**6. Conclusions**

This paper has proposed and evaluated a wide load range SC converter aiming on sensor nodes, where the load power requirements varies based on a schedule. The proposed voltage converter is proactively reconfigured to ensure high conversion efficiency, for various load current and voltage requirements. The converter utilizes multiphase operation to mitigate output ripple voltage and

phase frequency modulation using an integrated oscillator to maintain high conversion e fficiency across the load range. In addition, the converter employs the proposed distributed non-overlapping generation to reduce area and power consumption. The measurements demonstrate above 80% efficiency for output current ranging from 10 μA to 10 mA. Due to its wide load range, the converter is a strong candidate for driving sensor nodes, where the power requirements often change by orders of magnitude between sleep and active state. The converter architecture allows for upscaling the number of phases/capacitor banks to further reduce output ripple. Moreover, the converter architecture can be evolved to incorporate existing feedback-based PFM to fine-tune its oscillator frequency against minor load variations.

**Author Contributions:** Conceptualization, S.A.; methodology, S.A., S.A.A.S., H.K.; software, S.A. and S.A.A.S.; validation, S.A. and S.A.A.S.; formal analysis, S.A.; investigation, S.A. and S.A.A.S.; Writing—original draft preparation, S.A.; Writing—review and editing, S.A.A.S. and H.K.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Grand Information Technology Research Center support program (IITP-2020-0-01462) supervised by the IITP and funded by the MSIT (Ministry of Science and ICT) of Korean government, and it was also supported by Industry coupled IoT Semiconductor System Convergence Nurturing Center under System Semiconductor Convergence Specialist Nurturing Project funded by the National Research Foundation (NRF) of Korea (2020M3H2A107678611). This work was also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) gran<sup>t</sup> funded by the Korea governmen<sup>t</sup> (MSIT) (No.2020-0-01304, Development of Self-learnable Mobile Recursive Neural Network Processor Technology).

**Conflicts of Interest:** The authors declare no conflict of interest.
