A sub-skin-depth (nanoscale metallization) thin film antenna is shown that is monolithically integrated with an array of neural recording electrodes on a flexible polymer substrate. The structure is intended for long-term biometric data and power transfer such as electrocorticographic neural recording in a wireless brain-machine interface system. The system includes a microfabricated thin-film electrode array and a loop antenna patterned in the same microfabrication process, on the same or on separate conductor layers designed to be bonded to an ultra-low power ASIC.

Self-driving cars, drones, robots and other autonomous systems rely on various sensors for obstacle detection and avoidance to navigate safely through environments. One of the most common methods to sense obstacles is light Detection and Ranging (LiDAR), which uses light in the form of a pulsed (or amplitude/frequency modulated CW) laser to measure variable distances. These light pulses—combined with other data recorded by the airborne system— generate precise, three-dimensional information about the shape of the surrounding environment and its surface characteristics. While LiDAR is a well established and utilized system within many mobility companies, it’s large size and high cost-per-unit has prevented its implementation in many commercial applications. Solid state LiDARs with non-mechanical scanning elements have received increasing interests. In particular, the optical phased array (OPA) provides non-mechanical scanning in a compact form factor. More importantly, at reduced size OPAs enable sophisticated beamforming such as simultaneous scanning, pointing, and tracking of multiple objects, or even direct line-of-sight communications. Unfortunately, at large-scale OPAs have been found to have slow response times, making their application for commercial use impossible. Researchers at the University of California, Berkeley, have designed an optical phased array with rapid response time. This novel technology utilizes arrays of micromirrors actuated by micro-electro-mechanical systems (MEMS). This novel OPA operates with a larger field of view, with a wide range of laser wavelengths, and without the need for high voltage electronics. It is also far more compact and sophisticated than bulky and intrusive mechanical LIDAR technologies.

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.  

Realizing the potential of massive sensor networks requires overcoming cost and power challenges. When sleep/wake strategies can adequately limit a network node's sensor and wireless power consumption, then the power limitation comes down to the real-time clock (RTC) that synchronizes sleep/wake cycles. With typical RTC battery consumption on the order of 1µW, a low-cost printed battery with perhaps 1J of energy would last about 11 days. However, if a clock could bleed only 10nW from this battery, then it would last 3 years. To attain such a clock, researchers at UC Berkeley developed a mechanical circuit that harnesses squegging to convert received RF energy (at -58dBm) into a local clock while consuming less than 17.5nW of local battery power. The Berkeley design dispenses with the conventional closed-loop positive feedback approach to realize an RCT (along with its associated power consumption) and removes the need for a sustaining amplifier altogether. 

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;} UC Berkeley researchers developed a software for displaying three-dimensional imagery of general scenes with nearly correct focus cues on multi-plane displays.  These displays present an additive combination of images at a discrete set of optical distances, allowing the viewer to focus at different distances in the simulated scene. The software uses an optimization algorithm to compute the images to be displayed on the presentation planes so that the retinal images, when accommodating to different distances, match the corresponding retinal images of the input scene as closely as possible.  The researchers demonstrated the utility of the technique using imagery acquired from both synthetic and real-world scenes, and analyzed the system’s characteristics including bounds on achievable resolution and found this software improves the practicality and realism of 3D displays by enabling realistic focus cues to be reproduced.  

Magnetic stripe-enabled card applications have been around for over two decades. The use of magnetic and other "chip" smart cards is widespread. for most high security applications, however, downsides remain with current offerings, including the risks of sensitive card information being copied or stolen. Alternatives suing biometrics typically require robust local storage via biometric parameters database or continuous communication with it. Moreover, the biometric parameters associated with such systems are constant and often cannot be modified. To address these problems, researchers at the University of California, Berkeley, have developed a novel approach to storing card information on person without a direct need for a smart cards or biometric recognition features like fingerprint, face, iris, voice, or palmprint.

