An Optimized Radiation Tolerant Baseline Correction Filter for HEP Using AI Methodologies

Abstract

This paper presents an improved baseline correction filter to be used in the readout front-end ASICs of gaseous detector systems in high energy physics experiments. The digital filter is based on an FIR section and used in the data pipeline running in real-time thought the input signals, allowing adequate data compression by the subsequent blocks in the processing chain. The proposed design was optimized to cope with the power and area trade-offs while hardening the block using redundancy and a novel automated design system based on artificial intelligence. This optimizer is also discussed including its logic and improvements using genetic algorithms with dynamic mutation and cross-over probabilities connected to run-time EDA tools. The designed filter was fabricated in 130 nm process, packaged and tested in the Peletron particle accelerator at the University of Sao Paulo. The device was irradiated and received a total dose of 70 krad during about one week of exposition to alpha particles without any measured failure, validating the design and solving the vulnerability with a small increase in the channel area of just 0.07%.