Spectral machine vision collects both the spectral and spatial dependence (x,y,λ) of incident light, containing potentially useful information such as chemical composition or micro/nanoscale structure.  However, analyzing the dense 3D hypercubes of information produced by hyperspectral and multispectral imaging causes a data bottleneck and demands tradeoffs in spatial/spectral information, frame rate, and power efficiency. Furthermore, real-time applications like precision agriculture, rescue operations, and battlefields have shifting, unpredictable environments that are challenging for spectroscopy. A spectral imaging detector that can analyze raw data and learn tasks in-situ, rather than sending data out for post-processing, would overcome challenges. No intelligent device that can automatically learn complex spectral recognition tasks has been realized.       UC Berkeley researchers have met this opportunity by developing a novel photodetector capable of learning to perform machine learning analysis and provide ultimate answers in the readout photocurrent. The photodetector automatically learns from example objects to identify new samples. Devices have been experimentally built in both visible and mid-infrared (MIR) bands to perform intelligent tasks from semiconductor wafer metrology to chemometrics. Further calculations indicate 1,000x lower power consumption and 100x higher speed than existing solutions when implemented for hyperspectral imaging analysis, defining a new intelligent photodetection paradigm with intriguing possibilities.

      Optical tweezers generated with light modulation devices have great importance for highly precise laser imaging and addressing systems e.g. excitation and readout of single atoms, imaging of interactions between molecules, or highly precise spatial trapping and movement of particles. To generate dynamic optical tweezers adjustable at the microsecond scale, acousto-optic deflectors (AOD) are commonly used to modulate the spatial profile of laser light. Dynamic optical tweezers are increasingly relevant for emerging technologies such as neutral atom quantum computers, and tightly focused laser spot arrays may enable advanced imaging and/or semiconductor processing applications. However, dynamic optical tweezer systems capable of rapid, aberration-free movement of one or multiple atoms in independent, arbitrary three-dimensional trajectories with minimal aberration have not yet been realized.      UC Berkeley researchers have developed a dynamic optical tweezer system that overcomes significant defects such as limited 2D motion and optical aberration present in existing art. Carefully designed waveform modulation of one or more acousto-optic deflector lenses (AODLs) enables atomic addressing and rapid tweezer motions while minimizing significant optical aberrations present in prior methods. The invention is capable of microsecond scale single or multi tweezer motion in arbitrary three-dimensional trajectories without the use of translation stages. The invention can flexibly address one atom, multiple atoms, or the entire array.

      Effective thermal management remains a critical challenge in designing and operating next-generation electronics, data centers, and energy systems. Devices are steadily shrinking and handling increased power densities. Traditional cooling strategies, such as heat sinks and immersive cooling systems, fall short in delivering the targeted, localized cooling needed to prevent or address thermal hotspots. Current solutions for localized hotspot cooling require active, energy-intensive methods like pumping of coolants and complex thermal architecture design.       To overcome these challenges, UC Berkeley researchers present a transformative passive method for localized, autonomous cooling of hotspots. The cooling system delivers effective, localized cooling across various device surfaces and geometries, including those geometries wherein cooling media must move against gravity. The benefits of the present system will be appreciated for computer chip and other electronics cooling, microgravity applications, battery thermal management. Beyond thermal management, the underlying system may also open novel avenues in fluid manipulation and energy harvesting.

      Molecular dynamics (MD) is a computational materials science modality widely used in academic and industrial settings for materials discovery and more. A critical aspect of modern MD calculations are machine learning interatomic potentials (MLIPs), which learn from reference quantum mechanical calculations and predict the energy and forces of atomic configurations quickly. MLIPs allow for more accurate and comprehensive exploration of material/molecular properties at-scale. However, state-of-the-art MLIP methods mostly use a short-range approximation, which may be sufficient for describing properties of homogeneous bulk systems but fail for liquid-vapor interfaces, dielectric response, dilute ionic solutions with Debye-Huckel screening, and interactions between gas phase molecules. Short-range MLIPs neglect all long-range interactions, such as Coulomb and dispersion interactions.      To address the current shortcoming, UC Berkeley researchers have developed a straightforward and efficient algorithm to account for long-range interactions in MLIPs. The algorithm can predict system properties including those with charged, polar or apolar molecular dimers, bulk water, and water-vapor interfaces. In these cases standard short-range MLIPs lead to unphysical predictions, even when utilizing message passing algorithms. The present method eliminates artifacts while only about doubling the computational cost. Furthermore, it can be incorporated into most existing MLIP architectures, including potentials based on local atomic environments such as HDNPP, Gaussian Approximation Potentials (GAP), Moment Tensor Potentials (MTPs), atomic cluster expansion (ACE), and MPNN (e.g., NequIP, MACE).

