Georgios Panagopoulos

Ilias Exouzidis, Alberto Garcia-Ortiz, George Floros et al.

Through-silicon vias (TSVs) enable dense vertical interconnects in 3D-IC and chiplet systems, but their metal-oxide-silicon structure introduces significant parasitic coupling paths that can degrade the spectral purity of sensitive RF blocks. This paper presents a compact, design-oriented methodology for assessing TSV-induced substrate noise in mixed-signal circuits. We derive a closed-form analytical three-port RLGC macromodel for a Signal-Ground TSV pair that explicitly exposes the substrate node. The methodology is validated using a three-stage Ring VCO designed in a 22 nm FD-SOI technology, where specific RF devices from the process design kit (PDK) provide direct access to the transistor substrate terminals for controlled noise injection. Multi-tone Harmonic Balance simulations in Spectre RF quantify the impact of TSV aggressors on the oscillator's output spectrum. The results indicate that an aggressor of 1 GHz, 0.5 V$_{pp}$ induces a primary sideband spur of -35.2 dBc. Sensitivity characterization reveals that the magnitude of these sideband spurs increases monotonically with the aggressor amplitude. Furthermore, frequency sweeps demonstrate a low-pass coupling response, where the induced spur magnitude decreases from -20.2 dBc at 500 MHz to -33.1 dBc at 2 GHz.

Maha Shatta, Konstantinos Balaskas, Paula Carolina Lozano Duarte et al.

Flexible Electronics (FE) offer a promising alternative to rigid silicon-based hardware for wearable healthcare devices, enabling lightweight, conformable, and low-cost systems. However, their limited integration density and large feature sizes impose strict area and power constraints, making ML-based healthcare systems-integrating analog frontend, feature extraction and classifier-particularly challenging. Existing FE solutions often neglect potential system-wide solutions and focus on the classifier, overlooking the substantial hardware cost of feature extraction and Analog-to-Digital Converters (ADCs)-both major contributors to area and power consumption. In this work, we present a holistic mixed-signal feature-to-classifier co-design framework for flexible smart wearable systems. To the best of our knowledge, we design the first analog feature extractors in FE, significantly reducing feature extraction cost. We further propose an hardware-aware NAS-inspired feature selection strategy within ML training, enabling efficient, application-specific designs. Our evaluation on healthcare benchmarks shows our approach delivers highly accurate, ultra-area-efficient flexible systems-ideal for disposable, low-power wearable monitoring.