Apr 29, 2021

[PhD] VLSI Interconnect Reliability

Shaoyi Peng
Modeling and Simulation Methods for VLSI Interconnect Reliability Focusing 
on Time Dependent Dielectric Breakdown
PhD Dissertation in Electrical Engineering
University of California Riverside
https://escholarship.org/uc/item/966241xk (March 2021)

Abstract: Time dependent dielectric breakdown (TDDB) is one of the important failure mechanisms for Copper (Cu) interconnects that are used in VLSI circuits. This reliability effect becomes more severe as the space between wires is shrinking and low-k dielectric materials (low electrical and mechanical strength) are used. There are many studies and theories focusing on the physics of it. However, there is limited research from the electronics design automation (EDA) perspective on this topic, aiming to evaluate, or alleviate it from the perspective of designing a VLSI chip. This thesis compiles several studies into evaluating TDDB on the circuit level, and engineering methods that help the evaluation. The first work extends the study of a published physics model on simplified yet practical cases. It simplifies the calculation of lifetime by deriving an analytic solution and applying fitting methods. The second study proposes a new way to evaluate lifetime of a chip by extending the models of simple interconnect structures to the complete chip. This method is more robust as it focuses more on a complete chip. However, heavy dependence of finite element method (FEM) makes the flow very slow. The third study adopts machine learning methods to accelerate this slow evaluation process. The proposed method is also applicable to other similar electrostatics applications. Last but not least, the fourth study focuses on a GPU based LU factorization algorithm, which, on a broader aspect, is a universal numerical algorithm used in many different simulation applications, which can be helpful to TDDB evaluations as it can be used in FEM.
Fig: Structure of two copper interconnect wires and the IMD in the cross-section SEM image after TDDB failure [sem]
REF
[sem] N. Suzumura, S. Yamamoto, D. Kodama, K. Makabe, J. Komori, E. Murakami, S. Maegawa, and K Kubota. A new TDDB degradation model based on Cu ion drift in Cu interconnect dielectrics. In IEEE Int. Reliability Physics Symposium (IRPS), pages 26–30, 2006.

Apr 28, 2021

Nanya Technology, the world’s fourth-largest #memory chip manufacturer



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Apr 26, 2021

Apr 21, 2021

[paper] Physical parameter-based data-driven modeling

Gokhan Satilmis1, Filiz Gunes2, Peyman Mahouti3
Physical parameter-based data-driven modeling of small signal parameters of a metal-semiconductor field-effect transistor
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 
(IJNM 2020): e2840

1 Department of Electric and Electronic Engineering, Mus¸ Alparslan University, Mus, Turkey
2 Department of Electronics and Communication Engineering, Yıldız Technical University, Istanbul, Turkey
3 Department of Electronic and Automation, Vocational School of Technical Sciences, Istanbul University Cerrahpasa, Istanbul, Turkey


Abstract: In this work, physical parameter-based modeling of small signal parameters for a metal-semiconductor field-effect transistor (MESFET) has been carried out as continuous functions of drain voltage, gate voltage, frequency, and gate width. For this purpose, a device simulator has been used to generate a big dataset of which the physical device parameters included material type, doping concentration and profile, contact type, gate length, gate width, and work function. Five state-of-the-art algorithms: multi-layer perceptron (MLP), IBk, K*, Bagging, and REPTree have been used for creating a regression model. The symbolic regression algorithm has been used to obtain analytical expressions of the real and imaginary parts of the Scattering (S) parameters using the physics-based generated dataset. The regression performances of all the benchmarks and the symbolic regression have been compared to references from the device simulator results. The results of the derived equations and the best algorithms have been then compared to the device simulator results, with case studies for validation. The DC performance characteristics of the MESFET have been also obtained. The proposed model can be used to predict the small signal parameters of new devices prior to development, and allows for both the device and circuit to be optimized for specific applications.

Fig: Input and output parameters used for the MESFET simulations

Acknowledgements: We would like to express our special appreciation and gratitude to the DataRobot Company for providing the software license

#Flying on #Mars fueled with #opensource software



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