Selective manipulation of micrometric objects in a standard microscopy environment is possible with miniaturized acoustical tweezers.

Triggered by the pioneering research on graphene, the family of two-dimensional layered materials (2DLMs) has been investigated for more than a decade, and appealing functionalities have been demonstrated. However, there are still challenges inhibiting high-quality growth and circuit-level integration, and results from previous studies are still far from complying with industrial standards. Here, we overcome these challenges by utilizing machine-learning (ML) algorithms to evaluate key process parameters that impact the electrical characteristics of MoS₂ top-gated field-effect transistors (FETs). The wafer-scale fabrication processes are then guided by ML combined with grid searching to co-optimize device performance, including mobility, threshold voltage and subthreshold swing. A 62-level SPICE modeling was implemented for MoS₂ FETs and further used to construct functional digital, analog, and photodetection circuits. Finally, we present wafer-scale test FET arrays and a 4-bit full adder employing industry-standard design flows and processes. Taken together, these results experimentally validate the application potential of ML-assisted fabrication optimization for beyond-silicon electronic materials.

Contactless manipulation of microparticles is demonstrated with single-beam acoustical tweezers based on precursor swirling Rayleigh waves. These surface waves degenerate into acoustical vortices when crossing a stack made of a fluid layer and its solid support, hence creating a localized acoustical trap in a fluid cavity. They can be synthesized with a single interdigitated transducer whose spiraling shape encodes the phase of the field like a hologram. For applications, these tweezers have many attractive features: they are selective, flat, easily integrable, and compatible with disposable substrates.

When an acoustic wave travels in a lossy medium such as a liquid, it progressively transfers its pseudo-momentum to the fluid, which results in a steady flow called acoustic streaming. This phenomenon involves a balance between sound attenuation and shear, such that the streaming flow does not vanish in the limit of vanishing viscosity. Hence, the effect of viscosity has long been ignored in acoustic streaming experiments. Here, we investigate the acoustic streaming in sessile droplets exposed to surface acoustic waves. According to experimental data, the flow structure and velocity magnitude are both strongly influenced by the fluid viscosity. We compute the sound wave propagation and hydrodynamic flow motion using a numerical method that reduces memory requirements via a spatial filtering of the acoustic streaming momentum source terms. These calculations agree qualitatively well with experiments and reveal how the acoustic field in the droplet, which is dominated by a few caustics, controls the flow pattern. We evidence that chaotic acoustic fields in droplets are dominated by a few caustics. It appears that the caustics drive the flow, which allows for qualitative prediction of the flow structure. Finally, we apply our numerical method to a broader span of fluids and frequencies. We show that the canonical case of the acoustic streaming in a hemispherical sessile droplet resting on a lithium niobate substrate only depends on two dimensionless numbers related to the surface and bulk wave attenuation. Even in such a baseline configuration, we observe and characterize four distinct flow regimes.

From radio-electronics signal analysis to biological sample actuation, surface acoustic waves (SAWs) are involved in a multitude of modern devices. However, only the most simple standing or progressive waves such as plane and focused waves have been explored so far. In this paper, we expand the SAW toolbox with a wave family named "swirling surface acoustic waves" which are the 2D anisotropic analogue of bulk acoustic vortices. Similarly to their 3D counterpart, they appear as concentric structures of bright rings with a phase singularity in their center resulting in a central dark spot. After the rigorous mathematical definition of these waves, we synthesize them experimentally through the inverse filtering technique revisited for surface waves. For this purpose, we design a setup combining arrays of interdigitated transducers and a multichannel electronic that enables one to synthesize any prescribed wave field compatible with the anisotropy of the substrate in a region called the “acoustic scene.” This work opens prospects for the design of integrated acoustic vortex generators for on-chip selective acoustic tweezing.

The rapid development of machine vision applications demands hardware that can sense and process visual information in a single monolithic unit to avoid redundant data transfer. Here, we design and demonstrate a monolithic vision enhancement chip with light-sensing, memory, digital-to-analog conversion, and processing functions by implementing a 619-pixel with 8582 transistors and physical dimensions of 10 mm by 10 mm based on a wafer-scale two-dimensional (2D) monolayer molybdenum disulfide (MoS ₂ ). The light-sensing function with analog MoS ₂ transistor circuits offers low noise and high photosensitivity. Furthermore, we adopt a MoS ₂ analog processing circuit to dynamically adjust the photocurrent of individual imaging sensor, which yields a high dynamic light-sensing range greater than 90 decibels. The vision chip allows the applications for contrast enhancement and noise reduction of image processing. This large-scale monolithic chip based on 2D semiconductors shows multiple functions with light sensing, memory, and processing for artificial machine vision applications, exhibiting the potentials of 2D semiconductors for future electronics.