In-Field Dynamic Performance Optimization of Sensory Readout Electronics with Self-X Properties using Low Cost and Power-Efficient Indirect Measurement Method

Abstract

This contribution presents the intrinsic implementation of the in-field dynamic optimization of the analog front-end readout circuitry for the robust realization of the sensory interface system. The optimization utilizes a low-cost measurement setup to optimize the performance and the power efficiency of the reconfigurable fully-differential indirect current feedback instrumentation amplifier (CFIA) to support the self-X properties (selfcalibration, self-optimization, etc). A single sinusoidal stimulus is required to optimize the CFIA characteristics by using an experience replay particle swarm optimization (ERPSO) algorithm. A power monitoring module (PMM) is integrated into the CFIA to provide the DC power information to the algorithm toward achieving powerefficient and non-harmful solutions. The chip is designed using the standard NWELL XH035 CMOS technology by XFAB. The intrinsic optimization is performed onchip, with changes in temperature ranging from .20 °C to 40 °C, and also by reducing the supply voltage by 25 % from the nominal value of 3.3V. The practical evaluation demonstrates the possibility of tackling all the sources of variation. i.e, process, voltage and temperature (PVT). By utilizing the PMM, the CFIA power dissipation is reduced by 34 %, achieving an average power of 53.9mW within the tested temperature range. The measured total harmonic distortion (THD) is .71.93 dB at T = 25 °C, -69.95 dB at T = -20 °C, and .74.01 dB at T = 40 °C, relative to signal stimuli with THD of -74.49 dB. Initial chip testing, using a default configuration derived from simulations, exhibited unsatisfactory performance. However, the optimization process successfully restored the CFIA's functionality, enhancing the design yield. This demonstrates the effectiveness of implementing a reliable and robust design concept for supporting Industry 4.0 under dynamic operating conditions.