Physical reservoir computing with visible-light signals using dye-sensitized solar cells

Abstract

Physical reservoir computing (PRC) with visible-light signals was demonstrated using dye-sensitized solar cells. The short-term memory required for PRC was confirmed using light pulse inputs. Waveform learning was demonstrated for nonlinear autoregressive moving-average time series level 2 (NARMA2) signals with normalized mean square error of 0.027. The relatively slow (milliseconds to seconds) and complex charge transfer dynamics in the TiO _2 porous layer with redox reactions in the solution phase provided the characteristics required for PRC.

Keywords

• physical reservoir computing
• dye-sensitized solar cells
• machine-learning