Conference Agenda

This work investigates proposed maximum-powerpoint tracking (MPPT) approaches for a fully integrated hybrid voltage converter (H-VC) intended for alternative energy sources in low-power IoT systems, where passive components and power supply rail stability impose severe constraints. A monolithic Hybrid-Dual-Path (HDPC) voltage converter with an on-chip LDC = 15.07 nH inductor and strongly limited total capacitance (CIN = COUT ≈ 2.5 nF ) is considered, with 1.5 V output voltage regulation provided by a continuously operating shunt-type regulator. The two MPPT concepts are analyzed: (i) iMPPT based on the fractional open-circuit-voltage (FOCV) principle, which is energy-effcient but requires periodic VIN,OC sampling via brief AES disconnection, and (ii) oMPPT, which maximizes extracted load power by optimizing the combined product ηMPPT.ηVC and provides instantaneous power observability in a form of digital code. The feasibility and performance are evaluated in Cadence using a 65 nm CMOS technology and a realistic PV model (KXOB25-14X1F), across irradiance levels of 5–500 W/m2 corresponding to PIN,MPP from 114 µW to 14.8 mW . Simulation results show that both approaches regulate operation over a wide switching-frequency span of roughly 0.9–62 MHz via Frequency-Shift Modulation Control (FSM-C). Overall, iMPPT is favored when minimal control overhead is paramount, whereas oMPPT offers superior scalability and algorithmic flexibility for broader AES applicability at the cost of up to 5.5 times higher power consumption.

Monte Carlo circuit simulation is impossible without statistical compact models capable of faithfully reproducing the probability distributions of the circuit components' electrical characteristics. While such models do exist for devices fabricated using established processes and working under typical operating conditions, this is not always true of emerging devices or ones working at cryogenic temperatures. Although approximation MOSFET models can be used in place of physics-based ones, making them suitable for statistical simulation usually requires detailed information about the distributions of the transistors' physical parameters, which are difficult to extract. We propose an alternative approach that relies solely on \iv measurements of a set of transistors of various geometries. Based on these data, a conditional variational autoencoder (CVAE) is trained. It is subsequently used to generate arbitrarily large I-V datasets that match the statistical properties of the training data. The model can smoothly generalize between the training cases, which also enables reliable generation of I-V data for other transistor geometries than those used for training.

This approach has been shown to reliably reproduce the probability distributions of various figures of merit of an operational amplifier.

This paper presents a microstrip bandpass filter (BPF) with strong second-harmonic (2f0) suppression for a 6G-relevant frequency band. Starting from an LC prototype, the filter is synthesized using a harmonic-controlled transmission-line structure that shifts the second passband away from the 2f0 region while preserving the desired passband response. A prototype was fabricated and experimentally verified. The measured results show an insertion loss below 1.5 dB in the target passband and suppression better than 50 dB in the 2f0 region, demonstrating that the proposed method provides an efficient and practical solution for microstrip filters requiring strong harmonic suppression.