      The demand for power electronics with smaller volumes, lighter weights, and lower cost has motivated ongoing investigation into alternative power passive component technologies. Miniaturization of power converters is bottlenecked by magnetics, whose power densities fundamentally reduce at small scales. Capacitors exhibit much more favorable densities at small sizes, but efficient voltage regulation and galvanic isolation are difficult to achieve without magnetics. Therefore piezoelectric components have emerged as compelling alternative passive components for power electronics. However,  their high-performance capabilities have been limited to applications of high load impedance due to the high characteristic of piezoelectric resonators (PRs) themselves.       To overcome this challenge, UC Berkeley researchers have developed novel piezoelectric resonator (PR) designs based on overtones, with enhanced power densities and reduced optimal load impedances. The overtone PRs have been demonstrated to have comparable efficiency to fundamental-mode PRs, while their capabilities for power handling density and lower optimal load impedances are increased. Use of overtone PRs can expand the utility of piezoelectrics to a wider scope of power electronics.

      Probabilistic Seismic Hazard Analysis (PSHA) has become a foundational method for determining seismic design levels and conducting regional seismic risk analyses for insurance risk analysis, governmental hazard mapping, critical infrastructure planning, and more. PSHA traditionally relies on two computationally intensive approaches: Riemann Sum and conventional Monte Carlo (MC) integration. The former requires fine slices across magnitude, distance, and ground motion, and the latter demands extensive synthetic earthquake catalogs. Both approaches become notably resource intensive for low-probability seismic hazards, where achieving a COV of 1% for a 10−4 annual hazard probability may require 108 MC samples.       UC Berkeley researchers have developed an Adaptive Importance Sampling (AIS) PSHA, a novel framework to approximate optimal importance sampling (IS) distributions and dramatically reduce the number of MC samples to estimate hazards. Efficiency and accuracy of the proposed framework have been validated against Pacific Earthquake Engineering Research Center (PEER) PSHA benchmarks covering various seismic sources, including areal, vertical, and dipping faults, as well as combined types. Seismic hazards are calculated up to 3.7×104 and 7.1×103 times faster than Riemann Sum and traditional MC methods, respectively. Coefficients of variation (COVs) are below 1%. Enhanced “smart” AIS PSHA variants are also available that outperform “smart” implementations of Riemann Sum by a factor of up to 130.

      Miniaturization and performance of power electronics is fundamentally limited by magnetic components, whose power densities inherently reduce at small scales. Piezoelectric resonators (PRs), which store energy in the mechanical compliance and inertia of a piezoelectric material, offer various advantages for power conversion including high quality factors, planar form factors, opportunity for batch fabrication, and potential for integration. Contrary to magnetic components, PRs have increased power handling densities at small scales. Noteworthy advancements have been made in magnetic-less, PR-based power converter designs, demonstrating significant achievements in both power density (up to 5.7 kW/cm3) and efficiency (up to >99%). However, while PRs are promising alternative passive components, they cannot be used as drag-and-drop replacements for magnetics; achieving high performance in a PR-based converter requires complicated control of multi-stage switching sequences. A need exists for more practical ways to leverage piezoelectrics in power conversion without such added complexity.      To address this challenge, UC Berkeley researchers have developed a piezoelectric component that may be leveraged to directly emulate the dynamics of a magnetic component. The “active inductor” can serve as a drag-and-drop replacement for bulky magnetic inductors in power converters. Power density and efficiency of underlying piezoelectrics are preserved while the design complexity associated with piezoelectric-based power converters is simplified. Detailed models and control strategies for the piezoelectric-based active inductors have been developed and usage demonstrated in a classic buck converter. The active inductor is further validated with closed-loop simulation results and open-loop experimental results, confirming its inductor-like behavior.

