GaN-Based Dilute Magnetic Semiconductors for Room Temperature Neuromorphic and Quantum Computing

Abstract

Neuromorphic computing and quantum computing are attracting more research attention in recent years. Neuromorphic computing mimic the quantum decoherence for neuron firing and the microtubule processes, and artificially recreating the highly parallel computing architecture of the mammalian brain. Quantum computing is also of great interest for next generation of micro- and nano-electronic devices with enhanced functionalities, which involves the mathematical analysis, algorithmic manipulation, storage, and transmission of the fundamental unit of quantum information, the qubit. However, most of the current quantum computing and neuromorphic computing systems operate at cryogenic temperatures to avoid thermal activation, which limits their wide application. Spintronic devices, exploiting electron spin as a further degree of freedom in addition to the electronic charge, have shown to be promising for quantum information processing and modeling brain-like systems, realizing device-level components for quantum computing and neuromorphic computing applications. In addition, wide bandgap dilute magnetic semiconductors (DMS) have the potential for room temperature (RT) spintronic applications. Gallium nitride (GaN) based DMS are promising materials for spintronic applications due to their theoretically predicted and experimentally observed ferromagnetic properties at RT [1]. In this work, MOCVD-grown Gd-doped GaN showed ferromagnetic hysteresis in vibrating sample magnetometry measurement and Anomalous Hall Effect (AHE) measurement at room temperature. AHE measurement of samples with different carrier densities showed a superlinear relation between anomalous Hall conductivity σAHE and lateral conductivity σxx, σAHE∝σxx1.78 , which indicates the mechanism for the ferromagnetism is intrinsic and likely mediated by free carriers [2], which is conducive for spintronic applications. However, the ferromagnetism is only observed in GaGdN grown using a (TMHD)3Gd precursor, which contains oxygen in its organic ligand that appears to be incorporated into the GaGdN. The role of oxygen in the ferromagnetic properties was predicted in density functional theory calculations, which show that elements such as oxygen and carbon, when incorporated into GaGdN could result in p-d hybridization of the p-orbitals of oxygen or carbon with the d-orbitals of Gd; oxygen or carbon could introduce deep localized states close to the Fermi level in GaGdN that couple with Gd states to render ferromagnetism [3,4]. In order to experimentally examine the role of oxygen or carbon in the ferromagnetic properties of GaGdN, oxygen and carbon are implanted in GaGdN originally grown using a Cp3Gd precursor that does not contain oxygen. As-grown GaGdN from Cp3Gd source, which does not contain O, is not ferromagnetic but post-implantation with O or C does result in ferromagnetism. X-ray diffraction of the implanted GaGdN samples exhibits good crystal quality, and peak shifts as compared to the GaGdN before implantation showing signs of O or C incorporation. Annealing the implanted GaGdN activates the dopant, improves the crystal quality, and shows clear signs of AHE. These results show that oxygen or carbon could have a significant role in rendering the intrinsic and potentially free carrier-mediated ferromagnetism in GaGdN at RT. A better understanding of the mechanism for RT ferromagnetism will enable these DMS materials to build spintronic devices, such as memristive, spin-transfer torque, and non-volatile memory spintronic devices have tremendous applications in neuromorphic computing and quantum computing applications. References Kane, S. Gupta and I. Ferguson, “Transition metal and rare earth doping in GaN”, Woodhead publishing, (2016).Saravade, C. Ferguson, A. Ghods, C. Zhou, and I. Ferguson, MRS Adv. 3 (3), p. 159, (2018).Liu, X. Yi, J. Wang, J. Kang, A. Melton, Y. Shi, N. Lu, J. Wang, J. Li, and I. Ferguson, Appl. Phys. Lett. 100 (23), 232408, (2012).Xie, H. Xing, Y. Zeng, Y. Liang, Y. Huang and X. Chen, AIP Advances 7, 115003, (2017).