Deep learning-enabled ultra-broadband terahertz high-dimensional photodetector

Abstract

Capturing multi-dimensional optical information is indispensable in modern optics. However, existing photodetectors can at best detect light fields whose wavelengths or polarizations are predefined at several specific values. Integrating broadband high-dimensional continuous photodetection including intensity, polarization, and wavelength within a single device still poses formidable challenges. Here we present a metasurface-mediated high-dimensional detector that projects polarimetric and spectral responses into the Orbital Angular Momentum (OAM) domain via dispersion-driven OAM multiplication. By decoupling the frequency-controlled transmission phase response and polarization-controlled geometric phase response, spectrum and polarization information are encoded into unique polaritonic vortex patterns, which can be accurately deciphered via machine learning technique. Eventually our neural-network assisted metadevice achieves full characterization of intensity-polarization-frequency 3D continuous parametric space, so that light with arbitrarily mixed polarization states across 0.3-1.1 THz can be accurately detected with total error <5.1%. Our technology also showcases application potential as OAM-mediated information encryption, offering impetus for next-generation high-dimensional photodetectors and information security.

Fig. 1. Design principles of our intelligent high-dimensional photodetector.

a Underlying mechanism of our multi-dimensional photodetector. The specially designed meta-slit array generates distinct polaritonic vortices over a broad spectral range under different polarizations and wavelengths. Since the topological charges and mode purities of generated OAM beams, respectively, rely on the incident wavelength and polarization states, the interference of these OAM beams results in various plasmonic distribution under different incidences, enabling the differentiation of multi-dimensional information. b Via training on the trends of metasurface-generated plasmonic patterns with a deep residual neural network, the effective identification of intensity, polarization and wavelength information can be implemented in the frequency range of 0.3–1.1 THz within 25 GHz accuracy.

Fig. 2. Metasurface design with high-dimensional identification capability.

a Schematic of the metasurface-mediated interferometric photodetector. b Theoretical results of generated OAM topological charges under LCP and RCP incidences at different frequencies, with both lines having slopes of -m/f₀. c Simulated plasmonic field distributions (real part of E_z component) and d corresponding mode purities under different illuminations when structural parameters m = 1 and f₀ = 0.3 THz. The OAM mode purity exhibits high purity (≈1.0). e The normalized amplitude of two generated OAM beams |M_l-1|, |M_l+1| and their phase difference angle (M_l-1–M_l+1) versus different QWP (quarter-waveplate) angle at three different frequencies, where l-1th and l+1th order vortices theoretically are the two predominant modes in OAM spectrum. Here, the QWP angle changes from −90° to 90° in increments of 22.5°, causing the polarization state to change from yLP to right-handed elliptical polarization, then back to yLP and finally transition to left-handed elliptical polarization. The OAM coefficient variation trends under diverse frequencies are basically the same.

Fig. 3. High-dimensional detection results for distinct polarizations at three bands.

a Calculated Stokes parameters from simulated and experimental results, under various polarizations and frequencies. b Simulated plasmonic electric field intensity profiles under different incident frequencies and polarizations, whose corresponding polarizations are the same with those depicted in (a). c Experimentally measured SPP intensity profiles. The measurement scope is 1.5 × 1.5 mm. d Extracted OAM purities under incidences of different polarizations and frequencies, which are one key basis for frequency identification. The ordinates represent the seven tested polarization states.

Fig. 4. Realization of an intelligent polarization spectrum detector based on deep-learning techniques.

a Demonstration of the measured phase and intensity distributions under specific incidence. b Architecture of the employed neural network for high-dimensional detection, with metasurface-generated intensity patterns as network inputs. The output layer is a vector indicating Stokes parameters and wavelength information. c The neural network predictions of wavelength (left panel) and polarimetric information (right panel) on the test set, which comes from simulations. The numbers on the diagonal of the left matrix reveal the count of precisely identified samples, while those off the diagonal reflect the number of incorrect predictions. The distinct polarization states are represented on the Poincaré sphere (the second inset), and the ResNet-predicted polarization results are projected onto the S₁–S₂ and S₂–S₃ planes for clarity. The different colors represent selected incident frequencies among 0.3–1.1 THz. d The prediction results based on a test set of real laboratory data. Our model can accurately predict wavelength (left panel) and polarimetric information (right three panels) in experimental testing.

Fig. 5. Experimental demonstration of information encrytion using designed metasurface.

a The one-to-one mapping between incident states (Stokes parameters and wavelength) and OAM spectrum enabled by the designed metasurface. b The OAM spectra under several distinct incident states, where 0th order (blue) and 2nd order (red) OAM modes are selected to encode information in this example. c By selecting incident states with the two (0th and −2nd orders) appropriate OAM mode intensities, the longitude and latitude coordinate information of Beijing (116.2°E, 39.6°N) are respectively encoded into two sets of incident states (Input states 1, Input states 2). d When obtaining correct keys (metasurface, 0th and −2nd orders), the receiver can encrypt the longitude (116.2° E) and latitude (39.6°N) information of Beijing by analyzing the metasurface-generated OAM mode intensities under the two sets of incident states. The letters “E” and “N” are revealed by the spatial arrangement of −2nd order OAM mode intensities in a square format, and the number “116.2″ and “39.6″ are decoded through binary encoding of 2nd order OAM mode intensities along each line, where a value greater than 0.4 is taken as “1” and a value less than 0.4 is considered as “0”. Here, the highest (leftmost) order is defined as a decimal point.