Atsugi, Japan — Sony Semiconductor Solutions Corporation (“Sony”) today announced the upcoming release of two types of stacked event-based vision sensors. These sensors designed for industrial equipment are capable of detecting only subject changes, and achieve the industry’s smallest*1 pixel size of 4.86μm.
Event-based vision sensors asynchronously detect luminance changes for each pixel and output the changed data only, combining it with information on pixel position (xy coordinates) and time, thereby enabling high-speed, low latency data output.
The two new sensors employ stacking technology leveraging Sony’s proprietary Cu-Cu connection*2 to achieve the industry’s smallest*1 pixel size of 4.86μm. In addition to operating with low power consumption and delivering high-speed, low-latency, high-temporal-resolution data output, the new sensors also feature a surprisingly high resolution for their small size. All of these advantages combine to ensure immediate detection of moving subjects in diverse environments and situations.
These two sensors were made possible through a collaboration between Sony and Prophesee, by combining Sony's CMOS image sensor technology with Prophesee's unique event-based vision sensing technology. This enables high-speed, high-precision data acquisition and contributes to improve the productivity of the industrial equipment.
*1: Among stacked event-based vision sensors. According to Sony research (as of announcement on September 9, 2021).
*2: Technology that provides electrical continuity via connected Cu (copper) pads when stacking the pixel section (top chip) and logic circuits (bottom chip). Compared with through-silicon via (TSV) wiring, where the connection is achieved by electrodes intruded around the circumference of the pixel area, this method gives more freedom in design, improves productivity, allows for a more compact size, and increases performance.
Stacked event-based vision sensors
Left:IMX636 Right:IMX637
| Name | Sample ship date (planned) |
|---|---|
| IMX636 1/2.5-type (7.137mm diagonal) approx. 0.92-effective-megapixel*3 stacked event-based vision sensor |
October 2021 |
| IMX637 1/4.5-type (3.983mm diagonal) approx. 0.33-effective-megapixel*3 stacked event-based vision sensor |
October 2021 |
*3: Based on image sensor effective pixel specification method.
With the ever more sophisticated and diversified needs of the industrial equipment business,
the use of sensing to extract the necessary information from images captured by cameras continues to grow, demanding ever more efficient data acquisition.
With the frame-based method which is commonly used, the entire image is output at certain intervals determined by the frame rate. The new Sony’s sensors, however, utilize an event-based method that asynchronously detects pixel luminance changes and outputs data with pixel position (xy coordinates) and time information. They employ a proprietary stacked structure leveraging Cu-Cu connection to achieve conduction between the pixel chip and the logic chip, which is equipped with a signal processing circuit for detecting luminance changes, for each pixel. This design makes it possible that only the pixels that have detected a change in luminance for the object can output data, allowing the sensor to immediately detect the luminance changes with high-speed, low-latency, high-temporal-resolution while operating with low power consumption. Meanwhile, the design boasts the industry’s smallest*1 pixel size of 4.86μm, making for a very compact, high-resolution sensor.
These products can sense slight changes in vibration, detecting abnormalities for use in predictive maintenance of equipment. They also sense changes in sparks produced during welding and metal cutting, information which can be used to notify workers of the optimal time to change tools, among other possibilities. Likewise, they can contribute to improved production in various applications that frame-based image sensors have trouble with, as well as to support work processes that rely on human experience.
Imaging example 1: Vibration detection (left: frame-based image, right: event-based sensing)
Imaging example 2: Spark detection during metal cutting (left: frame-based image, right: event-based sensing)
