24.5 A 0.5V 1.27mW nose-on-a-chip for rapid diagnosis of ventilator-associated pneumonia

Abstract

Ventilator-associated pneumonia (VAP) is the most frequently acquired infection among patients that receive mechanical ventilation in the intensive-care unit (ICU). The mortality rate for VAP lies in the 20-to-50% range and could be even higher in some ICUs. A standard operation procedure to VAP treatment includes a sequence of chest radiography, sputum gram stain, sputum culture, and empiric therapy, initially with antibiotics covering broad pathogens. However, collection of the gram stain and culture of lower respiratory tract specimen is usually not time-efficient (up to 5 days), delaying the initiation of therapy and unacceptable for critically ill patients. A rapid and accurate diagnosis for VAP is therefore crucial, but still unavailable. It is known that microorganisms generate complex metabolites during infection. Fast detection is feasible by examining metabolic wastes in proximal end of the expiratory device, demanding a miniaturized, battery-powered, gas-sensing device. In this work, a fully integrated low-power nose-on-a-chip with a robust learning kernel is developed for such a vital clinical need. Figure 24.5.1 shows the target application scenario and a top-level system view of the nose-on-a-chip. With a 3D structure, the chip integrates 8 sensors on top and processing circuits at the bottom, completely in a standard CMOS process. The signal-processing circuits include an 8-channel adaptive sensor interface, a SAR analog-to-digital converter (ADC), a RISC processor core with an 8K×32b cache memory, and a dedicated continuous restricted Boltzmann machine (CRBM) kernel for data clustering. With the scalability to form a larger array of chips, massive sensor information can be processed efficiently in parallel to enhance sensing performance for a diversity of applications. Figure 24.5.2 describes the principle of the nose-on-a-chip and the nanocomposite sensing materials. Distinct sensing materials deposited on the interdigitated electrodes (IDE) form an array of sensors. The collective response from the sensor array constructs unique gas fingerprints. Polymer-carbon composites are used for sensing materials. The carbon-based materials can be: carbon black, carbon nanotube, and mesoporous carbon, where mesoporous carbon has demonstrated superior sensitivity and reversibility. Mesoporous carbon is fabricated from platelet-shaped mesoporous SBA-15 silica and polymers are grown onto the carbon, as shown in the SEM images.The sensing material is deposited on the IDE, as shown in Figure 24.5.3. The passivation layer is removed with the 400×400μm 2 opening windows. An 8-channel adaptive interface reads out the sensor signals. The interface circuit works as a negative-feedback loop to tune out long time constant signals such as temperature, humidity, and background odors. This sensor interface consumes 215μW. A 0.5V 10b SAR ADC with a charge-average switching (CAS) technique [1] is adopted. The CAS DAC generates top-plate voltage shift by charge averaging instead of conventional charging and discharging oper...

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