Intelligent multi-functional sensor platform for autonomous nano-tomography

Leuven - PhD
|
More than two weeks ago

Your sensor platform revolutionizes the way how we move around in the nanoscopic-world.

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Tomography approaches like magnetic resonance imaging (MRI) have revolutionized our medical diagnosis and treatment possibilities and are today widely used in hospitals around the ‘macroscopic’ world. In the ‘nanoscopic’ world, more specifically in nanoelectronics chip analysis, a similar revolution is taking place right now. As the highest integrated chips contain today transistor structures with sub-10 nm wide three-dimensional geometries, traditional inspection techniques are running out of steam and disruptive new analysis approaches are needed. Therefore, imec has recently demonstrated a three-dimensional analysis approach based on scanning probe microscopy (SPM) whereby nanometer-thick slices of materials are locally removed by a sharp diamond tip whereby measuring the local resistivity in between the individual slicing steps. This results in a series of 2D images which can be converted into a nano-tomogram. New device insights can be obtained in this way and our first results illustrate the high potential of such nano-tomography applications. Unfortunately, this approach is strongly hampered by the given constraints of a classical SPM system which uses the same tip for slicing and measuring, can measure only a single physical quantity at a time, is very slow, and is highly manual. This research topic addresses these challenges by the development of a novel sensor platform which can measure several physical quantities together (multi-functional), uses smart logic to control the slice-and-view scanning parameter on the fly (intelligent), and works fully automated without operator support (autonomous).

In this PhD topic you will research the basic principles for an integrated nano-tomography platform. You will investigate different approaches allowing for the actuation and sensing of individuals tips within an array and will explore how to interconnect them with CMOS analogue and digital circuitry. You will establish fabrication schemes for silicon, metal and diamond tips which can be combined and used in parallel within the same nano-tomography sensor chip. Besides this hardware-centered research, the second important aspect in this topic is the development of smart algorithms for autonomous slicing and signal collection whereby a constant feedback loop accounts for unwanted changes in this process (e.g, tip wear) and compensates for it (e.g, in-situ tip replacement). The third aspect of this research deals with the evaluation of the fabricated sensor chips and the establishment of the best operating parameters allowing the fully automated acquisition of optimized nano-tomography data sets. An important task is hereby to gain a better insight in how to achieve a nanometer-scale smooth surface during slicing and in general to transform SPM slicing into ‘true’ nanomilling (similar to macroscopic milling). Last but not least, the performance and usefulness of the developed sensor platform is demonstrated and benchmarked on most advanced nanoelectronics device structures (e.g. transistors, memory, interconnects).

The PhD candidate on this topic should be very enthusiastic about working in a state-of-the-art cleanroom and lab environment work. The student will use the standard microfabrication techniques in this research such as lithography, wet/dry etching, and metal/diamond deposition. Furthermore, he/she will work with imec’s advanced SPM tools. The student will collaborate closely with our SPM and probe experts. A high degree of independent working and the ability to efficiently collaborate within a team are crucial.

Required background: physics/engineering/nanotechnology

Type of work: Experimental 70% / Theory 30%

Supervisor: Wilfried Vandervorst

Daily advisor: Thomas Hantschel

The reference code for this position is 2020-003. Mention this reference code on your application form.
Chinese nationals who wish to apply for the CSC scholarship, should use the following code when applying for this topic: CSC2020-03.

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