In this article, a highly reliable radio frequency (RF) wake-up receiver (WuRx) is presented for electronic toll collection (ETC) applications. An intelligent digital controller (IDC) is proposed as the final stage for improving WuRx reliability and replacing complex analog blocks. With IDC, high reliability and accuracy are achieved by sensing and ensuring the successive, configurable number of wake-up signal cycles before enabling power-hungry RF transceiver. The IDC and range communication (RC) oscillator current consumption is reduced by a presented self-hibernation technique during the non-wake-up period. For accommodating wake-up signal frequency variation and enhancing WuRx accuracy, a digital hysteresis is incorporated. To avoid uncertain conditions during poor and false wake-up, a watch-dog timer for IDC self-recovery is integrated. During wake-up, the digital controller consumes 34.62 nW power and draws 38.47 nA current from a 0.9 V supply. In self-hibernation mode, its current reduces to 9.7 nA. It is fully synthesizable and needs 809 gates for its implementation in a 130 nm CMOS process with a 94 × 82 µm2 area. The WuRx measured power consumption is 2.48 µW, has −46 dBm sensitivity, and a 0.484 mm² chip area.

This paper presents a register-transistor level (RTL) based convolutional neural network (CNN) for biosensor applications. Biosensor-based diseases detection by DNA identification using biosensors is currently needed. We proposed a synthesizable RTL-based CNN architecture for this purpose. The adopted technique of parallel computation of multiplication and accumulation (MAC) approach optimizes the hardware overhead by significantly reducing the arithmetic calculation and achieves instant results. While multiplier bank sharing throughout the convolutional operation with fully connected operation significantly reduces the implementation area. The CNN model is trained in MATLAB® on MNIST® handwritten dataset. For validation, the image pixel array from MNIST® handwritten dataset is applied on proposed RTL-based CNN architecture for biosensor applications in ModelSim®. The consistency is checked with multiple test samples and 92% accuracy is achieved. The proposed idea is implemented in 28 nm CMOS technology. It occupies 9.986 mm2 of the total area. The power requirement is 2.93 W from 1.8 V supply. The total time taken is 8.6538 ms.

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