An Efficient Biological Codon Recognition Reservoir Computing System Based on Low-Energy Epitaxial Hf<sub>0.52</sub>Zr<sub>0.48</sub>O<sub>2</sub> Ferroelectric Memristors.

The large demand for information processing has stimulated interest in low-power and fast-storage hafnium-based ferroelectric memristors because of their ability to precisely control the state of the resistor by polarization flip-flop without the need for electroforming. However, there is still a lack of hafnium-based ferroelectric memristor with both high stability and ultra-low operating energy consumption, which are the basic conditions for efficient neural network computation with high recognition rates. This article introduces a high-quality epitaxially grown Pd/Hf 0.52 Zr 0.48 O 2 (HZO) /La 0.67 Sr 0.33 MnO 3 /SrTiO 3 ferroelectric memristor. The device offers high stability, such as multi-stage stable storage states (16-state retention time can exceed 10 4 s), high endurance performance (10 8 cycles), and stable pulse modulation. At the same time, the device has an ultra-low energy consumption of 121 fJ. In addition, the HZO memristor is capable of a wide range of synaptic behaviors and logic operations. Importantly, this work is the first to apply a reservoir computing network based on HZO memristors to the field of biological genetics. The network successfully achieves a biological codon recognition accuracy of over 97% via the dual-feature strategy. This work provides concrete system and

Design: ideas for achieving low-cost and high-accuracy codon recognition in the biological field.