This paper presents a large-area image sensing and detection system that integrates, on glass, sensors and thin-film transistor (TFT) circuits for classifying images from sensor data. Large-area electronics (LAE) enables the formation of millions of sensors spanning physically large areas; however, to perform processing functions, thousands of sensor signals must be interfaced to CMOS ICs, posing a critical limitation to system scalability. This work presents an approach whereby image detection of shapes is performed using simple circuits in the LAE domain based on amorphous silicon (a-Si) TFTs. This reduces the interfaces to the CMOS domain. The limited computational capability of TFT circuits as well as high variability and high density of process defects affecting TFTs and sensors is overcome using a machine-learning algorithm known as error-adaptive classifier boosting (EACB) to form embedded weak classifiers. Through EACB, we show that high-dimensional sensor data from a-Si photoconductors can be reduced to a small number of weak-classifier decisions, which can then be combined in CMOS to achieve strongclassifier performance. For demonstration, a system classifying five shapes achieves performance of >85%/>95% [true-positive (tp)/true-negative (tn) rates] [near the level of an ideal softwareimplemented support vector machine (SVM) classifier], while the total number of signals from 36 sensors in the LAE domain is reduced by 3.5-9×.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Amorphous silicon (a-Si)
- Image detection
- Machine learning
- Thin film
- Thin-film transistor (TFT)