您将学到什么：
- 尽管环境噪音，但仍在设计高敏感的传感器的挑战。
- 蜘蛛网如何用作振动传感器的灵感。
- 为什么以及如何使用机器学习来优化设计。
- How the optimized design was fabricated and tested.
All sensors designed for ultra-low-level signals share a common problem: ambient noise. Whether thermal, electrical, vibration, or other, the resultant poor signal-to-noise ratio (SNR) can easily overwhelm the unknown signal of interest. Thus, even having a super-sensitive sensor may not yield meaningful sensing improvement.
Recognizing this dilemma, a team of researchers fromDelft University of Technology(Netherlands, often called TU Delft) designed one of the world’s most precise microchip sensors. Especially fascinating and useful, it can function at room temperature rather than need to go close to absolute zero to minimize the effect of noise.
By combining nanotechnology and machine learning, and inspired by nature’s spiderwebs, the team was able to devise a nanomechanical vibration sensor featuring extreme isolation from everyday noise. They then tested their proposed design by constructing a microchip version using a nanometer-thick film of silicon nitride (Si3n4）具有高内在应力，屈服强度，温度稳定性和化学惰性的所需材料特性，以及纳米技术中广泛使用的经验。
合并纳米技术和机器学习
领导研究的理查德·诺特（Richard Norte）和米格尔·贝萨（Miguel Bessa）正在寻找结合纳米技术和机器学习的新方法，并受到大自然的启发。著名项目联合领导人理查德·诺特（Richard Norte）说：“在锁定期间，我已经做了十年了，我注意到我的露台上有很多蜘蛛网。我意识到蜘蛛网确实是很好的振动探测器，因为他们想测量网络内的振动以找到猎物，但不像在树外面找到猎物。那么，为什么不搭便于数百万年的进化，并使用蜘蛛网作为超敏感设备的初始模型呢？”
由于团队对蜘蛛网的复杂性一无所知，因此他们决定让机器学习指导发现过程。Project Coeader Miguel Bessa补充说：“我们知道实验和模拟是昂贵且耗时的，因此，对于我的小组，我们决定使用一种称为贝叶斯优化的算法，可以使用一些尝试来找到良好的设计。”
团队成员Dongil Shin随后实施了计算机型号，并应用了机器学习算法以找到新的设备设计（图。1）。
1。Overview of the Bayesian optimization process for designing the spiderweb nanomechanical resonator: (A) designs and simulated mode shapes at the corresponding iterations highlighted by circular markers in the remaining figures. (B) Evolution of the quality factor Qm(the red markers and red line indicate the highest quality factor until that iteration). (C) The frequency fm。(D) The distance from a previous optimized point to the point considered in that iteration. (E) Values of the six design parameters at every iteration, providing an idea of how the designs change in the optimization process. The abscissa for (B)-(E) is the same and corresponds to the design iterations as the optimization process evolves. The blue region in (B)-(D) corresponds to the 40 initial designs that were randomly selected—that is, before starting the Bayesian optimization process—while the red region corresponds to the Bayesian optimization iterations.
To the researcher’s surprise, the algorithm proposed a relatively simple spiderweb out of 150 different spiderweb designs, consisting of only six strings put together in a deceivingly simple way（图2）。
2。Spiderweb nanomechanical resonator model with Nr= 8. The design parameters are d, w1, w2, l1, and l2are the design parameters; di, do, woh, and L are set as 1 µm, 4 µm, 2 µm, and 3 mm, respectively.
The design is isolated from ambient thermal environments via a novel “torsional soft-clamping” mechanism discovered by the data-driven optimization algorithm（图3）。
3.优化的蜘蛛网设计表现出软联系模式：（a）优化振动模式的完整运动，并在悬垂物周围放大，并说明了能量损耗的部分。该图中的灰色部分显示了休息时的结构（无振动）。（b）局部变形和（c）（a）中三个区域的归一化弯曲损耗密度。从上到下：中心（橙色标记），内侧横梁的关节（黄色标记）和边缘（蓝色标记）。
Bessa added, “Dongil’s computer simulations showed that this device could work at room temperature, in which atoms vibrate a lot, but still have an incredibly low amount of energy leaking in from the environment—a higher quality factor, in other words. With machine learning and optimization, we managed to adapt Richard’s spiderweb concept towards this much-better quality factor.”
测试微芯片“ Web”
他们构建了一个微芯片传感器，并通过强制振动微芯片“ Web”并测量振动所花费的时间来进行测试(Fig. 4)。他们从超高效率（UHV）设置中的“回合”测量中确定了系统的质量因子，以避免空气阻尼。
4。Schematic representation of the fabrication process flow and the mechanical characterization setup: Process steps consist of deposition of Si3n4进入硅基板（a），掩模图案（b），si3n4patterning via dry etching (C), mask removal (D), and Si3n4底切和释放（e）。蜘蛛网纳米力学谐振器是由压电执行器共同驱动的，其运动通过平衡的同伴干涉仪（F）光学测量。谐振器放置在UHV腔室内，以达到低于10的压力-8MBAR。VOA：可变光学衰减器；PID：比例积分导数控制器；FPC：纤维极化控制器；φ：纤维担架。
Ringdown measurements involve using piezoelectric stages to resonantly excite the motion of nanomechanical resonators, stopping the drive, and observing decay of the resonators’ motion via interferometric optical readout. The rate of decay of the resonators amplitude gives its rate of energy dissipation and thus its mechanical quality factor.
The results were impressive, with record-breaking isolated vibration at room temperature. Norte said, “We found almost no energy loss outside of our microchip web. The vibrations move in a circle on the inside and don’t touch the outside. This is somewhat like giving someone a single push on a swing and having them swing on for nearly a century without stopping.”
他们测量了机械质量因子（qm), which is the ratio of energy stored in a resonator over the energy dissipated over one cycle of oscillation, to greater than one billion. Further, the compact design doesn’t require sub-micrometer lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales.
Details of the work and its many aspects are in their 11-page paper “蜘蛛网纳米力学谐振器通过贝叶斯优化：受大自然的启发，受到机器学习的指导，“ 出版于Advanced Materials。还有13页Supporting Information包含大量理论分析，描述，模型，仿真和对项目各个方面的实际讨论的文件（不过，有些令人惊讶的是，这项工作没有发布视频）。
