Physical unclonable functions (PUFs) are considered the most promising approach to address the
global issue of counterfeiting. Current PUF devices are often based on a single stochastic process,
which can be broken, especially since their practical encoding capacities can be significantly lower
than the theoretical value. Here we present stochastic PUF devices with features across multiple
length scales, which incorporate semiconducting polymer nanoparticles (SPNs) as fluorescent
taggants. The SPNs exhibit high brightness, photostability and size tunability when compared to
the current state-of-the-art taggants. As a result, they are easily detectable and highly resilient to
UV radiation. By embedding SPNs in photoresists, we generate PUFs consisting of nanoscale
(distribution of SPNs within microspots), microscale (fractal edges on microspots), and macroscale
(random microspot array) designs. With the assistance of a deep-learning model, the resulting
PUFs show both near-ideal performance and accessibility for general end users, offering a strategy
for next-generation security devices.