However, upscaling analog computing platforms is often difficult, as their underlying components can behave differently in larger systems. The synthetic frequency domain approach developed by Shao and his colleagues allows them to encode large amounts of data (e.g., a 16x16 matrix) on a single analog computing device. This prevents errors that commonly emerge from device-to-device variance in analog computing platforms that integrate more devices. Notably, the first PPN-based analog computing system created using their method was found to perform remarkably well in a task that entailed classifying data into four possible categories. More information: Jun Ji et al, Synthetic-domain computing and neural networks using lithium niobate integrated nonlinear phononics, Nature Electronics (2025).