For decades, microwave designers have used optimization methods in their designs to improve and concentrate circuit performance. Thanks to some of the new technologies developed over the past decade, analog IC designers can now easily build and efficiently optimize their designs.
Unlike previous circuit optimizers, which must be monotonously set and run primarily in batch mode, these newer solutions are specifically designed to make setup and interactive use of the circuit design creation phase easier and more convenient. . While many solutions include only one algorithm, some tools now offer a number of optimization algorithms and methods that can be applied based on the actual situation of the problem and the width of the design space. Many of these algorithms start with a user-defined initial point and search in the design space to find the local best. There are also ways to search the entire design space for the best of the world.
Let us analyze an application example in which the analog IC is required to amplify the intermediate wideband signal to 2 GHz. As an operational amplifier, this IC is always used in a closed-loop structure, and at these frequencies, it is a real challenge. Therefore, the phase shift after the signal passes through the amplifier must be kept to a minimum. Due to this high frequency requirement, the amplifier will be implemented using 60 GHz silicon germanium technology.
This amplifier is designed to meet or exceed bandwidth and gain requirements while minimizing power and maintaining stability. Indeed, these requirements are very contradictory. By simply meeting these specifications, designers can spend hours or even days, not to mention finding the best solution. Often, to save time, designers have to reluctantly accept a barely acceptable solution that doesn't make the most of their design potential. And this is where optimization can really take advantage.
In addition to bandwidth, other requirements such as gain, power, and stability must be considered. In this example, the power supply rejection ratio and the preferred DC offset are also trade-offs in the optimization. Most of these targets are unequal constraints and must be less than or greater than a certain target value or line segment.
Once the measurement parameters have been defined, it is easy to set the optimization goals. The user simply selects the measurement parameters needed in the optimization process (opTImizaTIon session) and chooses whether it is less than, greater than or equal to a certain value (or, if applicable, a certain frequency or time range) Range of values).
Once these goals, weights, design parameters, and constraints are defined, the optimizer is ready to run. Since the amplifier has discrete design parameters, a pointer algorithm or a random algorithm can be applied. In this case, the pointer algorithm is more suitable because it is generally more effective for non-linear problems that are computationally expensive to run. After the optimizer runs 50 iterations, the analysis results will find that the cost function (cost funcTIon) is fully improved. After the final adjustment, the optimizer took a total of 100 iterations in about 30 minutes to further optimize those parameters.
At this point, performance can be improved by refining the target weights at the expense of other requirements. Moreover, as the optimization process continues, some design parameters are less important and then no longer useful. Add another 100 iterations of the iteration/iteration to continue the optimization process, and finally get the overall trade-off options. This interaction is critical to maximizing optimization. The optimization process took several hours, but the designer was convinced that a comprehensive trade-off could be achieved to maximize the performance of the amplifier.
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