This paper describes logic and analog test schemes that improve the testability of a pixel-parallel fingerprint identification circuit. The pixel contains a processing circuit and a capacitive fingerprint sensor circuit. For the logic test, we propose a test method using a pseudo scan circuit to check the processing circuits of all pixels simultaneously. In the analog test, the sensor circuit employs dummy capacitance to mimic the state of a finger touching the chip. This enables an evaluation of the sensitivity of all sensor circuits on logical LSI tester without touching the chip with a finger. To check the effectiveness of the schemes, we applied them to a pixel array in a fingerprint identification LSI. The pseudo scan circuit achieved a 100% failure-detection rate for the processing circuit. The analog test determines that the sensitivities of the sensor circuit in all pixels are in the proper range. The results of the tests confirmed that the proposed schemes can completely detect defects in the circuits. Thus, the schemes will pave the way to logic and analog tests of chips integrating highly functional devices stacked on a LSI.

This paper presents fingerprint image enhancement and rotation schemes that improve the identification accuracy with the pixel-parallel processing of pixels. In the schemes, the range of the fingerprint sensor is adjusted to the finger state, the captured image is retouched to obtain the suitable image for identification, and the image is rotated to the correct angle on the pixel array. Sensor and pixel circuits that provide these operations were devised and a test chip was fabricated using 0.25-µm CMOS and the sensor process. It was confirmed in 150,000 identification tests that the schemes reduce the false rejection rate to 6.17% from 30.59%, when the false acceptance rate is 0.1%.

Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a “suddenness index (SI)” to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.