Self Calibrating Mixed Signal/RF Systems: Offline and Online Tuning Algorithms and Infrastructure
Author(s)
Komarraju, Suhasini
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Abstract
The objective of the proposed research is to develop algorithms that introduce
self-adaptability in modern embedded mixed signal/RF systems. With the advances in Silicon technology, the integration capabilities are increased and the transistor sizes are down to nanometer regime. On the other hand, these advances bring forth several issues
such as increased parasitics and reduced visibility to internal circuit nodes. State of the art parallel testing and tuning techniques are expensive and limited in terms of number of components that can be tested/tuned simultaneously and in terms of the order of distortions that can be estimated. The main aim of this research is to design algorithms
to develop built-in testable and self-calibrating mixed signal systems that can self-test and self-adapt on-chip with minimal use of external test instrumentation and response
measurement systems. Offline frequency-efficient test schemes for MIMO systems that can detect upto fifth order non-linearities are proposed such that they can test several RF chains simultaneously in minimal time. Offline test techniques that apply an alternative test stimulus to detect outliers/bad devices that violate one or many diverse range of specifications are proposed. The proposed machine learning driven test approaches are validated on a diverse range of circuits and can be implemented on-chip for built-in testing of RF/mixed-signal circuits and systems. Besides, the post manufacture tuning and real-time adaptation algorithms proposed are expedited using the information obtained by using the offline test schemes for each test-case.
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Date
2024-09-04
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Dissertation