Multi-scale Investigation of the Nitride perovskite LaWN3: towards a functional integration in thin films

Mind-LWN是一个研究项目，它将为控制极性氮化物钙钛矿铺平道路，为它们在不损失其固有功能的情况下集成到半导体器件中提供设计策略。氮化镧(LWN)是新兴的氮化物钙钛矿材料中的一种极化材料。这类新材料对于开发可集成在微电子设备中并能够在较低电压下工作的无氧功能材料具有极大的兴趣。然而，人们对它们在薄膜中的稳定性知之甚少。理论上预测LWN是铁电性的，因此能够显示稳定有序的电偶极子，可以通过电场控制，因此在电子存储方面有很好的应用前景。这种材料最近已经在薄膜中合成，但并没有实现它的承诺：实验验证没有铁电性，其化学计量比测量显示存在大量的氮空位，可能干扰其功能。尽管温度、缺陷、应变和静电等控制参数对电偶极子有关键影响，但它们在氮化物钙钛矿背景下的作用尚未揭示。因此，我建议使用先进的密度泛函理论和机器学习技术对LWN薄膜的原子结构、电子结构和极化特性进行深入的理论研究。具体地说，我将研究厚度、缺陷浓度和温度之间的相互作用，以了解和控制它们对铁电稳定性的影响。该项目将为半导体行业可能的突破奠定基础，允许将数十年来在钙钛矿材料方面的发现与半导体的密集工业发展结合在一起。Mind-LWN将允许未来设计更高效的设备，这将产生直接的技术和经济后果。

MINd-LWN is a research project that will pave the way to controlling polar nitride perovskites by providing design strategies for theirintegration into semiconductor devices without the loss of their intrinsic functionalities. Lanthanum tungsten nitride (LWN) is a polarmaterial in the emerging class of nitride perovskites. This new class of materials is of great interest for developing oxygen-freefunctional materials, integrable in microelectronic devices and able to operate at a lower voltage. However, little is known about theirstability in thin films. LWN is theoretically predicted to be ferroelectric and thus able to display stable and ordered electric dipoles,controllable through an electric field; therefore promising for memory applications in electronics. This material has recently beensynthesised in thin films but did not hold its promises: no ferroelectricity was experimentally verified, and measurements of itsstoichiometry revealed the presence of a significant concentration of nitrogen vacancies, likely interfering with its functionalities.While control parameters such as temperature, defects, strain, and electrostatics have a crucial effect on electric dipoles, their role inthe context of nitride perovskites is unrevealed. Therefore, I propose a thorough theoretical study of the atomistic and electronicstructures and the polarisation properties of LWN thin films using advanced density functional theory and machine learningtechniques. Specifically, I will study the interplay between thickness, defect concentration, and temperature to understand andcontrol their influence on the ferroelectric stability. This project will set the scene for possible breakthroughs in the semiconductorindustry by allowing to merge decades of discoveries in perovskite materials with the intensive industrial development ofsemiconductors. MINd-LWN will allow future the design of more efficient devices, which will have direct technological and economicconsequences.