Optical atomic clocks have taken a giant leap in recent years, with several experiments reaching uncertainties at the 10−18 level. The development of synchronized clock networks and transportable clocks that operate in extreme and distant environments would allow clocks based on different atomic standards or placed in separate locations to be compared. Such networks would enable relativistic geodesy, tests of fundamental physics, dark matter searches, and more. However, the leading neutral-atom optical clocks operate on wavelengths of 698 nm (Sr) and 578 nm (Yb). Light at these wavelengths is strongly attenuated in optical fibers, posing a challenge to long-distance time transfer. Those wavelengths are also inconvenient for constructing the ultrastable lasers that are an essential component of optical clocks. To address this problem, UC Berkeley researchers have developed a new, laser-cooled neutral atom optical atomic clock that operates in the telecommunication wavelength band. The leveraged atomic transitions are narrow and exhibit much smaller black body radiation shifts than those in alkaline earth atoms, as well as small quadratic Zeeman shifts. Furthermore, the transition wavelengths are in the low-loss S, C, and L-bands of fiber-optic telecommunication standards, allowing the clocks to be integrated with robust laser technology and optical amplifiers. Additionally, the researchers have identified magic trapping wavelengths via extensive studies and have proposed approaches to overcome magnetic dipole-dipole interactions. Together, these features support the development of fiber-linked terrestrial clock networks over continental distances.

         Recent mass evacuation events, including the 2018 Camp Fire and 2023 Maui Fire, have demonstrated shortcomings in our communication abilities during natural disasters and emergencies. Individuals fleeing dangerous areas were unable to obtain fast or accurate information pertaining to open evacuation routes and faced traffic gridlocks, while nearby communities were unprepared for the emergent situation and influx of persons. Climate change is increasing the frequency, areas subject to, and risk-level associated with natural hazards, making effective communication channels that can operate when mobile network-based systems and electric distribution systems are compromised crucial.         To address this need UC Berkeley researchers have developed a mobile network-free communication system that can function during natural disasters and be adapted to most communication devices (mobile phones and laptops). The self-organized, mesh-based and low-power network is embedded into common infrastructure monitoring device nodes (e.g., pre-existing WSN, LoRa, and other LPWAN devices) for effective local communication. Local communication contains dedicated Emergency Messaging and “walkie-talkie” functions, while higher level connectivity through robust gateway architecture and data transmission units allows for real-time internet access, communication with nearby communities, and even global connectivity. The system can provide GPS-free position information using trilateration, which can help identify the location of nodes monitoring important environmental conditions or allowing users to navigate.

Vacuum grippers are widely used in industry to handle objects via suction pressure. Unicontact suction cups are commonly used for gripping because they are simple to operate and can handle a variety of items, including those that are delicate, large, or inaccessible to jaw grippers. However, suction cup grippers have challenges such as planning a contact location and inertial force-induced grasping failure. To address these challenges, UC Berkeley researchers developed a tactile sensing technology for smart suction cups. This Berkeley sensing technology can detect suction contact and prevent suction cup grasp failures. It can perform tactile sensing of object properties such as roughness or porosity that might lead to grasping failures before they happen. If a grasp failure does happen, the technology gains additional information about why and how the failure occurred　to prevent similar failures in future attempts. Sensing occurs quickly, such that robot behavior can remain fast while increasing performance, efficiency and reliability.　 As compared with other robotic grasping sensing technologies, this smart suction cup technology is affordable, resilient and easy to service. The cup is manufactured using the same process as other suction cups, and electronics are simple and located away from the point-of-contact and protected from damage or hazardous exposure.

