An innovative system designed to enhance manufacturing efficiency through predictive maintenance using machine learning.

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A fundamental scientific breakthrough is poised to fundamentally reshape the nature of light-matter interactions, particularly in silicon, resulting in dramatic enhancement of optical absorption and emission and potentially revolutionizing its applications in various fields.

Volatile organic compounds (VOCs) are released by various products and during various processes. Ethanol is one such VOC that is released as an important byproduct of alcoholic fermentation. Ethanol emitted during fermentation can be estimated using the amount of liquid lost during storage. The instrumentation needed to accurately quantify ethanol emissions is specialized and costly. Researchers at UC Santa Cruz have developed low-cost VOC sensors that are useful for the wine industry, among others.

 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. 

Certain difficult optimization problems, such as the traveling salesman problem, can be solved using so-called analog Ising machines, in which electronic components (such as certain arrangements of diodes or electronic switches) implement an analog of a well-studied physical system known as an Ising machine. The problem is recast so that its solution can be read off from the lowest-energy configuration of the analog Ising machine, a state which the system will naturally evolve towards. While promising, this methodology suffers major drawbacks. Firstly, the number of subunits, known as “spins”, in the analog Ising machines, as well as the number of connections between these subunits, can grow substantially with problem size. Secondly, existing implementations of this principle rely on chip constructions which are optimized for one or a few problems, and are not sufficiently reprogrammable to be repurposed efficiently for other applications. To address these problems, researchers at UC Berkeley have developed a device known as a Field-programmable Ising machine which can be adapted to implement an analog Ising machine using a variety of hardware designs, such as the diodes and switches mentioned above. These Ising machines can be effectively reprogrammed to efficiently solve a wide array of problems across various domains. The inventors have shown that this design can be applied to SAT (“Satisfiability”) problems, a class known to be similar to the traveling salesman problem, in that the number of spins needed and their level of connectivity do not grow too quickly with problem size.

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Modern Graphical Processing Units (GPUs) consist of several Streaming Multiprocessors (SM) – each has its own Register File (RF) and a number of integers, floating points and specialized computational cores. GPU program is decomposed into one or more cooperative thread arrays that are scheduled to the SMs. GPUs invest in large RFs to enable fine-grained and fast switching between executing groups of threads. This results in RFs being the most power hungry components of the GPU. The RF organization substantially affects the overall performance and energy efficiency of the GPU.

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UCLA researchers in the Department of Electrical and Computer Engineering have developed a terahertz (THz) detector that utilizes the micro-Doppler effect to detect vibrations and long-distance sounds.

Electromagnetic noise from surfaces is one of the limiting factors for the performance of solid state and trapped ion quantum information processing architectures. This noise introduces gate errors and reduces the coherence time of the systems. Accordingly, there is great commercial interest in reducing the electromagnetic noise generated at the surface of these systems.Surface treatment using ion bombardment has shown to reduce electromagnetic surface noise by two orders of magnitude. In this procedure ions usually from noble gasses are accelerated towards the surface with energies of 300eV to 2keV. Until recently, commercial ion guns have been repurposed for surface cleaning. While these guns can supply the ion flux and energy required to prepare the surface with the desired quality, they are bulky and limit the laser access, making them incompatible with the requirements for ion trap quantum computing.To address this limitation, UC Berkeley researchers have developed an ion gun that enables in-situ surface treatment without sacrificing high optical access, enabling in situ use with a quantum information processor.

A new structure for III-N transistors that is able to maintain a high breakdown and operating voltage while improving the gain of the device.

Researchers led by Dr. Potkonjak from the UCLA Department of Computer Science have developed a technique to detect hardware Trojans in integrated circuits.

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

   Researchers at UCI have, for the first time, developed a method for modeling the efficiencies of artificial photosynthetic devices containing multiple light absorbers. As these devices more closely parallel naturally occurring photosynthesis, they offer higher performance than standard single-absorber devices.

A new approach to photonic integrated circuit fabrication.

UCLA researchers in the Department of Electrical Engineering have developed read-disturbance-free content addressable memory (CAM) using voltage controlled magneto-electric tunnel junctions (MEJs).

Researchers led by Jason Cong from the Computer Science Department at UCLA have developed a general synthesis and mapping system for SRAM-based FPGAs.

UCLA researchers in the Department of Materials Science and Engineering have developed a novel piezoelectric thin film that can control magnetic properties of individual magnetic islands.

Researchers led by Yong Chen from the Department of Mechanical and Aerospace Engineering have developed an artificial synapse for neuromorphic chips that have integrated logic, memory, and learning capabilities.

UCLA researchers in the department of Electrical Engineering have developed a novel magetoelectric device for use as a spin transistor.

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A method of replacing standard electrical injection of the quantum wells in semiconductor devices with optically-pumping, by coupling a short-wavelength electrically pumped active region to a long-wavelength optically pumped region via a tunnel junction.

A low-cost, highly scalable approach to integrating a compound-semiconductor laser or light source with silicon-photonic circuitry.