All-ferroelectric implementation of reservoir computing.
All-ferroelectric implementation of reservoir computing.
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DOI：
10.1038/s41467-023-39371-y
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发表时间：
2023-06-16
影响因子：
16.6
通讯作者：
Liu JM
中科院分区：
文献类型：
--
作者：
Chen Z;Li W;Fan Z;Dong S;Chen Y;Qin M;Zeng M;Lu X;Zhou G;Gao X;Liu JM
油藏计算以较低的训练成本提供了高效的时间信息处理。RC的全铁电实现很有吸引力，因为它可以充分利用铁电忆阻器的优点(例如，良好的可控性)；然而，由于开发特定于存储和读出网络的具有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，我们实验演示了一个全铁电RC系统，其存储和读出网络分别由易失性和非易失性铁电二极管(FD)实现。挥发性和非易失性FD通过压印场(EIMP)的操作从相同的铂/BiFeO_3/SrRuO_3结构中得到。结果表明，含EIMP的挥发性FD表现出短时记忆和非线性，而EIMP可忽略的非挥发性FD表现出长期的增减效应，分别满足了储能器和读出网络的功能要求。因此，全铁电RC系统能够胜任各种临时任务。特别是，在Hénon图时间序列预测中，它获得了0.017的超低归一化均方误差。此外，挥发性和非挥发性FDs在环境空气中都表现出长期稳定性、高耐久性和低功耗，有望成为一种可靠的、低功耗的时间信息处理神经形态硬件。虽然油藏计算可以有效地处理时间信息，但由于缺乏健壮和节能的硬件，其硬件实现仍然是一个挑战。在这里，作者开发了一个全铁电储集层计算系统，在时间序列预测等各种任务中表现出高精度和低功耗。
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 (Eimp). It is shown that the volatile FD with Eimp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible Eimp 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. While reservoir computing can process temporal information efficiently, its hardware implementation remains a challenge due to the lack of robust and energy efficient hardware. Here, the authors develop an all-ferroelectric reservoir computing system, showing high accuracies and low power consumptions in various tasks like the time-series prediction.
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影响因子：
16.6
作者：
Bellec, Guillaume;Scherr, Franz;Maass, Wolfgang
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作者：
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影响因子：
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作者：
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