Physics of the fluctuation response of dielectric materials to external electrical perturbations, for advanced interconnects and emerging memories

Dielectric breakdown is one of the most important failure mechanisms responsible for the performance degradation of a wide spectrum of semiconductor devices. Dielectric failures are one of the main obstacles for the continuation of Moore’s law. Conventional methodologies study dielectric failures using accelerated tests, such as e.g. Time-Dependent Dielectric Breakdown (TDDB). The main limitation of TDDB is the lack of physical insights into the dielectric degradation mechanisms. 
 
In this project, an alternative methodology, based on 1/f noise, is explored to complement the missing physics of TDDB. 1/f noise probes the average defect density of the material. The more defects or ‘traps’ a dielectric has, the more degraded it is. The 1/f noise spectrum is obtained by examining the microscopic fluctuations arising in the dielectric leakage current. By studying the statistical distribution of these fluctuations, one can obtain a fundamental understanding of the physics of dielectric breakdown. 

What will you do? 

• Perform electrical 1/f noise measurements in both the time domain and the frequency domain, on dielectric materials
• Analyze the experimental 1/f noise data, using Taylor’s law, wherein the 1/f noise variance is expressed as a power law of the 1/f noise mean
• Develop a physical model to explain the observed dielectric degradation phenomena  

Who are you?

• You have a basic knowledge of semiconductor materials physics
• You have a science or engineering background with basic programming skills
• You are interested in experimental work, in state-of-the-art facilities at imec

References

1. N. Saini et al., IEEE IITC/MAM (2023). https://doi.org/10.1109/IITC/MAM57687.2023.10154814
2. H. Choi et al. IEEE EDL (2009). https://doi.org/10.1109/LED.2009.2015586
3. R. Degraeve et al., IEEE TED (1998). https://doi.org/10.1063/1.2147714
4. https://en.wikipedia.org/wiki/Pink_noise
5. S. Beyne et al., IEEE ICNF (2017). https://doi.org/10.1109/ICNF.2017.7985968 

 
Type of work: 50 % experimental, 30 % data analysis, 20 % interpretation

Duration: Minimum 3 months

Language requirements: English 

Type of Project: Internship 

Bachelor's program/required background: Physics, Electrical Engineering, Nanotechnology, Materials Science, Materials Engineering 

For more information or application, please contact Davide Tierno (davide.tierno@imec.be) and Nishant Saini (nishant.saini@imec.be)