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      Microwave impedance microscopy (MIM) is an emerging scanning probe technique that enables non-contact, nanoscale measurement of local complex permittivity. By integrating an ultrasensitive, phase-resolved microwave sensor with a near-field probe, MIM has made significant contributions to diverse fundamental and applied fields. These include strongly correlated and topological materials, two-dimensional and biological systems, as well as semiconductor, acoustic, and MEMS devices. Concurrently, notable progress has been made in refining the MIM technique itself and broadening its capabilities. However, existing literature has focused exclusively on linear MIM based on homodyne architectures, where reflected or transmitted microwave is demodulated and detected at the incident frequency. As such, linear MIM lacks the ability to probe local electrical nonlinearity, which is widely present, for example, in dielectrics, semiconductors, and superconductors. Elucidating such nonlinearity with nanoscale spatial resolution would provide critical insights into semiconductor processing and diagnostics as well as fundamental phenomena like local symmetry breaking and phase separation.       To address this shortcoming, UC Berkeley researchers have introduced a novel methodology and apparatus for performing multi-harmonic MIM to locally probe electrical nonlinearities at the nanoscale. The technique achieves unprecedented spatial and spectral resolution in characterizing complex materials. It encompasses both hardware configurations enabling multi-harmonic data acquisition and the theoretical and calibration protocols to transform raw signals into accurate measures of intrinsic nonlinear permittivity and conductivity. The advance extends existing linear MIM into the nonlinear domain, providing a powerful, versatile, and minimally invasive tool for semiconductor diagnostics, materials research, and device development.

A novel manufacturing process that shapes complex components from nanostructured materials using a combination of pressure, heat, and electricity.

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 The dynamic patterning of 3D optical point clouds has emerged as a key enabling technology in volumetric processing across a number of applications. In the context of biological microscopy, 3D point cloud patterning is employed for non-invasive all-optical interfacing with cell ensembles. In augmented and virtual reality (AR/VR), near-eye display systems can incorporate virtual 3D point cloud-based objects into real-world scenes, and in the realm of material processing, point cloud patterning can be mobilized for 3D nanofabrication via multiphoton or ultraviolet lithography. Volumetric point cloud patterning with spatial light modulators (SLMs) is therefore widely employed across these and other fields. However, existing hologram computation methods, such as iterative, look-up table-based and deep learning approaches, remain exceedingly slow and/or burdensome. Many require hardware-intensive resources and sacrifices to volume quality.To address this problem, UC Berkeley researchers have developed a new, non-iterative point cloud holography algorithm that employs fast deterministic calculations. Compared against existing iterative approaches, the algorithm’s relative speed advantage increases with SLM format, reaching >100,000´ for formats as low as 512x512, and optimally mobilizes time multiplexing to increase targeting throughput. 

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Plasma medicine has emerged as a promising approach for treatment of biofilm-related and virus infections, assistance in cancer treatment, and treatment of wounds and skin diseases. However, an important challenge arises with the need to adapt control policies, often only determined after each treatment and using limited observations of therapeutic effects. Control policy adaptation that accounts for the variable characteristics of plasma and of target surfaces across different subjects and treatment scenarios is needed. Personalized, point-of-care plasma medicine can only advance efficaciously with new control policy strategies.To address this opportunity, UC Berkeley researchers have developed a novel control scheme for tailored and personalized plasma treatment of surfaces. The approach draws from concepts in deep learning, Bayesian optimization and embedded control. The approach has been demonstrated in experiments on a cold atmospheric plasma jet, with prototypical applications in plasma medicine.

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The inventors have discovered a new way to generate ultrafast back-gating, by leveraging the surface band bending inherent to many semiconductor materials. This new architecture consists of a standard bulk semiconductor material and a layered material on the surface. Optical pulses generate picosecond time-varying electric fields on the surface material. The inventors have successfully applied this method to a quantum well Rashba system, as this is considered today one of the most promising candidates for spin-based devices, such as the Datta Das spin-transistor. The technology can induce an ultrafast gate and drive time-dependent Rashba and quantum well dynamics never observed before, with switching faster than 10GHz. This approach minimizes lithography and will enable light-driven electronic and spintronics devices such as transistors, spin-transistors, and photo-controlled Rashba circuitry. This method can be applied with minimal effort to any two-dimensional material, for both exfoliated and molecular beam epitaxy grown samples. Electric field gating is one of the most fundamental tuning knobs for all modern solid-state technology, and is the foundation for many solid-state devices such as transistors. Current methods for in-situ back-gated devices are difficult to fabricate, introduce unwanted contaminants, and are unsuited for picosecond time-resolved electric field studies.  

UCI researchers developed a new device that uses electricity to drive ion separation across a membrane. This device can increase the energy efficiency of various applications such as artificial photosynthesis, water desalination, and chemical separations.

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UCLA researchers in the Department of Materials Science and Engineering have developed a nanofabrication method for improving the thermal properties of polycrystalline diamond films grown by chemical vapor deposition.

UCLA researchers in the Department of Materials Science & Engineering have developed a new method of diamond deposition in integrated circuit vias for thermal dissipation.

Organic-inorganic hybrid perovskites have demonstrated tremendous potential for next-generation electronic and optoelectronic devices due to their remarkable carrier dynamics. However, current studies of electronic and optoelectronic devices have been focused on polycrystalline materials, due to the challenges in synthesizing device compatible high quality single crystalline materials.

UCLA researchers in the Department of Materials Science and Engineering have developed a low-temperature metal patterning technique.

Flexible semiconductors are far less costly, resource and energy intensive than conventional silicon production. Yet, as an unintended consequence of semiconductor printing, the films produced contain structural heterogeneities, or defects, which can limit their capacity to shuttle energy, or, information, over device-relevant scales. To be able to fully embrace this new, greener process, it is essential to elucidate which physical material properties most influence energy flow and which defects are most deleterious to efficient energy transport so that they can be targeted for elimination at the materials processing stage. Although some rather complex approaches have recently been used to track energy flow, the applicability of each one depends on specifics of the semiconductor properties (bandgap, excitonic vs charge carrier form of excitation, strong absorption or emission). Existing techniques cannot therefore be applied to a broad range of materials, and often necessitate adapting samples to fit the specific requirements of the technique. A broadly applicable approach is therefore needed to non-invasively and simultaneously reveal and correlate material morphology and energy flow patterns across many scales.    Researchers at the University of California, Berkeley have developed a new high-sensitivity, non-invasive, label-free, time-resolved optical scattering microscope able to map the flow of energy in any semiconductor, and correlate it in situ to the semiconductor morphology. This device has been shown as a far simpler approach to spatio-temporally characterize the flow of energy in either charge or exciton form, irrespective of the electronic properties of the material, and with few-nm precision. Furthermore, built into this approach is the unprecedented capability to perform in situ correlation to the underlying physical structure of the material. 

UCLA researchers in the Department of Chemistry and Biochemistry & Physics and Astronomy have developed a novel method to lithograph two polished solid surfaces by using a simple mechanical alignment jig with piezoelectric control and a method of pressing them together and solidifying a material.

Prof. Yang and colleagues have developed a novel method of preparing organic-inorganic thin films using a solution process followed by vapor treatment, presenting a low-cost, high-performance solution method of producing optoelectronic devices.

UCLA researchers in the Department of Electrical Engineering have invented a novel biocompatible flexible device fabrication method using fan-out wafer level processing (FOWLP).