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  <title><![CDATA[Ph.D. Dissertation Defense - Huan Yu]]></title>
  <body><![CDATA[<p><strong>Title</strong><em>:&nbsp; </em><em>Behavioral Modeling of Drivers and Oscillators Using Machine Learning</em></p>

<p><strong>Committee:</strong></p>

<p>Dr. Madhavan Swaminathan, ECE, Chair , Advisor</p>

<p>Dr. Sung-Kyu Lim, ECE</p>

<p>Dr. Arijit Raychowdhury, ECE</p>

<p>Dr. Saibal Mukhopadhyay, ECE</p>

<p>Dr. Suresh Sitaraman, ME</p>

<p><strong>Abstract: </strong></p>

<p>The objective of this dissertation is to develop time-domain behavioral models for I/O drivers and oscillators for fast simulation and IP protection. For oscillators, augmented neural networks (AugNNs) are proposed to capture the oscillatory behavior of fixed-frequency oscillators and VCOs. When output buffer is included as a part of the oscillator circuit, AugNN-based models are developed taking into account the I/O behavior of the oscillator. For tunable drivers with pre-emphasis, state-aware weighting functions are proposed, and the dynamic memory characteristics of the driver&rsquo;s output stage are captured using recurrent neural networks (RNNs). The behavior of the tunable control parameters is captured. Furthermore, a transition-variational model is discussed for the modeling of I/O drivers under overclocking conditions. The proposed models are compatible with Verilog-A.</p>
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