Intrinsic Disordered Network in Multiferroic YMnO3 Single Crystals for In-Materio Physical Reservoir Computing Through Tuneable Domain-Wall Structure

Physical reservoir computing (PRC) is an innovative computational paradigm that leverages intrinsic nonlinearity of physical systems to efficiently perform complex tasks. It is discovered that the intrinsically disordered domain structure in multiferroic YMnO3 provides significant nonlinearity, making it a promising candidate for robust PRC with tuneability and functionality at high temperatures. This work explores the potential of YMnO3 single crystals for PRC. PRC performance of YMnO3 is systematically evaluated by analysing its nonlinear responses, phase shifts, and high dimensionality through benchmark tasks such as waveform generation (WG), memory capacity (MC), and second-order nonlinear autoregressive moving average (NARMA2) time-series prediction. This results demonstrate that YMnO3 single crystals exhibit superior performance in these tasks, achieving high accuracy and low power consumption (≈1.77 µW and ≈0.02 nW/domain). These crystals also performed well in practical application of low-power speech recognition. These findings establish YMnO3 as a viable platform for next-generation PRC technologies, addressing critical challenges in the field.