Reinforcement learning (RL) is a mathematical framework of neural learning by trial and error that revolutionized the field of artificial intelligence. However, until now, RL has been implemented in algorithms with the compatibly of traditional complementary metal-oxide-semiconductor-based von Neumann digital platforms, which thus limits performance in terms of latency, fault tolerance, and robustness. Here, we demonstrate that nanocolumnar (∼12 nm) HfO₂ structures can be used as building blocks to conduct the RL within the material by combining its stress-adjustable charge transport and memory functions. Specifically, HfO₂ nanostructures grown by the sputtering method exhibit self-assembled vertical nanocolumnar structures that generate voltage depending on the impact of stress under self-biased conditions. The observed results are attributed to the flexoelectric-like response of HfO₂. Further, multilevel current (>10^–3 A) modulation with touch and controlled suspension (∼10^–12 A) with a short electric pulse (100 ms) were demonstrated, yielding a proof-of-concept memory with an on/off ratio greater than 10⁹. Utilizing multipattern dynamic memory and tactile sensing, RL was implemented to successfully solve a maze game using an array of 6 × 4. This work could pave the way to do RL within materials for a variety of applications such as memory storage, neuromorphic sensors, smart robots, and human–machine interaction systems.

中文翻译：

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超薄 HfO2 的类挠曲电响应支持的自适应记忆和材料内强化学习

强化学习 (RL) 是一种通过反复试验进行神经学习的数学框架，彻底改变了人工智能领域。然而，到目前为止，RL 已经在与传统的基于互补金属氧化物半导体的冯诺依曼数字平台兼容的算法中实现，因此在延迟、容错和鲁棒性方面限制了性能。在这里，我们展示了纳米柱状 (∼12 nm) HfO ₂结构可以用作构建块，通过结合其应力可调的电荷传输和记忆功能来在材料内进行 RL。具体来说，HfO ₂通过溅射法生长的纳米结构表现出自组装的垂直纳米柱状结构，该结构根据自偏置条件下应力的影响产生电压。观察到的结果归因于 HfO ₂的类挠曲电响应。^{此外，演示了带有触摸和受控悬架 (∼10 –12} A) 以及短电脉冲 (100 ms) 的多级电流 (>10 ^–3 A) 调制，产生了具有开/关比的概念验证存储器大于 10 ⁹. 利用多模式动态记忆和触觉传感，RL 成功地解决了使用 6×4 阵列的迷宫游戏。这项工作可以为各种应用的材料内进行 RL 铺平道路，例如记忆存储，神经形态传感器，智能机器人和人机交互系统。