[paper] CrOx/TiOy Memristive Devices

Phu-Quan Pham1,2, Ngoc-Lam Le Pham3,4, Thuy-Anh Tran1,2, Van-Son Dang4, Quang Nguyen2,5, Ngoc Kim Pham1,2, Thuat Tran Nguyen3,4

On-Pinched Hysteresis in CrOx/TiOy-based Memristive Devices: Modeling and Analysis

Appl. Phys. Lett. 128, 153502 (2026)

DOI: 10.1063/5.0332014

1 Faculty of Materials Science and Technology, University of Science, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, 72754, Vietnam
2 Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, 71309, Vietnam
3 Semiconductor and Advanced Materials Institute, Technology and Innovation Park, Vietnam National University – Hanoi, Hoa Lac, Hanoi, 13151, Vietnam.
4 Faculty of Physics, University of Science, Vietnam National University – Hanoi, 334 Nguyen Trai, Thanh Xuan, Hanoi, 11406, Vietnam
5 Department of Physics, International University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, 71309, Vietnam

Abstract: Transition-metal oxide memristors are promising for neuromorphic computing, yet most SPICE models overlook material-specific effects such as oxygen stoichiometry and non-pinched hysteresis. Here, we systematically study CrOx/TiOy memristors fabricated under controlled oxygen concentrations (10%–50%) and propose an improved SPICE-compatible model. The devices exhibit oxygen-dependent resistive switching, retention, and pulse-driven plasticity, with optimal performance at 40% oxygen. Our model explicitly reproduces the non-pinched hysteresis observed in I–V curves, consistent with behaviors such as ion immigration, charge trapping, and remnant polarization, and achieves close agreement with experiments across multiple stoichiometries. Validation includes endurance, retention, and synaptic functions such as long-term potentiation/depression and spike-number/amplitude-dependent plasticity. Finally, the model is extended from single devices to a 4 × 4 crossbar array, demonstrating its scalability for artificial neural network simulations. These results emphasize the critical role of oxygen stoichiometry in CrOx/TiOy memristors and introduce a modeling framework that bridges experimental device physics with circuit-level neuromorphic applications.

Fig. a. Fabricated single cell memristor devic and b. 4×4 crossbar array

[paper] Microchips for Memristive Applications

Kaichen Zhu, Sebastian Pazos, Fernando Aguirre, Yaqing Shen, Yue Yuan, Wenwen Zheng, Osamah Alharbi, Marco A. Villena, Bin Fang, Xinyi Li, Alessandro Milozzi, Matteo Farronato, Miguel Muñoz-Rojo, Tao Wang, Ren Li, Hossein Fariborzi, Juan B. Roldan, Guenther Benstetter, Xixiang Zhang, Husam N. Alshareef, Tibor Grasser, Huaqiang Wu, Daniele Ielmini & Mario Lanza 

Hybrid 2D–CMOS microchips for memristive applications

Nature 618, 57–62 (2023)

DOI: 10.1038/s41586-023-05973-1

Abstract: Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2–Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2) (refs. 4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications.

FIG: Structure of the considered SNN. Each MNIST image is reshaped as a 784x1 column vector, and the intensity of the pixels is encoded in terms of the firing frequency of the input neurons. The only trainable synapses are those connecting the input layer with the excitatory layer, and they are modelled with the STDP characteristic of the CMOS-h-BN based 1T1M cells. The learning is unsupervised, and the neurons are labelled only after the training. These label-neuron assignments are then feed to the decision block altogether with the firing patterns of the neurons, to infer the class of the image presented in the input. 

Acknowledgements: This work has been supported by the Ministry of Science and Technology of China (grant nos. 2019YFE0124200 and 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.