R&D: Slow Migration-Controlled Resistive Switching in Stable Dion–Jacobson Hybrid Perovskites for Flexible Memristive Applications

ACS Applied Electronic Materials has published an article written by Mansi Patel, Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India, and Flextronics Laboratory, Pandit Deendayal Energy University, Gandhinagar 382426, India, Jeny Gosai, Flextronics Laboratory, Pandit Deendayal Energy University, Gandhinagar 382426, India, and Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India, Aziz Lokhandwala, and Ankur Solanki, Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India, and Flextronics Laboratory, Pandit Deendayal Energy University, Gandhinagar 382426, India.

Abstract: “The limitations of Moore’s law and the von Neumann bottleneck have sparked an increasing interest in advanced intelligent systems, such as memristors and neuromorphic devices. This work unveils the role of slow ion migration for resistive switching (RS) and exceptional environmental and mechanical resilience achieved with butane-1,4-diammonium (BDA)-based BDAPbI₄ memristors, meticulously fabricated and measured in ambient conditions. These memristors demonstrate exceptional durability with consistent characteristics for up to 60 days and a slight decay in the ON/OFF ratio on the 140th day. Devices show the potential for flexible random-access memories with a low operating voltage of ∼100 mV and strong data retention and endurance measured up to 35 h and ∼1000 cycles, respectively. RS in these devices is attributed to energy barrier modulation at the perovskite/Ag interface and ion migration in the perovskite film. Furthermore, the initial investigations into their synaptic characteristics reveal stable learning behavior (potentiation and depression) and an invariant paired pulse facilitation (PPF), tested on flat and 5 mm bending radii. Additionally, the application of the spike time-dependent plasticity (STDP) Hebbian learning rule effectively demonstrates the feasibility of these memristors for neuromorphic computing applications. This is particularly promising for use in extreme mechanical conditions, such as electronic skins, and extends their potential beyond traditional data storage solutions.“