Ultrafast Biomimetic Untethered Soft Actuators with Bone‐In‐Flesh Constructs Actuated by Magnetic Field

Soft actuators with unique mechanics have gained significant interests for unique capabilities and versatile applications. However, their actuation mechanisms (usually driven by light, heat, or chemical reactions) result in long actuation times. Reported magnetically actuated soft actuators can produce rapid and precise motions, yet their complex manufacturing processes may constrain their range of applications. Here, the “bone-in-flesh” is proposed that constructs combining rigid magnetic structures encapsulated within soft polymers to create untethered magnetic soft actuators. This approach enables these soft, impact-resistant, agile actuators with a significantly simplified fabrication process. As demonstration examples, multiple soft actuators are fabricated and tested, including actuators for auxetic properties, 2D–3D transformations, and multi-stable states. As such, this work offers a promising solution to challenges associated with soft actuators to potentially expand their applications in various domains

Designing Weakly Coupled Mems Resonators with Machine Learning-Based Method

We demonstrate a design scheme for weakly coupled resonators (WCRs) by integrating the supervised learning (SL) with the genetic algorithm (GA). In this work, three distinctive achievements have been accomplished: 1) the precise prediction of coupling characteristics of WCRs with an accuracy of 98.7% via SL; 2) the stepwise evolutionary optimization of WCR geometries while maintaining their geometric connectivity via GA; and 3) the highly efficient generation of WCR designs with a mean coupling factor down to 0.0056, which outperforms 98% of random designs. The coupling behavior analysis and prediction are validated with experimental data of coupled microcantilevers from a published work. As such, this newly proposed scheme could shed light upon the structural optimization methods for high-performance MEMS devices with high degree of design freedom

Customizing Mems Designs via Conditional Generative Adversarial Networks

We present a novel systematic MEMS structure design approach based on a 'deep conditional generative model'. Utilizing the conditional generative adversarial network (CGAN) on a case study of circular-shaped MEMS resonators, three major advancements have been demonstrated: 1) a high-throughput vectorized MEMS design generation scheme that satisfies the geometric constraints; 2) MEMS structural customization toward tunable, desired physical properties with excellent generation accuracy; and 3) experience-free design space explorations to achieve extreme physical properties, such as low anchor loss of micro resonators. Excellent agreements with experimental data, numerical ulations, and a previously reported machine learning-based analyzer are achieved for validation of our methodology. As such, the proposed scheme could open up a new class of data-driven, intelligent design systems for a wide range of MEMS applications

Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning

We present a systematic MEMS structural design approach via a "trial-and-error"learning process by using the deep reinforcement learning framework. This scheme incorporates the feedback from each "trial"to obtain sophisticated strategies for MEMS design optimizations. Disk-shaped MEMS resonators are selected as case studies and three remarkable advancements have been realized: 1) accurate overall performance predictions (97.9%) via supervised learning models; 2) efficient MEMS structural optimizations to guarantee targeted structural properties with an excellent generation accuracy of 97.7%; and 3) superior design explorations to achieve one order of magnitude performance enhancement than the training dataset. As such, the proposed scheme could facilitate a wide spectrum of MEMS applications with this data-driven inverse design methodology

Auto-Positioning and Haptic Stimulations via A 35 mm Square Pmut Array

This work reports an engineered platform for the non-contact haptic stimulation on human skins by means of an array of piezoelectric micromachined ultrasonic transducer (pMUT) via the beamforming scheme. Compared to the state-of-art reports, three distinctive achievements have been demonstrated: (1) individual single pMUT unit based on lithium niobate (LN) with measured high SPL (sound pressure level) of 133 dB at 2 mm away; (2) a beamforming scheme simulated and experimentally proved to generate ∼2.3x higher pressure near the focal point; and (3) the combination of auto-positioning and haptic stimulations on volunteers with the smallest reported physical device size to achieve haptic sensations. As such, this work could have practical applications in the broad areas to stimulate haptic sensations, such as AR (Augmented Reality), VR (Virtual Reality), and robotics

Ferromagnetism-Based Insect-Scale Untethered Robots

Ferromagnetism can offer strong magnetic interaction as the fundamental working principle in a wide range of magnetic actuating applications. This dissertation demonstrates the design, fabrication, and testing of three types of untethered insect-scale robots by using soft and hard ferromagnetic materials, including robust crawling robots on the solid ground, fast-moving swimming robots in the liquid, and the smallest untethered flying robots in the air. Specifically, untethered soft crawling robots of 11 mm in length driven by magnetic anisotropy are designed and constructed by a molding process using self-assembled iron filing mesh as the soft ferromagnetic material. By applying an external alternating magnetic field at the resonant frequency of the robot, untethered crawling movements are realized with a speed of ~0.19 cm/s at 2.5 Hz and 46 mT. The soft crawling robot is robust to be still functional after being crushed by a car. An insect-scale swimming robot with a weight of 46 mg is fabricated by a 3D printing process and equipped with two permanent hard magnets of NdFeB. A single axis alternating magnetic system is utilized as the external powering source for controllable mobility and stability in water to achieve a vertical moving speed of 19.1 body length per second and perform key maneuvering functions such as upwards, downwards and stationary motions as well as designated movements toward targeted locations. The single-axis driving mechanism is then used to power untethered insect-scale flying robots of 9.4 mm in the wingspan size in the air with self-stabilized and navigable aerial travels as compared to state-of-art works of flying robots in similar sizes, including hovering, turning, object hitting, and collision survivability. Furthermore, by adding an infrared detecting sensor to a 110-mg flying robot, the system can perform environmental light survey during an untethered flight. The wireless drive mechanism, system operation principle, and flight characteristics can be optimized for further advancements and miniaturizations toward practical applications

Deep learning for non-parameterized MEMS structural design

The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a nonparameterized, topologically unconstrained form using pixelated black-and-white images. After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis. As an example, we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators. With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4.6 × 103 times and 2.6 × 104 times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98.8 ± 1.6% and 96.8 ± 3.1%, respectively. When simultaneously predicting the frequency and the quality factor, up to ~96.0% of the total computation time can be saved during the design process. The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs