Electron Microscopy and Modeling of Resistive Switching Devices Based on TaOx

非技术描述：人工智能(AI)对信息处理提出了特定的要求，随着应用程序的增加，它们需要专门为任务设计的专用硬件。目前基于晶体管的数字电路不太适合人工智能和寻找具有新功能的新材料，启用新的器件原理对于该领域的进步至关重要。该项目旨在为特定类型的器件带来在工作(循环)条件下材料特性演变的高级知识，即基于钽氧化物的开关，该开关可以根据施加的偏压在高和低值之间反复改变阻值。这种结构的应用包括非易失性固态(没有移动部件)记忆和模仿人脑功能的人工突触。这项研究强调对材料特性和控制这种器件的电阻的过程的了解。并行的目标是为电子行业的劳动力培养下一代工程师。该计划的组成部分是：(I)通过‘Engineering@CMU’计划接触高中生，(Ii)通过半导体研究公司-本科生研究机会计划让本科生参与研究，以及(Iii)与英特尔和IBM等公司定期举行电话会议。技术描述：阻性开关设备在十多年前就得到了展示，但许多期望的性能标准尚未达到。这在一定程度上是由于我们对电阻转换所涉及的过程，特别是高阻氧化层内导电丝的形成和演化缺乏了解。阻性开关通过离子在功能氧化物和电极之间的垂直和在氧化膜内的横向重新分布来编码信息。这项研究的目的是通过X射线能量色散光谱和电子和原子探针层析技术记录二维和三维元素图，绘制氧化膜中离子运动与器件偏置历史半导体的函数关系。特别令人感兴趣的是灯丝在器件寿命接近尾声时的演化。对寿命终止材料性能的分析导致了对器件失效机理的识别和预测。实验分布图由基于扩散方程的有限元模型模拟，扩散方程包括离子的运动以及热和电荷的传输，并附加了电偏置电路的限制。该模型旨在对整个类别的阻性开关设备通用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Non-technical description: Artificial Intelligence (AI) places specific demands on the information processing and as the applications multiply, they require a dedicated hardware specifically designed for the task. Current transistor-based digital circuits are not well suited for AI and finding new materials with novel functionalities, enabling new device principles is critical to the progress in this field. This project aims to bring advanced knowledge of material properties evolution upon working (cycling) conditions for a specific type of devices, namely for switches based on tantalum oxide that can repeatedly change the electrical resistance between high and low values as the result of the applied bias. The applications of such structures include non-volatile solid state (no moving parts) memories and artificial synapses mimicking the functions of the human brain. The research emphasizes understanding of material properties and the processes controlling the resistance of such device. The parallel goal is to educate the next generation of engineers for the electronics industry workforce. The components of the plan are (i) reaching out to high school students through 'Engineering@CMU' program, (ii) involvement of undergraduates in research through Semiconductor Research Corporation - Undergraduate Research Opportunity program, and (iii) holding regular conference calls with companies such as Intel and IBM.Technical description: Resistive switching devices have been demonstrated more than a decade ago but many of the desired performance criteria have not been met. This is, in part, due to our poor understanding of the processes involved in resistance switching and, in particular, the formation and evolution of a conducting filament within highly resistive oxide layer. Resistive switches encode information by redistribution of ions both vertically between the functional oxide and the electrodes and laterally within the oxide film. The research aims to map out the ion motion in the oxide film as a function of device bias history semiconductor by recording two- and three-dimensional elemental maps by X-ray energy dispersive spectroscopy and electron and atom probe tomographies. Of particular interest is the filament evolution toward the end of device lifetime. The analysis of end-of-endurance material properties leads to identification and prediction of the device failure mechanisms. The experimental distribution maps are being simulated by the finite element model based on diffusion equations including motion of ions and transport of heat and electric charge with additional restrictions imposed by the electrical biasing circuit. The model is intended to be universal for the entire class of resistive switching devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.