High-performance display technologies require light emitters that remain stable under intense operation while providing exceptional color purity. UC Berkeley researchers have developed stable metal halide perovskite red, green, and blue emitters that utilize both lead-based and lead-free materials. The technology relies on quantum dots integrated into specialized photoresist formulations. These formulations allow for the high-precision fabrication of patterned micro-light emitting diode devices with sub-micron pixel sizes. 

Achieving seamless robotic interaction with physical environments requires a sophisticated blend of sensory perception and logical reasoning. UC Berkeley researchers have developed "RealWorldPlay," a physical artificial intelligence system designed to enhance robotic action through a unified multimodal reasoning framework. The system integrates a visuo-tactile policy—combining sight and touch—with a large language model (LLM) that provides real-time verification feedback and strategic planning. By utilizing a "world model" to generate self-training data, the platform allows robots to autonomously set goals and learn from simulated scenarios, ensuring that their physical actions are both reasoned and verified before execution.

Researchers at the University of California, Davis have developed a method and device performing simultaneous flip and merge operations to improve logical operation performance in high-efficiency superconducting circuits.

Traditional electronic materials typically exhibit electrical properties aligned in the same direction as the applied electric field. However, researchers at UC Berkeley have developed a new class of Aurivillius phase layered ferroelectric materials that enable unique "trans-capacitance" effects. These materials possess a coexistence of in-plane and out-of-plane polarization.

Developing robotic hands that can safely and effectively grasp a wide variety of objects remains a significant challenge, often requiring heavy motors and complex sensor arrays. Researchers at UC Berkeley have developed an underactuated dual-finger mechanism that features a unique force-triggered carpometacarpal (CMC) joint articulation. By utilizing underactuation—where a single motor drives multiple degrees of freedom—the design achieves high dexterity with minimal mechanical complexity. The CMC joint is engineered to respond passively to contact forces, allowing the fingers to wrap around objects of varying shapes and sizes automatically. This innovation enables a natural, compliant grip that mimics human hand mechanics, providing a lightweight and cost-effective solution for advanced manipulation.

Quantifying greenhouse gas emissions is a critical component of climate change research and environmental management. To facilitate long-term, high-frequency monitoring, UC Berkeley researchers have developed a fully autonomous methane flux chamber system. This continuously and remotely operable technology integrates a specialized methane sensor and an automated pump system within a flux chamber to measure gas exchange between the ground and the atmosphere. The system features a controller that manages evacuation and fresh air intake cycles based on real-time sensor data. Equipped with its own power source, data storage, and network connectivity, the device can operate in remote locations and transmit measurement data to external servers without the need for manual intervention.

Traditional imaging techniques often rely on bulky hardware or complex computational methods to resolve depth. UC Berkeley researchers have developed a three-dimensional imaging system that utilizes piezoelectric micromachined ultrasound transducers to capture high-resolution spatial data with an integrated approach that allows for compact, high-performance imaging that can be used in a variety of environments where traditional optical or radar systems might be limited.

Digital integrated circuit design has evolved significantly over the past several decades, with synthesis becoming increasingly automated and sophisticated. The traditional synthesis flow emerged in the 1980s when commercial logic synthesis packages from companies like Cadence and Synopsys revolutionized chip design by automatically converting hardware description languages (HDL) into gate-level netlists. Electronic design automation (EDA) tools evolved from simple netlist extraction to complex optimization processes, progressing through gate-level optimization, register-transfer-level synthesis, and eventually algorithmic synthesis. However, as designs have grown exponentially in complexity, synthesis times have become a major bottleneck, with full synthesis often taking hours or days for large designs, significantly impacting designer productivity and iteration cycles. Long synthesis runtimes prevent designers from rapid iteration, with typical synthesis taking 3+ days for complex designs, forcing designers to carefully consider when to submit jobs and wait for delayed feedback. The traditional register-transfer level (RTL) design flow suffers from critical limitations including the inability for RTL engineers to identify and resolve top-level timing issues early in the design process, routing congestion problems that cannot be detected until placement is completed, and insufficient feedback on power consumption during early architectural phases. Additionally, even small design changes trigger full re-synthesis of large blocks, wasting computational resources on unchanged portions of the design, while inter-module optimization requirements often degrade quality-of-results (QoR) when designs are artificially partitioned.

Traditional 3D printing methods rely on layer-by-layer deposition, which often limits speed and introduces structural weaknesses. Computed Axial Lithography (CAL) revolutionized the field by using projected light to cure entire volumes at once, but it was previously constrained by the size of the illumination field. UC Berkeley researchers have advanced this technology with a Helical Cone Beam CAL system. By combining a rotating target volume with a synchronized translation mechanism, the system projects patterned cone beams in a helical path through radiation-reactive material. This allows for continuous printing of much larger objects than traditional CAL and even enables "inner printing"—the fabrication of new structures inside or around existing solid objects.

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An innovative design automation methodology leveraging graph neural networks to enhance integrated circuit security by mitigating hardware threats and protecting intellectual property.

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      On-chip ultrafast light devices with a compact footprint and low cost would provide a practical platform for applications such as optical signal processing, molecular sensing, microwave generation and nonlinear optical processes. With the help of recent advances in nanofabrication techniques, the ability to reach low propagation loss on-chip has driven the development of high-quality (Q) factor microresonators. These microresonators allow for microcomb and pulse generation under intense continuous wave (CW) pumping. However, low nonlinear conversion efficiencies and high repetition rates, fixed by the resonator geometry, make achieving ultrashort pulses with high peak power remains an ongoing challenge.       To overcome these challenges, UC Berkeley researchers have demonstrated the integration of an electro-optic-comb system and dispersion-engineered nonlinear waveguides on a thin-film lithium niobate platform. The compact, on-chip device can achieve 35-fs pulse generation, corresponding to 6.7 cycles at 1550 nm, via higher-order soliton compression. The present invention facilitates development of ultrafast nano-optics and nano-electronics.

       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.

An innovative learning algorithm that enables efficient online training of spiking neural networks on memristive neuromorphic hardware.

Sensors are used in a multitude of applications from molecular biology, chemicals detection to wireless communications. Researchers at the University of California Irvine have invented a new type of electronic circuit that utilizes exceptional points of degeneracy to improve the sensitivity of signal detection.

      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.

Researchers at the University of California, Davis have developed a technology that introduces a novel approach to hardware security using photonic physically unclonable functions for true random number generation and biometric ID.

Researchers at the University of California, Davis have developed an artificial intelligence machine that uses a combination of electronic neuromorphic circuits and photonic neuromorphic circuits.

Researchers at the University of California, Davis have developed a large-scale, energy-efficient, high-throughput, and compact tensorized optical neural network (TONN) exploiting the tensor-train decomposition architecture on an integrated III–V-on-silicon metal–oxide–semiconductor capacitor (MOSCAP) platform.

Researchers at the University of California, Davis, have developed a memory system that uses optical interconnects.

Researchers at the University of California, Davis have developed a haptic interface designed to aid visually impaired individuals in navigating their environment using their portable electronic devices.

Researchers at the University of California, Davis have developed a technology that provides a novel method for decoding biosignals into speech, enhancing communication for individuals with speech impairments.