This case helps reinvent the transistor by building on the success of Berkeley’s 3D FinFET/Trigate/Tri-Gate methods and devices, with increased focus on the negative capacitance of the MOS-channel and ferroelectrics, and an unconventional effective oxide thickness approach to the gate dielectric. Proof of concept devices have been demonstrated at 30nm gate length and allow for use of thinner ferroelectric films than 2D negative capacitance transistors (e.g. see http://digitalassets.lib.berkeley.edu/techreports/ucb/text/EECS-2014-226.pdf ). The devices also performed at low operating voltage which lowers operating power.

Wearable technologies can play a significant role in realizing personalized medicine through continuously monitoring an individual’s physical and physiological states. Most currently developed wearable technologies are capable only in tracking the physical activities of an individual and fail to provide insight into the individual’s state of health. Human sweat contains the physiologically rich information needed to infer an individual’s sate of health and is an excellent candidate for non-invasive monitoring. The wearable sweat sensors can serve as an ideal platform for a wide range of real-time healthcare monitoring such as exercise-induced dehydration and medical diagnosis.

Energy Isolation Lock out Tag out (“LOTO”) is a series of CalOSHA and FedOSHA code compliance requirements and is the primary means by which equipment must be rendered “safe” prior to allowing personnel to work on the equipment.  LOTO codes require equipment-specific written procedures identifying all types of energy sources needed to operate the equipment as well as the energy-isolation methods and locations of utility disconnects, stored energy, etc. In addition, every LOTO procedure must be annually verified to confirm the written procedure is still accurate to the equipment.   Whereas current LOTO procedures are typically hand-written or using other time-consuming processes, UC Berkeley authors have created software allowing users to retrieve LOTO procedures in real-time guiding the end-user through a logical thought process to allow them to identify all energy sources and safety processes, and equipment needed.  

The ability to see depth is a key visual function, as three-dimensional vision is used to guide body movements. Although many visual cues are used to infer spatial relationships, depth perception relies primarily on stereopsis, or the perception of depth based on differences in the images in the two eyes. More than 5% of the US population, however, is unable to see in three dimensions due to stereo-blindness and stereo-anomaly. Without depth perception, basic activities such as catching a ball or driving a car are not possible. Current therapeutic methods to address this issue include a set of eye-training exercises that aim to equalize the input from the eyes to the brain, which are collectively called orthoptics.   Researchers at UC Berkeley have developed an orthoptic method to train stereo depth perception. This method includes devices and systems for implementation, and it can be used in the home. 

Trillions of sensors are envisioned to achieve the potential benefits of the internet of things.  Realizing this potential requires wireless sensors with low power requirements such that there might never be a need to replace a sensor’s power supply (e.g. battery) over the lifetime of that device.  The battery lifetime of wireless communications devices is often governed by power consumption used for transmitting, and therefore transmit power amplifiers used in these devises are important to their commercial success.  The efficiencies of these power amplifiers are set by the capabilities of the semiconductor transistor devices that drive them.  To achieve improved efficiencies, researchers at UC Berkeley have developed a novel method and structure for realizing a zero-quiescent power trigger sensor and transceiver based on a micromechanical resonant switch.  This sensor/transceiver is unique in its use of a resonant switch (“resoswitch”) to receive an input, amplify it, and finally deliver power to a load.  This novel technology also greatly improves short-range communication applications, like Bluetooth.  For example, with this technology, interference between Bluetooth devices would be eliminated.  Also, Miracast would work, despite the presence of interfering Bluetooth signals.

