Classification principle enabled optimal frames for high-performance and intelligent polarimeters

The rapid development in nanophotonics has sparked a new wave of research into high-performance polarimeters. The novel materials, including metamaterials and low-dimensional materials, along with artificial intelligence (AI) algorithms, enable the realization of highly precise and ultra-compact full Stokes polarimeters. However, the detailed working mechanism remains unclear to this day. Here, we construct a general and visual model based on classification principles to optimize polarization detection conditions. For a polarization-sensitive system with a known mapping relationship S^=f(I^) between the Stokes vector S^ and the measured signal vector I^, a single signal I_i will determine one possible range for the Stokes vector. Conversely, multiple signals I^ will narrow this range and enhance the precision of polarization detection. The possible range is quantitatively described by the signal repetitive rate (RR). Our in-depth analysis reveals that strong optical chirality and high anisotropy are advantageous for reducing the RR and improving detection precision. However, only one signal with suitable optical chirality is sufficient for realizing a full-Stokes polarimeter, whereas all signals should possess high sensitivity to optical anisotropy. Moreover, these optical anisotropies should have suitable rotation angles relative to each other to reduce the RR. These factors explain why incorporating more diverse signals in intelligent polarimeters can significantly decrease the RR, even in systems with weak optical chirality and anisotropy. Additionally, reducing the RR can be achieved by enhancing the intensity of the finally detected signals, decreasing the signal intensity interval, and eliminating signal errors. We have systematically investigated the influence of optical chirality and anisotropy, the combination modes of polarization-sensitive systems, and the performance of photodetectors. Our work provides new insights into the working mechanism of polarimeters based on novel materials and AI algorithms and will greatly advance the development of high-performance polarimeters and polarization imaging devices.