QD-TMD Heterojunction Synapse for Infrared Object Recognition
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Solution Overview
Problem
Current optoelectronic devices for autonomous driving, such as LiDAR, face challenges in accurately recognizing objects due to overlapping optical signals with visible light, particularly in general atmospheric environments. Additionally, these devices require rapid and sensitive photodetection capabilities for infrared wavelength signals to ensure safe and stable autonomous driving.
Innovation Solution
An optoelectronic synaptic device is developed, featuring a photoactive layer with a heterojunction of inorganic quantum dots and a two-dimensional semiconductor material. This device is designed to respond to infrared wavelengths, enabling accurate object recognition and simulating human visual-brain functions with neuromorphic characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR based on InGaAs is used for 900 nm band detection, then infrared wavelength detection capability is improved, but recognition rate is lowered due to visible light overlap interference
Solution Approach 1:
The patent applies local quality by creating a heterojunction with distinct regional properties: quantum dots with specific size (2-5 nm) provide infrared sensitivity while the two-dimensional semiconductor material (MoS2, WS2, MoSe2, WSe2) provides visible light filtering. This spatial differentiation of material properties within the photoactive layer enables selective wavelength response, allowing the device to detect infrared signals while rejecting visible light interference that plagues conventional LiDAR systems.
Solution Approach 2:
The patent employs composite materials by combining quantum dots and two-dimensional semiconductor materials in a heterojunction structure. This composite approach leverages the complementary strengths of each material: quantum dots offer tunable infrared absorption through quantum confinement effects, while two-dimensional semiconductors provide atomic-layer precision and visible light transparency. The synergistic combination resolves the contradiction by achieving both infrared detection capability and visible light rejection in a single integrated photoactive layer.
2Adaptability or versatility
If multiple components are used for risk avoidance and autonomous driving processes, then functional completeness is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent merges multiple functional components into a single integrated optoelectronic synaptic device. The heterojunction photoactive layer simultaneously performs photodetection, synaptic computation, and neuromorphic processing functions that traditionally required separate components. The device integrates infrared detection, visible light rejection, and learning/forgetting characteristics in one structure, dramatically reducing system complexity while maintaining complete risk avoidance functionality for autonomous driving applications.
Solution Approach 2:
The patent achieves universality by designing a multi-functional device that can perform diverse operations within a single structure. The optoelectronic synaptic device simultaneously serves as an infrared detector, a synapse with learning capabilities, and a neuromorphic computing element. This universal design enables the device to handle multiple tasks including object detection, risk assessment, and decision-making processes, eliminating the need for separate specialized components and reducing overall system complexity.
3Speed
If processing speed is increased for recognition-computation-determination-response sequence, then autonomous driving response time is improved, but power consumption increases
Solution Approach 1:
The patent replaces conventional electronic computation with optoelectronic synaptic processing that mimics biological neural mechanisms. The heterojunction device utilizes photo-induced charge separation and trapping effects to create analog synaptic weights, enabling parallel processing of recognition, computation, determination, and response operations. This substitution of traditional sequential electronic processing with optoelectronic neuromorphic processing achieves ultra-high-speed operation while consuming minimal power, as the device operates passively under illumination without requiring active switching or regeneration.
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
The optoelectronic synaptic device effectively enhances object recognition accuracy by sensitively responding to infrared wavelength signals, while also implementing learning and forgetting characteristics, thus supporting the development of stable and safe autonomous driving systems.
Implementation Method 1
An optoelectronic synaptic device with a photoactive layer comprising a heterojunction of inorganic quantum dots and a two-dimensional semiconductor material, capable of responding to near-infrared wavelengths
Implementation Method 2
A heterojunction may be formed by directly contacting the inorganic quantum dots and the two-dimensional semiconductor material
Data Source
AI summary
As the optoelectronic synaptic device according to a preferred embodiment includes a photoactive layer in which a heterojunction is formed as inorganic quantum dots that accept a near-infrared light signal directly contacts a transition metal dichalcogenide as a two-dimensional semiconductor material that exhibits synaptic characteristics, there is an effect of making a synaptic response to an optical signal in the near-infrared wavelength range. Therefore, as a function of simulating the human visual-brain function, which shows the neuromorphic characteristics by the photo response (visual response) of the infrared wavelength, together with light detection characteristics sensitively and rapidly responding to an infrared wavelength signal as well as a visible light signal, can be implemented in a single device for the sake of accurate recognition of objects, it can be easily applied in the autonomous driving mobility field.