A novel radiation-hardened-by-design (RHBD) technique that utilizes charge sharing to mitigate single-event voltage transients is employed to harden bias circuits. Sensitive node active charge cancellation (SNACC) compensates for injected charge at critical nodes in analog and mixed-signal circuits by combining layout techniques to enhance charge sharing with additional current mirror circuitry. The SNACC technique is verified with a bootstrap current source using simulations in a 90-nm CMOS process. Reductions of approximately 66% in transient amplitude and 62% in transient duration are observed for 60-degree single-event strikes with an LET of 40 MeV*cm 2 /mg.The SNACC technique can be extended to protect multiple sensitive nodes (M-SNACC). M-SNACC is used to harden the bias circuit of a complementary folded cascode operational amplifier, providing a significant reduction in single-event vulnerability for a 8-bit digital-to-analog converter.
Background
Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias.
Methods
Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages.
Results
Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research.
Conclusion
The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.
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