The power demands on data centers are large and increasing rapidly. This is straining data center economic and environment impacts, and in turn driving improvements in data center power efficiencies. Data centers have been widely adopting 48 V intermediate bus architectures due to higher efficiency, good flexibility, and reduced cost. However, a major challenge in such systems is the conversion from the 48 V bus to the extreme low voltage and high current operating levels of server CPUs and GPUs.To address this challenge, UC Berkeley researchers developed a multi-phase hybrid power converter architecture. The Berkeley design uses hybrid converter topologies. A switched-capacitor network is smartly merged with a switched-inductor network, resulting in circuit component number reduction and soft-charging operation of the capacitors. Furthermore, the Berkeley architecture integrates a multi-phase control technique to achieve a higher conversion ratio of the switched-capacitor network, which can further improve the overall system efficiency without increasing the circuit size.  

Computer-based, internet-connected, audio/video meeting platforms have become pervasive worldwide, especially since the 2020 emergence of the COVID-19 pandemic lockdown. These meeting platforms include Cisco Webex, Google Meet, GoTo, Microsoft Teams, and Zoom. However, those popular platforms are optimized for meetings in which all the participants are attending the meeting online, individually. Accordingly, those platforms have shortcomings when used for hybrid meetings in which some participants are attending together in-person and others attending online. Also, the existing platforms are problematic for large meetings in big rooms (e.g. classrooms) in which most or all of the participants are in-person. To address those suboptimal meet platform situations, researchers at UC Berkeley conceived systems, methods, algorithms and other software for a meeting platform that's optimized for hybrid meetings and large in-person meetings. The Berkeley meeting platform offers a user experience that's familiar to users of the conventional meeting platforms. Also, the Berkeley platform doesn't require any specialized participant hardware or specialized physical room infrastructure (beyond standard internet connectivity).

The emergence of huge, online digital repositories of data (AKA "big data") has made data mining challenging, especially for researchers, scientists, and businesses. These growing massive pools of online data have made it difficult to find relevant information, "connect the dots", and gain "big picture" perspective. For example, in the area of intellectual property, the access to global patent and trademark information includes billions of documents. To date, visualization of large-scale data sets is typically limited to two-dimensional tables, diagrams, and images. Many find these existing tools inadequate.To address this problem, researchers at UC Berkeley developed systems and methods for visualizing large amounts of data in three-dimensional virtual reality and augmented reality spaces. The initial application for this Berkeley technology has been patent documents. However, it's also applicable to visualize non-patent data, including technical and commercial data, etc.

96 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The ∼10% optical contrast of graphene on specialized substrates like oxide-capped silicon substrates, together with the high-throughput and noninvasive features of optical microscopy, have greatly facilitated the use and research of graphene research for the past decade.  However, substantially lower contrast is obtained on transparent substrates. Visualization of nanoscale defects in graphene, e.g., voids, cracks, wrinkles, and multilayers, formed during either growth or subsequent transfer and fabrication steps, represents yet another level of challenge for most device substrates.     UC Berkeley researchers have developed a facile, label-free optical microscopy method to directly visualize graphene on transparent inorganic and polymer substrates at 30−40% image contrast per graphene layer.  Their noninvasive approach overcomes typical challenges associated with transparent substrates, including insulating and rough surfaces, enables unambiguous identification of local graphene layer numbers and reveals nanoscale structures and defects with outstanding contrast and throughput. We thus demonstrate in situ monitoring of nanoscale defects in graphene, including the generation of nano-cracks under uniaxial strain, at up to 4× video rate.  

3D printing uses additive processes, which add layers on top of each other, to generate shapes. In order to do this, the material used undergoes a phase transformation, from a malleable state to a solid state. This process incorporates the new layer onto the previous layer. Most currently used 3D printing technologies use a phase transition temperature that is higher than the room temperature, which allows printing in air at room temperature. The 3D printing device heats the material to a malleable form, then deposits a layer that cools into a solid. This method does not, however, allow sufficient structural or temporal control for printing biological materials.   UC Berkeley researchers have developed methods and devices for cryogenic 3D printing that enables printing with biological materials. Complex structures can be generated when the object is immersed in a liquid coolant, and this immersion also ensures that already printed layers remain at a constant temperature.