Low-Cost Indirect Measurements for Power-Efficient In-Field Optimization of Configurable Analog Front-Ends with Self-X Properties: A Hardware Implementation

This paper presents a practical implementation and measurement results of power-efficient chip performance optimization, utilizing low-cost indirect methods to support self-X properties (self-calibration, self-healing, self-optimization, etc.) for in-field optimization analog front-end sensory electronics with XFAB 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The reconfigurable, fully differential current-feedback instrumentation amplifier (CFIA) is intrinsically optimized by employing single test sinusoidal signal stimulus measuring the total harmonic distortion (THD) at output. To enhance process, experience replay particle swarm (ERPSO) algorithm utilized as an artificial intelligence (AI) agent, implemented hardware level, optimize characteristics CFIA. ERPSO extends selection producer capabilities classical PSO methodology incorporating buffer mitigate likelihood being trapped in local optima. Furthermore, CFIA circuit has been integrated simple power-monitoring module assess power consumption solution, achieve reliable configuration. showed approximate 34% increase efficiency while achieving targeted THD value −72 dB, 1 Vp-p input frequency MHz, consuming approximately 53 mW power. Preliminary tests conducted on fabricated chip, using default configuration pattern extrapolated from post-layout simulations, revealed unacceptable behavior Nevertheless, proposed successfully restored circuit’s performance, resulting robust design that meets achieved phase.