Adaptative machine vision with microsecond-level accurate perception beyond human retina.
Ling LiShasha LiWenhai WangJielian ZhangYiming SunQunrui DengTao ZhengJianting LuWei GaoMengmeng YangHanyu WangYuan PanXueting LiuYani YangJingbo LiNengjie HuoPublished in: Nature communications (2024)
Visual adaptive devices have potential to simplify circuits and algorithms in machine vision systems to adapt and perceive images with varying brightness levels, which is however limited by sluggish adaptation process. Here, the avalanche tuning as feedforward inhibition in bionic two-dimensional (2D) transistor is proposed for fast and high-frequency visual adaptation behavior with microsecond-level accurate perception, the adaptation speed is over 10 4 times faster than that of human retina and reported bionic sensors. As light intensity changes, the bionic transistor spontaneously switches between avalanche and photoconductive effect, varying responsivity in both magnitude and sign (from 7.6 × 10 4 to -1 × 10 3 A/W), thereby achieving ultra-fast scotopic and photopic adaptation process of 108 and 268 μs, respectively. By further combining convolutional neural networks with avalanche-tuned bionic transistor, an adaptative machine vision is achieved with remarkable microsecond-level rapid adaptation capabilities and robust image recognition with over 98% precision in both dim and bright conditions.
Keyphrases
- deep learning
- convolutional neural network
- high frequency
- endothelial cells
- molecular dynamics simulations
- machine learning
- high resolution
- transcranial magnetic stimulation
- induced pluripotent stem cells
- pluripotent stem cells
- diabetic retinopathy
- risk assessment
- climate change
- human health
- quantum dots
- loop mediated isothermal amplification