What This Document Is
This document represents Lecture Thirteen from the Introduction to Microelectromechanical Systems (MEMS) course (ELENG 247A) at the University of California, Berkeley. It’s a focused exploration of data converters, a critical component in interfacing the analog world with digital systems – essential for any MEMS device that senses and processes physical phenomena. This lecture delves into the intricacies of testing and designing these converters, building upon foundational knowledge of analog and digital circuit principles.
Why This Document Matters
This lecture is invaluable for students seeking a deeper understanding of how to evaluate and create high-performance data conversion systems. It’s particularly relevant for those specializing in analog circuit design, signal processing, or the development of sensor interfaces within MEMS. Engineers and researchers working on projects involving analog-to-digital and digital-to-analog conversion will find the concepts discussed here directly applicable to their work. Understanding these principles is crucial for optimizing system accuracy and efficiency.
Topics Covered
* Advanced Data Converter Testing Methodologies
* Dynamic Testing Techniques, including Spectral Analysis
* Relationships between key performance metrics: DNL, INL, SNR, and SFDR
* Effective Number of Bits (ENOB) and its significance
* Architectures of Digital-to-Analog Converters (DACs) – including resistor string, charge scaling, R-2R ladder, and current-based designs
* Impact of non-ideal characteristics (DNL and INL) on converter performance
* SNR degradation analysis related to DNL
What This Document Provides
* A detailed examination of static and dynamic testing procedures for data converters.
* Insights into interpreting test results and relating them to overall system performance.
* A comparative overview of various DAC architectures, highlighting their strengths and weaknesses.
* A framework for understanding the trade-offs involved in data converter design.
* Conceptual explanations and relationships between key performance parameters, allowing for informed design choices.
