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18 The PCB Design Magazine • July 2016 ing and the Y axis plots the eye height derived from the optimized coding. As such, points on the diagonal black line represent channels that performed the same using either SerDes con- figuration. Channels above the line have bet- ter eye heights using optimization, while points below the line represent channels with bet- ter eye heights using the baseline coding. The plots show that the optimized settings exceed the baseline in 85% of the channels using the original algorithm (left) and 95% of channels using the improved algorithm (right). As de- sired, worst-case channels in the lower-left cor- ners of both plots increasingly move away from the black line. The Y axes are aligned to show ~100mV gain in some channels. In both cases in Figure 4, the default (non- optimized) coding is better in some mid-range channels. The channels highlighted in blue (left) represent channels that did not trade tap one optimally between the Tx and Rx in the original algorithm, as this trade-off is not sim- ple to derive. Although the optimization tech- niques described in the next section will detail the removal of intersymbol interference (ISI) by forcing the pulse response to zero in pre- and post-tap UIs, in some cases that method does not provide the optimal result. In Figure 5, the zeroforcing solution (blue) produces a smaller eye than the one derived by the refined auto- mated algorithm (red). In some cases the com- plete elimination of ISI can remove too much amplitude from the main cursor (compare red and blue at ~8.25 nS). As such, optimization al- gorithms must intelligently trade-off amplitude, Tx/Rx taps, equalizable ISI, residual ISI in long tails, and other factors to arrive at the optimal solution. This is not a simple task—particularly since the "optimal" solution may vary depend- ing on performance criterion. 3. System-Level Channel Optimization Techniques 3.1 Overview One of the tasks to be performed in the anal- yses shown in the preceding section was to op- timize the combination of transmit and receive equalization separately for each channel in the system. Given the number of channels to be op- timized, an automated procedure was required. The optimization procedure we used is based on an analysis of the impulse response at the output of the receiver's IBIS-AMI model. The procedure is based on a pulse response de- rived from the impulse response and assumes Figure 5: Pulse responses derived using conceptual and automated techniques. NEW SI TECHNIQUES FOR LARGE SYSTEM PERFORMANCE TUNING