A hardwired machine learning processing engine fabricated with submicron metal-oxide thin-film transistors on a flexible substrate

Flexible electronics can create lightweight, conformable components that could be integrated into smart systems for applications in healthcare, wearable devices and the Internet of Things. Such integrated smart systems will require a flexible processing engine to address their computational needs. However, the flexible processors demonstrated so far are typically fabricated using low-temperature poly-silicon thin-film transistor (TFT) technology, which has a high manufacturing cost, and the processors that have been created with low-cost metal-oxide TFT technology have limited computational capabilities. Here, we report a processing engine that is fabricated with a commercial 0.8-μm metal-oxide TFT technology. We develop a resource-efficient machine learning algorithm (the ‘univariate Bayes feature voting classifier’) and demonstrate its implementation with hardwired parameters as a flexible processing engine for an odour recognition application. Our flexible processing engine contains around 1,000 logic gates and has a gate density per area that is 20–45 times higher than other digital integrated circuits built with metal-oxide TFTs. Using commercial 0.8-μm metal-oxide thin-film transistor technology, a flexible processor chip can be built that has hardwired parameters for machine learning and is capable of smart applications such as odour recognition.