Fig. 5: Simulations of O-FGT-based neural network for microorganism recognition and motion detection.

a Optical microscopy image of the short-channel O-FGT device. Scale bar, 5 μm. The inset is a SEM image of the MoS₂ channel in O-FGT. Scale bar, 500 nm. b The output curves of O-FGT with the channel length of 80 nm, which was tuned into 256 resistive states by light, and each resistive state was read by a drain voltage sweeping from 0 to −1 V. c Cumulative positive and negative photo-responses with progressive multilevel states under 516 nm laser, where the laser pulse width was 10 μs. Linear fittings were made to the variations of drain current with time for both writing and erasing processes. d Structure of the YOLOv8 network used for microorganism MDR. The input is a motion video of microorganisms. Backbone part is a residual neural network (ResNet), responsible for extracting features from the input image for subsequent processing and analysis by neck part. The neck part is a feature pyramid network (FPN), used to reduce or adjust the dimensionality of features from the backbone part for better recognition and motion tracking of microorganisms. In our network, the head part is a classifier, which is responsible for extracting target and categorizing the positions of microorganisms from the feature map output by neck, and generating the results of identification and motion tracking of microorganisms. e The annotated images with detection results for microorganisms by using O-FGT array (left) and GPU (right). Calculated loss (f), precision (g), accuracy (h), and recall (i) as a function of training epochs using O-FGT array and GPU. j The motion tracking results of microorganisms using O-FGT array.