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，寻找具有新功能的新材料，实现新的器件原理对该领域的进展至关重要。该项目旨在为特定类型的器件提供在工作（循环）条件下材料特性演变的先进知识，即基于氧化钽的开关，该开关可以由于施加偏置而在高值和低值之间反复改变电阻。这种结构的应用包括非易失性固态（无运动部件）记忆和模仿人脑功能的人工突触。该研究强调对材料特性和控制该器件电阻的过程的理解。平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：(1)通过“Engineering@CMU”计划接触高中生；(2)通过半导体研究公司的大学生研究机会计划让大学生参与研究；(3)与英特尔和IBM等公司定期举行电话会议。技术描述：电阻开关器件在十多年前就已被证明，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开关过程的理解不足，特别是对高电阻氧化层中导电丝的形成和演变的理解不足。电阻开关通过离子在功能性氧化物和电极之间的垂直和氧化膜内的横向重新分配来编码信息。该研究旨在通过x射线能量色散光谱和电子原子探针层析成像记录二维和三维元素图，绘制氧化膜中离子运动作为器件偏置历史半导体的函数。特别令人感兴趣的是灯丝在器件寿命结束时的演变。对失效材料性能的分析有助于对器件失效机理进行识别和预测。实验分布图用有限元模型模拟，该模型基于扩散方程，包括离子运动和热电荷输运，外加电偏置电路的限制。该模型适用于所有类型的电阻开关器件。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.