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.

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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.

      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.

As the density and performance of electronics continues to increase, thermal challenges have become a primary concern. Removing heat from electronic components can be extremely challenging, given their small size, electrical activity, and mechanical constraints. This necessitates the design of cooling solutions for a wide variety of electronic designs in applications such as datacenters, renewables, aircraft, etc. To address this problem, researchers at UC Berkeley have developed a thermal test vehicle (TTV) for characterizing the performance of electronics cooling solutions under a wide variety of operating conditions. All of the TTV circuitry required to perform measurements and temperature estimations can be included on one printed circuit board (PCB). This represents a simple, highly flexible approach for thermal test vehicle design. The overall size of the array can be scaled to any desired amount. This novel TTV represents a simple, highly flexible approach for thermal test vehicle design. Furthermore, its use of standard commercial electronic components allows for a vast reduction in cost compared to existing commercial solutions.

      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.

The use of telemedicine/telehealth increased substantially during the COVID-19 pandemic, leading to its accelerated development, utilization and acceptability. Telehealth momentum with patients, providers, and other stakeholders will likely continue, which will further promote its safe and evidence-based use. Improved healthcare by telehealth has also extended to musculoskeletal care. In a recent study looking at implementation of telehealth physical therapy in response to COVID-19, almost 95% of participants felt satisfied with the outcome they received from the telehealth physical therapy (PT) services, and over 90% expressed willingness to attend another telehealth session. While telehealth has enhanced accessibility by virtual patient visits, certain physical rehabilitation largely depends on physical facility and tools for evaluation and therapy. For example, limb kinematics in PT with respect to the shoulder joint is difficult to evaluate remotely, because the structure of the shoulder allows for tri-planar movement that cannot be estimated by simple single plane joint models. With the emergence of gaming technologies, such as videogames and virtual reality (VR), comes new potential tools for virtual-based physical rehabilitation protocols. Some research has shown digital game environments, and associated peripherals like immersive VR (iVR) headsets, can provide a powerful medium and motivator for physical exercise. And while low-cost motion tracking systems exist to match user movement in the real world to that in the virtual environment, challenges remain in bridging traditional PT tooling and telehealth-friendly physical rehabilitation.

Researchers at the University of California, Davis have developed an autonomous crop transportation robot to aid field workers during harvest.

Prof. Sheldon Tan and his team have developed a new hierarchical learning-based electro-migration analysis method called HierPINN-EM to solve for multi-segment interconnects in VLSI chips. HierPINN-EM provides much better accuracy, faster training speeds and faster inference speeds compared to current state-of-the-art techniques.