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Optimization improves analog IC performance

Posted: 16 Jan 2007 ?? ?Print Version ?Bookmark and Share

Keywords:analog IC design? circuit optimizer? amplifier performance? IC performance optimization? James Spoto?

For decades, microwave designers have applied optimization to their designs to improve and center the performance of their circuits. Thanks to some new technologies developed over the past decade, analog IC designers can now easily set up and efficiently run optimization on their designs.

As opposed to older circuit optimizers that require tedious setup and run mostly in a batch mode, the newer solutions are designed for easy setup and interactive use during circuit design creation. While many solutions offer just one algorithm, some tools now provide many optimization algorithms and methods that can be applied depending on the problem definition and breadth of the design space to be explored. Many of these algorithms begin at a user-defined starting point and search the design space from that point to find the local optimum. Some can search the entire design space to find the global optimum.

Let's examine an application where the requirement of the analog IC is to amplify intermediate broadband signals up to 2GHz. As an op amp, the IC will always be used in a closed-loop configuration, a real challenge at these frequencies. Consequently, the phase shift through the amplifier must be kept to a minimum. Because of the high-frequency requirements, the amplifier will be implemented using a foundry's 60GHz SiGe technology.

For this amplifier, the goal is to meet or exceed the bandwidth and gain requirements, while minimizing power and maintaining stability. These are very conflicting requirements. Designers can spend many tedious hours trying to meet specifications, much less finding the best solution. Designers will often settle for an acceptable solution without pushing the design for all it can deliver. This is where optimization can really pay off.

Inequality constraints
In addition to bandwidth, gain, power and stability, other requirements must be considered. In this example, power supply rejection ratios and preferential DC offset are included in the optimization trade-offs. Most of these goals are inequality constraints that should be less or greater than a target value or line segment.

After the measurements are defined, setting up the optimization objectives is easy. The user simply selects the measurement to include in the optimization session and chooses whether to make it greater than, less than or equal to a value (or range of values over a frequency or time range, if applicable).

Once the goals, weights, design parameters and constraints are defined, the optimizer is ready to run. Since this amplifier has discrete design parameters, either the Pointer or Random algorithms can be applied. In this case, the Pointer algorithm is more appropriate because it is typically more efficient for nonlinear problems with costly simulation runtimes. By running the optimizer for 50 iterations and analyzing the results, you will see that the cost function is substantially improved. After making the final adjustments, the optimizer is run for a total of 100 iterations, taking about 30mins, to further optimize the parameters.

At this point, the objective weights can be refined to improve power at the expense of some of the other requirements. Also, some design parameters were disabled after they were seen to be less important as the session proceeded. This process continues with incremental 100-iteration runs/refinements to understand and generate options between the overall trade-offs. This interaction is vital to maximize the optimization. In total, the session takes several hours, but the designer can be confident that the trade-offs have been comprehensively covered, and the performance of the amplifier has been pushed to its limits.

- James Spoto
President and CEO, AWR makes radical enhancements to simulator software" target=_blank>Applied Wave Research Inc.

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