The volume of interactions in social media and crowd-sourcing tools continues to significantly grow.  As the amount of data being shared increases, showcasing relevant information has become a significant challenge.  Many social networking sites use linear lists for online discussions and crowd-sourcing feedback. Unfortunately, these systems do not scale well.  One major problem with linear lists is the amount of data presented to an end-user can become overwhelming. As an example, if a particular news story generates thousands of responses, then this data is impractical to navigate using a linear list, biases users to whatever data is presented at the top, and impedes consideration of the diversity of responses.  To address this situation, researchers at the University of California, Berkeley have developed a novel method to interactively visualize data for an online environment. This system can be applied to responses that are in textual, numeric and multimedia formats. By using canonical correlation analysis and other techniques, researchers have been able to highlight the most relevant information for end-users and in turn, facilitate browsing, and rating of responses, as well as displaying informative patterns.

Each university, company or hospital that has a license to work with radioactive materials or is authorized to use x-ray machines is required to train its radioactive material or x-ray machine users. UC Berkeley has developed a radiation safety online training course made up of 7 training modules, which fulfill this training requirement. This safety course can be used by other organizations that are interested in fulfilling this requirement in an interactive and engaging way.   

Information and communication technologies rely on integrated circuits (ICs) or “chips.” Increased integration has improved system performance and energy efficiency, and lowered the manufacturing cost per component. Moore’s Law predicts that the number of transistors on an IC will double every two years, yet industry experts predict that we are reaching economic limits of traditional circuit patterning processes. Photolithographic patterning is best suited to print linear features that are evenly spaced. The smaller or more complex the shape, the more likely the printed pattern will be blurred and unusable. Although multiple-patterning techniques can be used to increase feature density on ICs, they bring a high additional cost to the process. This means that the most advanced ICs available today have a high density of features, but are restricted to having simple patterns and are increasingly expensive to produce. Without innovations in production techniques, Moore’s Law will reach its end in the near future.  To address this issue, researchers at UC Berkeley have developed a one-step method to increase feature density on chips. This method is capable of achieving arbitrarily small feature size, and self-aligns to pre-existing features on the surface formed by other techniques. 

Earthquake early warning (EEW) networks are prevalent in several earthquake prone nations. For example, the Japanese EEW network has provided seconds to minutes of warning across the country - saving lives and properties. These EEW networks make use of the ability of sensors near a rupture point to transmit information about the rupture faster than the propagation of the earthquake wave. This is analogous to how the observed delay between a lightening flash and the corresponding thunder clap increases with distance from the lightening location. Likewise, the time delay between the EEW warning and an earthquake shaking can increase with distance from the epicenter. In 2013 the State of California mandated the development of an EEW system. However, the State hasn't funded the full deployment of the systems, so it is only available as a beta system in selected areas for selected entities.To leverage this emerging EEW, researchers at UC Berkeley (who have access to this EEW beta system) have developed an EEW alarm that has similar characteristics to ubiquitous, consumer fire/smoke alarms. The distinguishing attributes of the Berkeley earthquake alarm include: a low cost of manufacturing; easy installation (i.e. it doesn't require a professional to install); the form-factor of a home fire alarm; wireless connection to the EEW network; low power (3V, 5V or power-over-internet); battery back-up; always-on operation; audible and visual alerts. The device could also potentially connect to other warning networks such as for tsunamis, tornadoes, chemical spills, radioactive fallout, civil unrest, air-raids, etc. For detailed information, go to: http://5nf5.blogspot.com/2014/09/early-warning-device-of-earthquakes-and-other-maladies-for-everyone.html

Almost 170 million people in the United States (~55% of the total U.S. population) wear vision correction. Of this population, more than 63 million people (53%) up to age 64 have presbyopic vision. Eyeglasses have been the primary tool to correct such aberrations since the 13th century. In more modern times, contact lenses and refractive surgery have become viable alternatives to wearing eyeglasses. Unfortunately, these approaches require the observer to either use eyewear or undergo surgery, which is often uncomfortable and costly, and can lead to complications, in the case of surgery. To address these challenges, researchers at the University of California, Berkeley, and MIT have developed vision correcting screen technology which involves digitally modifying the content of a display so that the display can be seen in sharp focus by the user without requiring the use of eyeglasses or contact lenses. By leveraging specialized optics in concert with proprietary prefiltering algorithms, the display architecture achieves significantly higher resolution and contrast than prior approaches to vision-correcting image display. The teams have successfully demonstrated light field displays at low cost backed by efficient 4D prefiltering algorithms, producing desirable vision-corrected imagery even for higher-order aberrations that are difficult to be corrected with conventional approaches like eyeglasses.

