PhD researcher on Precursors for area selective deposition in the context of advanced patterning applications

Leuven - PhD
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Meer dan twee weken geleden
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Over the last decades, the dimensions of micro-electronic devices have been continuously scaled down, leading to exponential gains in computational power and reduced production cost. However, as the device dimensions are shrinking further, this scaling brings significant challenges to photolithography, which becomes increasingly complex and expensive. In addition to the issue of resolution, the accurate pattern placement, i.e. the alignment of features to already existing features on the wafer, is a major concern. Therefore, the combination of the conventional “top-down” patterning techniques with alternative “bottom up” strategies for patterning is necessary. Examples include self-aligned multiple patterning, directed self-assembly (DSA) and area selective deposition. In area selective deposition, differences in surface reactivity are exploited to deposit material only according to certain predefined patterns, while other patterns on the same substrate remain unaffected. This selectivity can be achieved by processes that are based on surface reactions between gas phase precursors and the specific pattern, as used in chemical vapor deposition (CVD) and atomic layer deposition (ALD). However, practical applications of area selective deposition in patterning today are still limited, mainly because selectivity has been demonstrated only for few material combinations. In addition, the inherent surface dependence of ALD and CVD processes is rarely sufficient, and a much higher selectivity is needed to enable applications for patterning. A better understanding on the role of the precursor of ALD and CVD processes is essential to expand the material combinations accessible by area selective deposition as well as to improve selectivity.

The general aim of this PhD project is to generate insight in suitable precursor chemistries for area selective ALD and CVD processes for patterning applications. We will focus on area selective deposition of metal oxides in view of their high potential for application in patterning. First, the impact of the precursor (size, polarity, reactivity ...), co-reagent and process conditions and how these affects selectivity need to be better understood, so that the process window for selective deposition can be broadened to enable applications in patterning. For different precursors, we will determine the selectivity window an investigation of the nucleation mechanisms on the “growth” and “no growth” surfaces. The latter is also of particular importance as insight in the mechanisms for selectivity loss can be used to design selective deposition processes with improved selectivity. Both inherent selectivity and selectivity induced by surface passivation, e.g. using self-assembled monolayers, can be investigated. The research will be mainly experimental, but depending on the interest of the student a combined experimental/theoretical investigation might also be possible. Second, this insight will be applied to design approaches for area selective deposition relevant for patterning applications. In our research program on area selective deposition, we leverage imec’s 300mm production line and advanced node technologies to gain access to patterned structures with dimensions down to tens of nanometers in order to industrially relevant research of area selective deposition for patterning applications. The area selective deposition approaches will be tested on pre-patterned substrates with nm-scale dimensions, in order to investigate the impact of the integration process on surfaces, as well as to address additional questions such as how to maintain lateral confinement and how to explore characterize deposition in nm-scale  structures.

Required background: 

Chemistry, materials, nanotechnology.

Type of work:

10% literature study, 90% experimental work (depending on the interest of the student a combined experimental/theoretical investigation might also be possible.

Supervisor: Annelies Delabie

Daily advisor: Annelies Delabie

When you apply for this PhD project, mention the following reference code in the imec application form: ref. STS 1704-16.

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