ABSTRACT
Printed electronics promises to drive the future data-intensive technologies, with its potential to fabricate novel devices over a large area with low cost on nontraditional substrates. In these emerging technologies, there exists a large digital information flow, which requires secure communication and authentication. Physical unclonable functions (PUFs) offer a promising built-in hardware-security system comparable to biometrical data, which can be constructed by device-specific intrinsic variations in the additive manufacturing process of active devices. However, printed PUFs typically exploit the inherent variation in layer thickness and roughness of active devices. The current in devices with enough significant changes to increase the robustness to external environment noise is still a challenge. Here, printable epsilon-type-structure indium tin oxide transistor arrays are demonstrated to construct high-reliability PUFs by modifying the coffee-ring structure. The epsilon-type structure improves the printing scalability, film quality, and device reliability. Furthermore, the print-induced uncertainty along the channel thickness and length can lead to changes in the carrier concentration. Notably, the randomly distributed printing droplets in a small area significantly increase this uncertainty. As a result, the PUFs exhibit near-ideal uniformity, uniqueness, randomness, and reliability. Additionally, the PUFs are resilient against machine-learning-based attacks with a prediction accuracy of only 55% without postprocessing.
ABSTRACT
The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium-gallium-zinc-oxide thin film phototransistors exhibit the optoelectronic switching and light-tunable synaptic characteristics for in-sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in-sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in-sensor computing. By integrating these two systems, a neuromorphic system for high-efficiency in-sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human-computer interactions and the Internet of Things.