The tracking scanning laser ophthalmoscope (TSLO) provides fast and accurate measurements of fixational eye motion with flexible field of views. Currently, this system is the most accurate, fast and functional eye-tracking system used in a standard ophthalmic instrument. At a basic research level, the benefits of accurate eye-tracking are especially useful for delivering stimuli to targeted retinal locations as small as a single cone. In the clinical domain, advances in imaging and tracking technology help render accurate images which can lead to better outcomes in treating eye disease. Scanning laser ophthalmoscopy (SLO) uses both a horizontal and vertical scanner to image a specific region of the retina. Current state of the art tracking SLO systems are only suitable for observing a narrow field of view (FOV < five degrees) and will lose signal with certain types of eye motion. This is problematic for patients suffering from varying retinal or neurological disorders, where unstable fixation hinders accurate eye-tracking and image acquisition. These include retinal diseases of the macula such as: age-related macular degeneration, or neurological disorders such as: Alzheimer's and Parkinson's disease. In cases such as these, it would be desirable to capture a larger field of view whose image quality is sufficient to track the retina for larger and more rapid eye movements. To help address this problem, researchers at the University of California, Berkeley have developed systems, software, and methods for an image-based high-performance TSLO. Early laboratory experimentation results suggest significantly enhanced eye-tracking in terms of: sampling uniformity of eye motion traces, detection of eye rotation, increased frame rate of image capture, expandable/adjustable FOV, stabilization accuracy of 0.66 arcminutes, and tracking accuracy of 0.2 arcminutes or less across all frequencies. The Berkeley system and techniques show promise for observing detailed structural and functional changes in the eye as a result of age and/or disease like never before.

Supercapacitors are attractive energy storage devices due to their high-power capabilities and robust cycle lifetimes.   “Super” capacitors are named in part because the electrodes are composed of materials with high specific surface area and the distance between the electrode and electrochemical double layer is very small compared to standard capacitors.  A variety of porous silicon nanowires have been developed for use as supercapacitors electrodes by maximizing the specific surface area of active materials.  Although the use of Si is attractive due to its wide-spread adoption by microelectronics industry and due to its abundance, Si nanowires are highly reactive and dissolve rapidly when exposed to mild saline solutions.  Previously, silicon carbide thin films were used to protect the porous silicon nanowires, but the coatings were 10’s of nm thick and while they successfully mitigated Si degradation during electrochemical cycling in aqueous electrolytes, they also resulted in pore blockage and a large decrease in energy storage potential.   Researchers at UC Berkeley have developed methods and materials to improve porous silicon nanowires by overcoming the above limitations.  The resulting nanowires have an ultrathin carbon coating preserving the pore structure while mitigating Si degradation.  The resulting supercapacitor electrodes have the highest capacitance (and hence energy storage) per projected area to date.   

A minimally invasive, wireless ECoG microsystem is provided for chronic and stable neural recording. Wireless powering and readout are combined with a dual rectification power management circuitry to simultaneously power to and transmit a continuous stream of data from an implant with a micro ECoG array and an external reader. Area and power reduction techniques in the baseband and wireless subsystem result in over 10x IC area reduction with a simultaneous 3x improvement in power efficiency, enabling a minimally invasive platform for 64-channel recording. The low power consumption of the IC, together with the antenna integration strategy, enables remote powering at 3x below established safety limits, while the small size and flexibility of the implant minimizes the foreign body response.

