New-Tech Europe | Q3 2026 | Digital Edition

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reasonable idea of what the final structure should look like. Inverse Design takes a different perspective. Instead of asking how to improve an existing design, engineers specify the desired operating frequency, bandwidth, efficiency, output power, manufacturing constraints, size limitations and any additional performance requirements. The algorithm then searches for geometries capable of satisfying those constraints, without being limited by conventional design intuition. In other words, the computer is not refining an existing concept-it is searching for entirely new ones. Why Is This Becoming Practical Now? The idea of Inverse Design is not new. Researchers have explored it for years, but its practical application was constrained by one unavoidable obstacle: computational cost. Every candidate RF geometry requires a full electromagnetic simulation based on Maxwell’s equations. For complex RFICs or mmWave structures, a single simulation may take minutes or even hours. Searching through hundreds of thousands of possible geometries quickly becomes impractical. Recent advances in machine learning have changed that equation. Rather than performing a full electromagnetic simulation for every candidate, researchers first train surrogate models using large datasets of highly accurate simulations. Once trained, these models can estimate the behaviour of new geometries in fractions of a second.

Rather than performing a full electromagnetic simulation for every candidate, researchers first train surrogate models using large datasets of highly accurate simulations. Once trained, these models can estimate the behaviour of new geometries in fractions of a second.

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Only the most promising candidates are then passed to full-wave electromagnetic simulation for final verification. The result is not a replacement for traditional simulation tools, but a dramatic reduction in the search space-making Inverse Design practical for increasingly complex RF problems. When the Best Design Doesn’t Look Like Engineering Perhaps the most fascinating aspect of the Princeton research is not the computational efficiency, but the type of solutions it discovers. Conventional optimisation algorithms usually improve existing layouts. They adjust dimensions, tune parameters and refine structures that already resemble familiar RF circuits. Inverse Design removes those assumptions. Any geometry capable of meeting the required specifications becomes a valid candidate-even if it bears little resemblance to anything an engineer would naturally draw. One of the most striking demonstrations presented by Professor Sengupta involves a silicon power amplifier operating between 30 and 100 GHz. Rather than optimising an existing amplifier, the researchers simply defined the required electrical performance and allowed the algorithm to search for a suitable layout. The resulting structure looked remarkably unconventional. Instead of the clean, symmetrical geometries common in RF design, the final layout appeared as a dense, irregular pattern that IEEE Spectrum compared to a QR code. More importantly, the design worked. After full electromagnetic verification, fabrication and laboratory testing, the amplifier demonstrated an impressive combination of bandwidth, output power and efficiency, providing one of the first practical demonstrations that

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Instead of asking how to improve an existing design, engineers specify the desired operating frequency, bandwidth, efficiency, output power, manufacturing constraints, size limitations and any additional performance requirements. The algorithm then searches for geometries capable of satisfying those constraints, without being limited by conventional design intuition.

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