A CMOS-Compatible Silicon Nanowire Array Natural Light Photodetector with On-Chip Temperature Compensation Using a PSO-BP Neural Network.

Silicon nanowire (SiNW) photodetectors exhibit high sensitivity for natural light detection but suffer from significant performance degradation due to thermal interference. To overcome this limitation, this paper presents a high-performance, CMOS-compatible SiNW array natural light photodetector with monolithic integration of an on-chip temperature sensor and an embedded intelligent compensation system. The device, fabricated via microfabrication techniques, features a dual-array architecture that enables simultaneous acquisition of optical and thermal signals, thereby simplifying peripheral circuitry. To achieve high-precision decoupling of the optical and thermal signals, we propose a hybrid temperature compensation algorithm that combines Particle Swarm Optimization (PSO) with a Back Propagation (BP) neural network. The PSO algorithm optimizes the initial weights and thresholds of the BP network, effectively preventing the network from getting trapped in local minima and accelerating the training process. Experimental results demonstrate that the proposed PSO-BP model achieves superior compensation accuracy and a significantly faster convergence rate compared to the traditional BP network. Furthermore, the optimized model was successfully implemented on an STM32 microcontroller. This embedded implementation validates the feasibility of real-time, high-accuracy temperature compensation, significantly enhancing the stability and reliability of the photodetector across a wide temperature range. This work provides a viable strategy for developing highly stable and integrated optical sensing systems.