Thin-Film Neuromorphic Opto-Electronic Device for AR Latency
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Solution Overview
Problem
Current augmented reality (AR) systems suffer from significant motion-to-photon latency, leading to delayed responses that can cause 'cybersickness' and limit real-time processing capabilities, hindering applications such as autonomous vehicles and live recognition, despite growing market demand.
Innovation Solution
The development of a thin-film neuromorphic opto-electronic device comprising photoresponsive elements, mirrors or optical filtering devices, and waveguides, which integrate OLEDs and tunable complex index of refraction thin films to enable high-bandwidth, low-latency processing by performing computations optically within each pixel, mimicking neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional AR systems are used, then the system structure is simple, but motion-to-photon latency is high causing cybersickness and limiting real-time processing
Solution Approach 1:
The patent merges the display and sensing functions into a single integrated pixel structure. Each pixel contains both an OLED display element and a photodetector sensing element, allowing simultaneous display and sensing operations within the same physical footprint. This integration eliminates the need for separate display and sensor systems, reducing overall system complexity while enabling real-time processing that lowers motion-to-photon latency.
Solution Approach 2:
The patent utilizes the vertical dimension by stacking the OLED and photodetector layers in a multi-layer pixel structure. The OLED emits light upward while the photodetector detects light from below, allowing both functions to operate simultaneously in different spatial dimensions within the same pixel area. This vertical integration enables real-time sensing and display without increasing lateral pixel density.
2Productivity
If computing tasks are performed centrally, then device complexity is low, but processing speed is insufficient for real-time AI applications
Solution Approach 1:
The patent segments the computing function into distributed operations at the pixel level. Each pixel independently performs sensing and can perform local computing operations on the captured data before transmission to central processors. This segmentation enables parallel processing across millions of pixels simultaneously, dramatically increasing overall processing speed for AI applications while distributing computational load to reduce central processor bottlenecks.
Solution Approach 2:
The patent introduces reconfigurable photonic circuits as intermediary processing elements between the photodetector and central processor. These photonic circuits can perform preliminary image processing, feature extraction, and data filtering operations optically before converting signals to electrical form for digital processing. This intermediary layer reduces the data burden on central processors and enables real-time preprocessing critical for high-speed AI applications.
3Ease of manufacture
If organic materials are used in OLEDs, then manufacturing cost is reduced and flexibility is improved, but performance consistency may be compromised
Solution Approach 1:
The patent employs reconfigurable photonic circuits that can dynamically adjust optical parameters such as wavelength filtering, intensity modulation, and signal timing to compensate for variations in organic OLED material performance. By changing these optical parameters in real-time, the system maintains consistent sensing and display output despite inherent material variations, ensuring reliable performance while retaining the manufacturing advantages of organic materials.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution significantly reduces latency and enhances processing speed, enabling seamless real-time overlays in AR systems and improving the performance of AI applications by integrating computing tasks within the AR sensors and displays.
Implementation Method 1
the at least one mirror or optical filtering device selectively reflects a range of frequencies and is more translucent to frequencies outside the range
Implementation Method 2
at least one deposited mirror or optical filtering device, comprising at least two reflective thin film stacks with an interstitial medium therebetween forming at least one optical cavity
Implementation Method 3
at least one thin film photoresponsive element
Implementation Method 4
OLEDs make use of thin organic films that emit light when voltage is applied across the device
Implementation Method 5
at least one tunable complex index of refraction thin film positioned in the cavity... the index of refraction of the at least one tunable complex index of refraction thin film is used to selectively tune at least one of spectral reflectance or transmission
Data Source
AI summary
A reconfigurable thin-film photonic filter weight bank comprises at least one photodetector and at least one optical filtering device comprising a pair of reflective thin film stacks with an interstitial medium cavity therebetween forming an optical cavity. Operation of a bank occurs when the reflective film is more selectively reflective to a range of frequencies and is more translucent to frequencies outside the range. The bank can reconfigurably weight signals by varying the cavity sizes or complex indices of refraction, with one photodetector integrating the weighted signals. Detectors can also be embedded inside the individual cavities to output unweighted, but demultiplexed, electrical signals. A neuromorphic opto-electronic system comprises a plurality of interconnected artificial optical neurons, each including at least one thin film neuromorphic opto-electronic device with a reconfigurable thin-film photonic filter weight bank. Related methods are also disclosed.