A Structurally Robust Framework for Intelligent Graphene Thermometry via Few‐Shot Transfer Learning and Algorithm‐Hardware Co‐Design

While ultrathin graphene sensors hold promise for flexible electronics, their applicability faces a challenging arising from the unavoidable nanoscale stochasticity and structural variability of compliant substrates. Features like intrinsic grain boundaries and microscopic wrinkles inevitably create pronounced device‐to‐device heterogeneity, rendering traditional batch calibration unreliable for high‐precision applications. Rather than eliminating these inherent physical imperfections, we introduce a variability‐resilient sensing framework built on an algorithm‐hardware co‐design. By employing a few‐shot transfer learning architecture with a frozen‐backbone neural network, our system effectively learns the universal physics of graphene carrier scattering from a source array and can rapidly adapt to the unique electrical footprint of new, uncalibrated devices using less than 1% of conventional calibration data ( R 2 > 0.99). This framework not only enables high‐precision graphene‐based flexible thermometry for the first time (±0.2°C), but also supports real‐time temperature inference across wide‐range robotic perception in harsh environments up to 200°C. Furthermore, we demonstrate that graphene's high intrinsic sensitivity remains functionally robust despite structural discrepancies, as a 1D convolutional neural network decodes complex physiological waveforms directly from raw signals with 94.95% accuracy. Collectively, this sensing‐as‐inference paradigm transforms graphene into a viable, calibration‐efficient material for intelligent wearable thermometry.