) receiver with energy harvesting ability. The MCU, together with the PMU, provides quite flexible communication and power-control modes for energy-efficient operations. The always-on passive RF receiver with an RF energy harvesting block offers the sensor nodes the capability of work-on-demand with zero standby power. Fabricated in standard 0.18-¿m complementary metal-oxide semiconductor technology, the ASIC occupies a die area of 2 mm × 2.5 mm. A wireless body sensor network sensor-node prototype using this ASIC only consumes < 10-nA current under the passive standby mode, and < 10 ¿A under the active standby mode, when supplied by a 3-V battery.
This paper presents the design of a wireless capsule endoscope system. The proposed system is mainly composed of a CMOS image sensor, a RF transceiver and a low-power controlling and processing application specific integrated circuit (ASIC). Several design challenges involving system power reduction, system miniaturization and wireless wake-up method are resolved by employing optimized system architecture, integration of an area and power efficient image compression module, a power management unit (PMU) and a novel wireless wake-up subsystem with zero standby current in the ASIC design. The ASIC has been fabricated in 0.18-mum CMOS technology with a die area of 3.4 mm * 3.3 mm. The digital baseband can work under a power supply down to 0.95 V with a power dissipation of 1.3 mW. The prototype capsule based on the ASIC and a data recorder has been developed. Test result shows that proposed system architecture with local image compression lead to an average of 45% energy reduction for transmitting an image frame.
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