All-in-plane image sensors free from readout integrated circuits

Abstract

High resolution image sensors require electrical access to each individual pixel for signal readout. Such access is especially challenging for ultra-miniaturized pixels, for heterogeneously integrated sensing and readout layers in long-wavelength detectors, and for novel light-sensing materials with unestablished integration to silicon chips. Here, we introduce and experimentally validate a novel imaging approach that does not require electrical connections to individual pixels. The sensor matrix involves photoresistive pixels connected neighbor-to-neighbor and packed into a rectangular lattice. The signal readout is based on electrical impedance tomography applied to the photoresistance: the photovoltage is measured at the matrix boundary at various positions of injected bias current, and the image is reconstructed algorithmically. We present experimental validations for moderate-size infrared imagers based on multilayer graphene (24 pixels) and amorphous vanadium oxide (264 pixels). The reconstruction procedure is mathematically stable, sustainable to variations of pixel resistivity and photosensitivity, and its complexity is that of linear system solution. The proposed method enables unprecedented architecture simplification of imaging devices.

Figures (3)

• Figure 1: Principles of in-plane tomographic image sensor.(a) Arrangement of photoresistive pixels comprising the image sensor and full signal acquisition scheme including peripheral electric contacts routed either to current source or voltmeter. (b) Sensitivity of the boundary photovoltage to the position of illumination spot: the spatial dependence of induced boundary potential (encoded with color) for two dissimilar positions of illuminated photoresistors, each mimiced as local voltage source with $\delta V({\bf r}_i)=1$ V (c) Manipulation of spatial profile of detector sensitivity with bias current: profiles of local bias current $I({\bf r})$ for different injection points (marked with $+$ for source and $-$ for drain). Color intensity encodes the local current.
• Figure 2: Proof-of-principle 24 pixel tomographic image sensor based on multilayer graphene (a) Micro-photograph of matrix detector based on multilayer graphene. Individual reference pixel is enclosed in white square. (b) Map of photovoltage $\delta V = I_{\rm bias}\delta \rho$ recorded upon illumination of reference pixel with bias current $I_{\text{bias}}=20 \ \mu$A (c) Scheme for averaging photo-resistance signals in a matrix photodetector for visualization. The detector signals in each square are to be averaged and displayed as pixel brightness (d) Reconstructed photoresistance in a 24-pixel graphene-based matrix detector when illuminated from various corners of the matrix. Photoresistors marked with red show the nominal position of illumination spot. (e) The relative sensitivity coefficients of the detectors in a $3 \times 3$ graphene matrix detector obtained as a result of applying the correction procedure based on permutation symmetries. (f) The result of the reconstruction of photoresistivity before and after applying the correction procedure when the red detector is illuminated.
• Figure 3: Larger-scale tomographic imager based on vanadium oxide. (a)-(b) Optical micro-photograph of the in-plane matrix detector based on amorphous VO$_x$ with 264 pixels.(c) Reconstructed photoresistance distributions $\delta \rho({\bf r})$ for illumination at red detectors of the matrix, demonstrating stable localization of the illumination spot using tomographic procedure.