This talk introduces a cutting-edge energy harvesting integrated circuit for battery-free IoT devices. It covers the consideration in the circuit design for a single-inductor multi-source multi-output converter, with particular emphasis on efficient methodologies for extracting energy from diverse transducers. The introduction of adaptive peak-inductor-current control is highlighted as a means to manage output voltage ripple, enhancing converter efficiency over wide input and output voltage range.

  Po-Hung Chen received the M.S. degree from the Institute of Electronics, National Chiao Tung University, Hsinchu, Taiwan, in 2007, and the Ph.D. degree in electrical engineering from The University of Tokyo, Tokyo, Japan, in 2012. In 2011, he was a Visiting Scholar with the University of California at Berkeley, Berkeley, CA, USA, where he conducted research in fully integrated power management circuits for RISC-V processors. He is currently a Professor with the Institute of Electronics, National Yang Ming Chiao Tung University. His research focuses on power management integrated circuits, with a special emphasis on energy harvesting, battery management, battery charger, and wireless power transmission.

  Digital technologies are rapidly advancing in our society, and the performance of analog circuits is improving, leading to steady growth in their market share. We have observed that, in many cases, analog components may be smaller than their digital counterparts, yet they often pose challenges. Testing analog circuits plays a crucial role in achieving both reliability and cost-effectiveness, especially in computer, smartphone, Internet of Things (IoT), artificial intelligence (AI), and automotive systems. Analog testing presents challenges related to circuit design, signal processing algorithms, and measurement methods/systems. This talk introduces research results from the author’s group in this field, conducted in collaboration with industry partners. It’s important to note that production testing and measurement/characterization for integrated circuits (ICs) share similarities but also have distinct differences. During this presentation, we will focus on the former. The topics covered include:

1. Waveform Sampling Methods: Explore techniques like golden ratio sampling and residue sampling.
2. Time-to-Digital Converters (TDCs): Understand their role in precise timing measurements.
3. ADC Testing and Calibration: Discuss strategies for ensuring accurate analog-to-digital conversion.
4. Test Input Signal Generation Algorithms: Learn about algorithms used to create test input signals.

  Haruo Kobayashi received the B.S. and M.S. degrees in information physics from the University of Tokyo in 1980 and 1982, respectively. He also obtained an M.S. degree in electrical engineering from the University of California, Los Angeles (UCLA) in 1989, and a Ph.D. degree in electrical engineering from Waseda University in 1995. In 1982, he joined Yokogawa Electric Corp. in Tokyo, Japan, where he was engaged in research and development related to measuring instruments and mini-supercomputers. In 1997, he became an Associate Professor at Gunma University and was later promoted to Full Professor in the Division of Electronics and Informatics. He currently holds the title of Professor Emeritus. He has served on the committees of many international conferences, including TJCAS 2019 in Nikko, Japan, where he served as the general chair. His research interests include mixed-signal integrated circuit design and test, as well as signal processing algorithms. He has published over 160 journal papers and 450 international conference papers. Additionally, he has supervised 20 Ph.D. students and 160 M.S. students. In 2003, he received the Yokoyama Award in Science and Technology. He is a Senior Member of IEEE, IEICE, and IEEJ.

  Machine learning has rapidly advanced in recent years, finding various applications in electronic design automation. This talk will focus on how machine learning can assist several physical design tasks, including partitioning, placement, and routing, to improve solution quality. Case studies will be presented to demonstrate the practical benefits and advancements enabled by machine learning for VLSI physical design.

  Ting-Chi Wang received his B.S. degree in Computer Science and Information Engineering from National Taiwan University, Taiwan, in 1986. He earned both his M.S. and Ph.D. degrees in Computer Sciences from the University of Texas at Austin, USA, in 1990 and 1993, respectively. Currently, he is a Professor in the Department of Computer Science at National Tsing Hua University, Taiwan. His primary research interest lies in VLSI physical design. He received Best Paper Awards from ASP-DAC 2006 and ISPD 2015, and supervised students who won prizes in several ISPD contests. He served as the General Chair of ASP-DAC 2022 and SASIMI 2015, and as the General Co-Chair of VLSI-DAT 2019. Additionally, he was the Technical Program Committee (TPC) Co-Chair of VLSI-DAT 2018, and a TPC member of ASP-DAC, DAC, DATE, ICCAD, ISLPED, and ISPD. He also served as an Associate Editor of ACM TODAES from 2013 to 2016 and as the Chair of IEEE CEDA Taipei Chapter from 2014 to 2015.