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Post-silicon tuning has recently emerged as an important technique to combat large-scale uncertainties (e.g., process variation, device modeling errors, etc) for today's nanoscale circuits. This talk presents a novel Bayesian Model Fusion (BMF) technique for efficient post-silicon performance modeling and tuning of analog and mixed-signal (AMS) circuits. The key idea is to borrow the simulation or...
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