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A 915-MHz energy-enhanced wake-up receiver (EE WuRx) is proposed for wireless energy-harvesting (WEH) systems based on a modified legacy device. With the presented frequency-hopping two-tone modulation (FH TTM), the receiver converts the in-band harvesting energy (HE) from the legacy device into the data power with reduced constraint to the HE frequency. Fabricated in a 65-nm CMOS process, the proposed wake-up receiver (WuRx) achieves a −90-dBm EE sensitivity when receiving a continue-wave (CW) energy of −25 dBm and gives a sensitivity improvement of 26.4 dB compared with the case without HE, while consuming $19~\mu \text{W}$ at a 5-kb/s data rate. Upon the −90-dBm EE sensitivity, the WuRx performs 46.5- and 22-dB tolerances to a 100-kb/s amplitude-modulated (AM) blocker and an 8-MS/s quadrature-AM blocker, respectively.

The ability to sense light, heat, and touch is vital for human beings, underpinning the interaction between humans and the environment. To mimic the biological perception system, the sensory system converts external light, heat, and mechanical inputs into electrical signals, then processing and storing the data in digital hardware before providing feedback. However, modern digital sensing‐processing systems based on the von Neumann architecture are facing significant challenges in power consumption and latency due to the unprecedented increase in data size and algorithm complexity. A promising solution is to integrate sensors, memory, and control. Here the “state‐of‐the‐art” fusion systems involved in the sensing of visual, olfactory, tactile, visual signals, and control is reviewed. The challenges in high performance and reliability are also discussed. Sensing, processing, and responding to external stimuli signals is the aim of intelligent sensory perception systems. Most of the modern sensory systems separate sensors, memory, and control modules, resulting in high power consumption and latency. A promising solution is integrating them together. Here the “state‐of‐the‐art” fusion systems are reviewed.

The R-2R resistor ladder is one of the best topologies for implementing compact-sized digital-to-analog converter (DAC) arrays in implantable neuro-stimulators. However, it has a limited resolution and considerable inter-channel variation due to component mismatches. To avoid losing analog information, we present sub-radix-2 DAC implemented by the R-βR resistor ladder in this paper. The digital successive approximation register (DSAR) algorithm corrects the transfer function of DACs based on their actual bit weights. Furthermore, a low-cost in situadaptive bit-weight calibration (ABC) algorithm drives the analog output error between two DACs to zero by adjusting their bit weights automatically. The simulation results show that the proposed algorithm can calibrate the non-linear transfer function of each DAC and the gain error among multiple channels in the background.