Differential perovskite hemispherical photodetector for intelligent imaging and location tracking

Abstract

Advanced photodetectors with intelligent functions are expected to take an important role in future technology. However, completing complex detection tasks within a limited number of pixels is still challenging. Here, we report a differential perovskite hemispherical photodetector serving as a smart locator for intelligent imaging and location tracking. The high external quantum efficiency (~1000%) and low noise (10^-13 A Hz^-0.5) of perovskite hemispherical photodetector enable stable and large variations in signal response. Analysing the differential light response of only 8 pixels with the computer algorithm can realize the capability of colorful imaging and a computational spectral resolution of 4.7 nm in a low-cost and lensless device geometry. Through machine learning to mimic the differential current signal under different applied biases, one more dimensional detection information can be recorded, for dynamically tracking the running trajectory of an object in a three-dimensional space or two-dimensional plane with a color classification function.

Fig. 1. The formation of nanoparticles and their influence on device gain.

a The crystal structure of the NGAI where NGAI is 1-(naphthalen-2-yl)guanidinium iodide. b The TEM image of the nanosheet structure aggregated by NGAI without adding PbI₂, scale bar: 200 nm. c The TEM image of the nanoparticles aggregated by NGAI-PbI₂ complex, scale bar: 20 nm. d Schematic diagram of the self-assembly of [NGA]⁺ in the polar solvent (w/o and w PbI₂) where [NGA]⁺ is the 1-(naphthalen-2-yl)guanidinium cation. The guanidinium of [NGA]⁺ was exposed to the outside. e Schematic diagram of the process of crystallization of perovskite (FAPbI₃ w NGAI) during spray-coating and annealing. f The crystalline processes of the FAPbI₃ films were monitored by the absorbance at 700 nm with different amounts of NGAI under 80 °C. g The EQE of FAPbI₃ (w 0%_mol ~ 40%_mol NGAI) photodetectors at −1.0 V bias condition. h The XRD spectra of FAPbI₃ (w 0%_mol ~ 30%_mol NGAI) films fabricated by spray-coating.

Fig. 2. The computational spectrometer by differential photodetectors.

a The EQE mapping of the differential photodetector at different reverse biases and wavelengths (The irradiance of light is shown in Supplementary Fig. 8b Condition 1). b The EQE curves of the photodetector at different reverse biases (–0.2 V, –0.6 V, –1.0 V) and wavelength. c The current density of the photodetector at different reverse biases and wavelengths under the constant irradiance of 50 μW cm⁻². d The schemed principle of the wavelength classification by differential photodetectors at different reverse biases. e The reconstructed spectra of four monochromatic lights by differential photodetectors match well with the reference spectra. f The reconstructed spectrum of the quasi-monochromatic light from a 520 nm laser. g The reconstructed spectrum of the polychromatic light.

Fig. 3. The colorful image of the differential photodetector based on machine learning.

a Schematic diagram of the single-pixel imaging the process of color classification. Patterns are projected onto the object by the projector. The reflected light is detected by the photodetector. Each pattern responded to a current density of photodetector. m is the numerical order of patterns. The m-current density curve can be translated into a gray-scale map by the algorithm. The gray level of pixels in gray-scale maps obtained under different reverse biases are collected as feature vectors. b The optimized images at different V_n from single-pixel imaging. c The color image realized by a lensless differential photodetector.

Fig. 4. The differential hemispherical photodetector for intelligent detection.

a The normalized effective incident flux on the hemispherical and planar surfaces with varying light source positions. b The changing curves of normalized effective incident flux at different points on the hemispherical and planar surfaces (d = 0.2 r ~ 1.0 r) of light at different horizontal positions. The height of the light source is constant. c The device structure of the hemispherical photodetector. d The process for building the model by NNF and trace reconstruction. e The signal matrix of the differential pixels distributing different positions (x₁, x₂, …, x₈) of the photodetector. f The trace reconstruction of the light source w/o color classification and with color classification. g The spatial trace reconstruction in 3D space enabled by the differential hemispherical photodetector.