industry news
Subscribe Now

UltraSense Systems Brings Neural Processing to Smart Surfaces with a New Generation of Multi-mode Sensing Solutions

Distributed touch sensing architecture allows for up to 8 buttons to be easily and cost-effectively integrated under any surface for automotive, consumer/industrial IoT and home appliances

San Jose, Calif., December 14, 2021 – UltraSense Systems today announced its next generation of multi-mode touch sensing technology with the release of TouchPoint Edge to revolutionize and cost-effectively replace a cluster of mechanical buttons under any surface material (e.g., metal, glass, plastic, etc.) and further forge the UI/UX paradigm shift from mechanical to digital interfaces for smart surfaces. 

TouchPoint Edge is a fully integrated system-on-a-chip (SoC) to replicate the touch input of mechanical buttons by directly sensing up to eight standalone UltraSense ultrasound + force sensing TouchPoint P transducers. TouchPoint Edge also uses an embedded, always-on Neural Touch Engine (NTE) to discern intended touches from possible unintended false touches, eliminating corner cases and providing input accuracy of a mechanical button. 

Smart surfaces will further change the way we interact with products in the coming years. The definition of smart surfaces is a solid surface with underside illumination to show the user where to touch. The UI/UX paradigm shift first started with the smartphone over a decade ago with the removal of the mechanical keyboard to simply tapping the keyboard on a capacitive screen. Not all smart surfaces will be a capacitive display though. UltraSense System’s first-generation sensor, TouchPoint Z, continued the paradigm shift by cost-effectively removing mechanical buttons and improving the user experience (UX) in smartphones, electric toothbrushes, home appliances and automotive interior overhead lights available starting in early 2022. 

TouchPoint Edge takes the experience to the next level in applications that use many mechanical buttons. For instance, the automobile cockpit has many uses cases including removing mechanical buttons in the steering wheel, center and overhead console controls for HVAC and lighting, door panels for seating and window controls and even embedded into soft surfaces like leather or even in foam seating to create new user interfaces where mechanical buttons could not be implemented before. Other applications include appliance touch panels, smart locks, security access control panels, elevator button panels and a multitude of other applications. 

“In just three years from first funding, we were able to develop, qualify and ship to OEMs and ODMs a fully integrated virtual button solution for smart surfaces,” said Mo Maghsoudnia, CEO of UltraSense Systems. “We are the only multi-mode sensor solution for smart surfaces, designed from the ground up to put neural touch processing into everything from battery-powered devices to consumer/industrial IoT products and now automotive in a big way.”

Human machine interfaces are highly subjective and are extremely complex under a solid surface to replicate a mechanical button press. It is more than an applied force being larger than a threshold to trigger a press of a surface. When a user applies force to a mechanical button, the user is applying a time-varying force curve where the mechanical button reacts with a lot of non-linearity due to friction, hysteresis, air gaps and spring properties to name a few. As a result, a simple piezoresistive or MEMS force-touch strain sensor with some algorithms and one or two levels of triggering threshold cannot effectively and accurately recreate the user experience of a mechanical button and eliminate false triggers. 

TouchPoint Edge, with multi-mode sensing and embedded Neural Touch Engine, processes on-chip machine learning and neural network algorithms, so the user intention can be learned. As with TouchPoint Z, TouchPoint Edge captures the unique pattern of the user’s press with respect to the surface material. The data set is then used to train the neural network to learn and discern the user’s press pattern, unlike traditional algorithms which accept a single force threshold. Once TouchPoint Edge is trained and optimized to a user’s press pattern, the most natural response of a button press can be recognized. Additionally, the unique sensor array design of the TouchPoint P transducer allows for the capture of unique, multi-channel data sets within a small, localized area, as a mechanical button would be located, which greatly improves the performance of the neural network to replicate a button press. The Neural Touch Engine greatly improves the user experience and is even better enhanced by being tightly coupled with the proprietary sensor design of TouchPoint P to provide optimal performance. Finally, having the Neural Touch Engine integrated into TouchPoint Edge, is a game changer in system efficiency where neural processing can be performed 27X faster with 80% less power from offloading the same system setup to an external ultra-low-power microcontroller.

