Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO₃/SrRuO₃ structure via the manipulation of an imprint field (E_imp). It is shown that the volatile FD with E_imp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible E_imp displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing.

储层计算（RC）以较低的训练成本提供高效的时间信息处理。RC的全铁电实现很有吸引力，因为它可以充分发挥铁电忆阻器的优点（例如良好的可控性）；然而，由于开发具有针对储存器和读出网络的明显不同开关特性的铁电忆阻器的挑战，这一点尚未得到证实。在这里，我们通过实验演示了一种全铁电 RC 系统，其储存器和读出网络分别由易失性和非易失性铁电二极管 (FD) 实现。挥发性和非挥发性 FD 源自相同的 Pt/BiFeO ₃ /SrRuO ₃结构，通过压印场 ( E _imp）。结果表明，具有E _imp的易失性 FD表现出短期记忆和非线性，而具有可忽略的E _imp的非易失性 FD表现出长期增强/抑制，分别满足储存器和读出网络的功能要求。因此，全铁电 RC 系统能够处理各种临时任务。特别是，它在 Hénon 图时间序列预测中实现了 0.017 的超低归一化均方根误差。此外，易失性和非易失性 FD 均表现出在环境空气中的长期稳定性、高耐用性和低功耗，使全铁电 RC 系统成为用于时间信息处理的可靠且低功耗的神经形态硬件。