Abstract
Abstract. This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.
Highlights
Introduction and literature surveyMachine learning (ML) and artificial intelligence (AI) are considered the electricity for the twentieth century
It is worth mentioning that the experience replay buffer (ERB) contained the previously visited global best positions
The preponderance of the proposed experience replay particle swarm optimization (ERPSO) algorithm is validated over four classical particle swarm optimization (PSO) algorithms on eight benchmarking functions
Summary
Introduction and literature surveyMachine learning (ML) and artificial intelligence (AI) are considered the electricity for the twentieth century. I(I)oTs devices and industry 4.0 introduce more demands on sensors and sensory electronics, primarily regarding measurement data accuracy, versatility, flexibility, long-term reliability, and robustness (Koenig, 2018; Trends, 2014). The performance of sensors and sensory electronics is strongly challenged by static and dynamic variations (Lee et al, 2018b; Lin et al, 2019). Fabrication process imperfections such as lithographic uncertainties, mechanical stress due to packaging, and process. The environmental variations, thermal drift due to external heating and self-heating, power supply fluctuations, and aging effects are considered a dynamic variation (Alraho et al, 2020).
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