“The challenges of replacing traditional mechanical buttons with sensor-based solutions requires technologies such as illumination of the solid surface, ultrasound or capacitive sensing, and force sensing,” said Nina Turner, research manager of IDC. “But those sensors alone can lead to false positives. The integration of machine learning integrated with these touch sensors brings a new level of intelligence to the touch sensor market and would be beneficial in a wide array of devices and markets.”  

Key Features of TouchPoint Edge

  • Neural Touch Engine for processing Machine Learning and Convolutional Neural Net
  • Open interface allows for non-proprietary and even non-touch sensors inputs (e.g., inertial, piezo, position, force, etc.) to be processed by the Neural Touch Engine 
  • Direct drive and sense of eight multi-mode TouchPoint P standalone transducers
  • Embedded MCU and ALU for algorithm processing and sensor post processing
  • Integrated analog front end (AFE)
  • Configurable power management and frame rate
  • I2C and UART serial interface
  • Two GPIO for direct connect to haptic, LED, PMIC, etc.
  • 3.5mm x 3.5mm x 0.49mm WLCSP package size

Key Features of TouchPoint P

  • Multi-mode standalone piezo transducer for ultrasound + strain sensing
  • 2.6mm x 1.4mm x 0.49mm QFN package

TouchPoint Edge evaluation kits using TouchPoint P transducers will be sampling to select customers next month with production samples available in Q1 2022.

About UltraSense Systems

Founded in 2018 and headquartered in San Jose, Calif., UltraSense Systems is changing the UI/UX paradigm in smartphones, consumer/industrial IoT, home appliances and automotive by creating multi-mode touch sensing solutions to enable smart surfaces with precise, highly localized, buttonless interfaces. Its TouchPoint product line enables customers to deliver seamless touch HMIs on hard and soft surfaces, including metals, glass, plastics, wood and leather. The company has raised over to $24M to date from investors including Robert Bosch Ventures, Artiman Ventures, Abies Ventures, Sony Innovation Fund and Asahi Kasei Corporation.

Leave a Reply

featured blogs
May 19, 2022
The current challenge in custom/mixed-signal design is to have a fast and silicon-accurate methodology. In this blog series, we are exploring the Custom IC Design Flow and Methodology stages. This... ...
May 19, 2022
Learn about the AI chip design breakthroughs and case studies discussed at SNUG Silicon Valley 2022, including autonomous PPA optimization using DSO.ai. The post Key Highlights from SNUG 2022: AI Is Fast Forwarding Chip Design appeared first on From Silicon To Software....
May 12, 2022
By Shelly Stalnaker Every year, the editors of Elektronik in Germany compile a list of the most interesting and innovative… ...
Apr 29, 2022
What do you do if someone starts waving furiously at you, seemingly delighted to see you, but you fear they are being overenthusiastic?...

featured video

Synopsys PPA(V) Voltage Optimization

Sponsored by Synopsys

Performance-per-watt has emerged as one of the highest priorities in design quality, leading to a shift in technology focus and design power optimization methodologies. Variable operating voltage possess high potential in optimizing performance-per-watt results but requires a signoff accurate and efficient methodology to explore. Synopsys Fusion Design Platform™, uniquely built on a singular RTL-to-GDSII data model, delivers a full-flow voltage optimization and closure methodology to achieve the best performance-per-watt results for the most demanding semiconductor segments.

Learn More

featured paper

5 common Hall-effect sensor myths

Sponsored by Texas Instruments

Hall-effect sensors can be used in a variety of automotive and industrial systems. Higher system performance requirements created the need for improved accuracy and more integration – extending the use of Hall-effect sensors. Read this article to learn about common Hall-effect sensor misconceptions and see how these sensors can be used in real-world applications.

Click to read more

featured chalk talk

Industrial CbM Solutions from Sensing to Actionable Insight

Sponsored by Mouser Electronics and Analog Devices

Condition based monitoring (CBM) has been a valuable tool for industrial applications for years but until now, the adoption of this kind of technology has not been very widespread. In this episode of Chalk Talk, Amelia Dalton chats with Maurice O’Brien from Analog Devices about how CBM can now be utilized across a wider variety of industrial applications and how Analog Device’s portfolio of CBM solutions can help you avoid unplanned downtime in your next industrial design.

Click here for more information about Analog Devices Inc. Condition-Based Monitoring (CBM)