Inkjet printing electronics is a growing market that reached 7.8 billion USD in 2020 and that is expected to grow to ∼23 billion USD by 2026, driven by applications like displays, photovoltaics, lighting, and radiofrequency identification. Incorporating two-dimensional (2D) materials into this technology could further enhance the properties of the existing devices and/or circuits, as well as enable the development of new concept applications. Along these lines, here we report an easy and cheap process to synthesize inks made of multilayer hexagonal boron nitride (h-BN)—an insulating 2D layered material—by the liquid-phase exfoliation method and use them to fabricate memristors. The devices exhibit multiple stochastic phenomena that are very attractive for use as entropy sources in electronic circuits for data encryption (physical unclonable functions [PUFs], true random number generators [TRNGs]), such as: (i) a very disperse initial resistance and dielectric breakdown voltage, (ii) volatile unipolar and non-volatile bipolar resistive switching (RS) with a high cycle-to-cycle variability of the state resistances, and (iii) random telegraph noise (RTN) current fluctuations. The clue for the observation of these stochastic phenomena resides on the unpredictable nature of the device structure derived from the inkjet printing process (i.e., thickness fluctuations, random flake orientations), which allows fabricating electronic devices with different electronic properties. The easy-to-make and cheap memristors here developed are ideal to encrypt the information produced by multiple types of objects and/or products, and the versatility of the inkjet printing method, which allows effortless deposition on any substrate, makes our devices especially attractive for flexible and wearable devices within the internet-of-things.
KAUST Repository Item: Exported on 2023-05-29
Acknowledgements: This work was supported by the National Key R&D Program of China (grant no. 2019YFE0124200, 2018YFE0100800), the National Natural Science Foundation of China (grant no. 61874075), and the Baseline funding scheme of the King Abdullah University of Science and Technology (KAUST). The authors also wish to acknowledge the financial support from MINECO (Spain) for Grant PID2019-105658RB-I00 (PRITES Project) and TED2021-129643B-I00 (FLEXRAM Project).