Digital twin methodologies are useful for designing and building workflows because they simulate every aspect of the product in detail, as if it was real. As a result, it eliminates the gap between theory and reality. Digital twins are essentially digital representations of physical systems. Despite its apparent simplicity, it becomes deceptively complex once unpacked. Many companies standardize and streamline their design, test, and build processes, but tools and methods remain complex. There are usually three to ten different software tools used by an organization for each process. Product introduction times, product performance issues, and poor quality can contribute, at least in part, to this complexity, both during production and possibly during customer support.

During the development of new products, you keep hearing the terms faster, cheaper, more accurate, lower power, smaller, and so on. Over the years, designers used step function innovations such as switching from through-hole components to surface-mount, manual analysis on spreadsheets to sophisticated simulations, and manual testing to accomplish goals. Despite recent improvements, most companies still take several months to correlate test data with design, which adds significant delays to product launches and strains engineering resources.

Design verification (simulation) and hardware validation contribute to nearly two-thirds of all product development time. This is an area where the industry hopes to improve. A closer look at these two activities reveals many similarities. Both activities attempt to characterize a design according to a set of requirements, with one activity taking place in the virtual digital twin domain and the other in the physical test domain. The realization of this concept allows connecting these tasks through digital threads, enabling the benefits previously mentioned.

With the "digital twin" concept, you can digitize most, if not all, physical activities in system engineering. In RF system scenarios, staging physical tests is challenging and real-world effects are difficult to replicate. This is when digital twins are extremely valuable. An accurate digital twin model should reflect the behavior of a physical system over the course of its lifecycle to reduce costs and risks. Models and simulation capabilities provide system architects with a comprehensive understanding of how RF processing blocks interact, even if they aren't experts in RF characteristics. With PathWave System Design, engineers can quickly create virtual prototypes of systems, optimize subsystems and algorithms, and test and evaluate complex mission scenarios digitally (Figure 1).

When replacing real-world activities with digital twins, accuracy is essential from the start. Performance modeling is one of the main pillars of a digital twin, which transforms a virtual prototype into a physical system instance by integrating measurements from a physical twin. The design of RF functional structures is usually straightforward, especially since PathWave System Design offers optional reference libraries for these applications. The evaluation of performance and test coverage of RF systems turns into a lengthy and sometimes risky game without verified complex waveforms. Furthermore, vector modulation analysis needs complex instrumentation for accurate results and powerful visualization to determine problems, that’s where Keysight’s powerful measurement science makes its mark. PathWave System Design utilizes the same fundementals that Keysight uses in the design of its instrumentation. You can incorporate a physical twin measurement into modeling for further analysis and enhancement.

For example, the Keysight PathWave Vector Signal Analyzer (VSA) on the Keysight signal analyzers handles modulation and provides demodulation and error vector measurements of your signals, as well as capabilities in carrier aggregation, and constellation signal quality analysis. These and other advanced measurement capabilities connect directly with PathWave System Design, eliminating guesswork and estimation, and increasing model fidelity. As specifications evolve, Keysight measurement science continues to advance. 

With these integrations, you can evaluate scenarios difficult to stage in the physical world, quickly in the virtual world, using the digital twin as a proving ground knowing its behavior mirrors the physical twin. If you are interested to learn more, register for a demo here.