Fig. 3: Optical ANN in the system.

a The stable and symmetric ΔT potentiation and depression in 8 cycles under different control powers. In each cycle, the control time delay was continuously decreased from 5.47 to 0 ps in the first half cycle and then increased to 5.47 ps in the second half cycle. b The approximately linear relationship between weight and Δτ. Based on the experimental results in (a), we encoded Δτ in the range of 0 to 5.47 ps into 20 weight levels. c, d The multilevel ΔT states for the potentiation (c) and depression (d) process under different control powers. The inset shows the minimum 5 ΔT states under \({P}_{{Ctl}}=1 \, {{{\rm{mW}}}}\), indicating adjacent ΔT states can be distinguished. e The training accuracy and cost in 30 epochs for a single layer ANN to classify a custom training dataset. f The experimental and simulated output vector components for the custom testing dataset. g The average output vector components for the true label H, U, S, and T in both experiment and simulation. h The testing confusion matrix in experiment and simulation for the custom testing dataset. i The training accuracy and cost in 100 epochs for a single layer ANN to classify the MNIST training dataset. j The testing confusion matrix in simulation for the MNIST testing dataset.