The main difference between professional video and amateur video is camera motion.  Most camera motions in amateur video are shot with hand-held devices (e.g., cell phone, video camera, etc.), producing video that is difficult to watch because of the camera shake that causes visible frame-to-frame jitter in the recorded video.  Previous methods on video stabilization can be roughly divided between 2D and 3D stabilization.  2D stabilization is robust and fast, but cannot account for the parallax induced by 3D camera motion.  3D stabilization can achieve higher quality results, but 3D methods are more fragile, performing 3D reconstruction is error-prone and robust 3D reconstruction requires long feature tracks, which are difficult to obtain in amateur video.   Researchers at UC Berkeley have developed software that handles parallax and rolling shutter effects which not requiring long feature trajectories or sparse 3D reconstruction.  The software uses bundled camera paths for video stabilization.

Data storage outsourcing has become one of the most popular applications of cloud computing, offering benefits such as economies of scale, flexible accessibility, efficiency, and allowing companies to focus on their primary business activities. Due to the increase in percentage of services conducted online and number of mobile internet connections, demand for data storage continues to grow. Customers in this industry are primarily concerned with authenticated storage and data retrievability. Although many efficient proof of retrievability technologies have been developed for static data, only two dynamic technologies exist. However, both are too expensive to implement in practice due to the fact that they require a high level of bandwidth. To address this problem, researchers have developed a dynamic proof of reliability scheme that requires 300 times less bandwidth than currently available technologies. This innovative technology makes dynamic proof retrievability of data practical and efficient, and thus attractive for the industry implementation. This technology gives clients of cloud storage providers assurance that their data has not been modified and that no data loss has occurred.

There is a long-standing problem of how to switch piezoelectric filters when used in switchable filter banks -- such as needed in RF channel-selection. To address this problem, researchers at UC Berkeley have developed a method and structure for a piezoelectric resonator with tunable transfer function -- i.e. tunable gain. This Berkeley resonator's gain is tunable to many values -- including values that are low enough to consider the device to be "off" relative to the background signal. Accordingly, this approach enables on/off switching of piezoelectric resonators; and it thereby obviates the need for separate low loss switches, which otherwise would be needed in series with piezoelectric resonators to switch them on and off -- adding insertion loss and raising system gain. In addition, this ability to adjust filter gain makes it possible for the resonator to control low power gain in a receiver front-end.

The reduction of power supply voltage with each new generation of CMOS technology continues to complicate the design of charge pumps needed for high voltage applications that integrate into systems alongside transistor chips -- such as the increasing number of MEMS-based gyroscopes, timing oscillators, and gas sensors. Moreover, the aggressive scaling in CMOS resulting in lower dielectric and junction breakdown voltages has compelled the use of customized CMOS processes -- including increased gate oxide thickness and/or added deep-n-wells. Clearly, advances in transistor technology are moving in the opposite direction of the needs of high voltage MEMS applications. To address this trend, researchers at UC Berkeley have developed a MEMS-based charge pump. This design avoids the turn-on voltage and breakdown limitation of CMOS. With much higher breakdown voltages than transistor counterparts, the demonstrated MEMS charge pump implementation should eventually allow voltages higher than 50V desired for capacitive-gap transduced resonators that currently dominate the commercial MEMS-based timing market.

A core problem in computer vision and graphics applications is recovering a model of the attributes that create an image. This model is comprised of the intrinsic scene properties: the collection of shapes, paints and lights which together create an image. Conventional methods for recovering intrinsic scene properties rely on multiple observations of the same scene in order to over-constrain the problem. By comparison, recovering these properties from a single image is vastly more difficult. To address this challenge, researchers at UC Berkeley have developed algorithms that infer the most likely intrinsic scene properties of a single image. Furthermore, the researchers have developed corresponding software that can render the scene with adjusted properties such as a different viewpoint, paints, lights, and shapes. 

Systems and methods for biosignal acquisition, and in particular, electrocorticography signal acquisition, are disclosed for small area, low noise recording and digitization of brain signals from electrode arrays.