Multi-field synergy in lateral photovoltaic position-sensitive detectors: physics, modulation, and applications

Abstract Position-sensitive detectors (PSDs) based on the lateral photovoltaic effect (LPE) are fundamental to precision optical sensing. Conventional Si-based PSDs, however, are fundamentally constrained by their rigid physical form, limited spectral range, and reliance on single-field modulation, failing to meet the growing demands for ultra-sensitivity, flexibility, and multifunctionality. The emergence of pyroelectric, piezoelectric, and photothermoelectric effects in non-centrosymmetric materials and hybrid structures has unlocked new degrees of freedom, enabling the transition from classical diffusion models to a multi-effect modulation framework. This review systematically charts the evolutionary trajectory of PSDs based on the lateral photovoltaic effect (LPE-PSDs)—from single-field modulation, through dual-field coupling, to multi-field synergy. By classifying synergistic mechanisms into Field-Assisted Field (FAF), Cascade Coupling, and Resonant Synergy, we demonstrate that spatiotemporally aligned polarization fields yield unprecedented performance breakthroughs. For instance, pyroelectric enhancement in a Si nanowire/CdS/ZnO/ITO heterojunction boosts position sensitivity to 15390 mV·mm-1 (a 1772% enhancement); graphene-based architectures achieve pW-level detection limits; and optimized nanostructures enable centimeter-scale working distances. This review comprehensively summarizes the underpinning physical mechanisms, emerging material platforms (including 2D materials, perovskites, and topological insulators), the classification of multi-field synergy, and their expanding applications in ultra-precise optical tracking and human-machine interaction, multifunctional imaging and optical information security, flexible, wearable and transient sensors, self-powered and energy-harvesting systems, and extreme-environment detection. Finally, key challenges are outlined, including the absence of a unified theoretical framework and issues of stability and scalability, while offering perspectives on AI-driven co-design, on-chip synergistic integration, and neuromorphic sensing as promising future directions.