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Data · dataset · 2026

MEMS-Driven Dynamic Pupil Expansion for High-Performance Retinal Projection Near-Eye Displays

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Near-eye displays (NEDs) have demonstrated tremendous potential across diverse fields.

Description

However, the eye-box size of a retinal projection display (RPD) is fundamentally constrained by the optical architecture and system volume. While pupil expansion techniques can replicate optical paths and viewpoints, existing static schemes often trade off optical efficiency for eye-box size, leading to energy loss and degraded image quality.

To address this challenge, we propose a new pupil expansion approach for laser-driven retinal projection augmented reality (AR) displays that integrate a microelectromechanical systems (MEMS) scanning mirror. By forming images directly on the retina, this architecture extends the depth of field and alleviates visual fatigue during viewing. Simultaneously, angular modulation of the converging laser beam via the MEMS mirror enables effective expansion of the retinal projection viewpoints.

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The designed optical architecture exhibits high imaging performance, achieving a modulation transfer function (MTF) exceeding 0.3 at 66.7 lp/mm at the central viewpoint, with system distortion maintained below 3%. Notably, the MEMS scanning mirror enables lateral shifting of the exit pupil, forming a large eye-box of 10 mm × 10 mm. This approach breaks the inherent coupling between eye-box expansion and optical efficiency found in static methods, thus achieving high light efficiency without compromising image quality due to aberrations.

Experimental results validate the system’s performance under accommodation distances ranging from 30 to 120 cm, confirming excellent image quality and luminance uniformity across the eye-box. This work demonstrates the feasibility of MEMS-enabled pupil expansion and supports future development of compact, high-performance retinal projection displays.

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Image 75% · Imaging 75%
Provenance · 1 source records, 11 field assertions
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ZivaHuboai:figshare.com:article/340285745 d agoJSON v1
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