Jonathon Magana, Ph.D.

Ph.D.

Electrical and Computer Engineering

University of Wisconsin-Madison

2018

M.S.

Computer Science

University of Wisconsin-Milwaukee

2012

J.D.

Law

Marquette University

2008

Presenter

STRITCHtalks  Cardinal Stritch University, Milwaukee, WI, 2017

Presenter

International Conference on Computer Aided Design  Austin, TX, 2016

Presenter

STRITCHtalks  Cardinal Stritch University, Milwaukee, WI, 2018

Analysis of Security of Split Manufacturing Using Machine Learning

Zhang, B., Magaña, J., Davoodi, A.

2018

This work is the first to analyze the security of split manufacturing using machine learning, based on data collected from layouts provided by industry, with 8 routing metal layers, and significant variation in wire size and routing congestion across the layers. We consider many types of layout features for machine learning including those obtained from placement, routing, and cell sizes. For the top split layer, we demonstrate dramatically better results in proximity attack compared to a recent prior work. We analyze the ranking of the features used by machine learning and show the importance of how features vary when moving to the lower layers. Since the runtime of our basic machine learning becomes prohibitively large for lower layers, we propose novel techniques to make it scalable with little sacrifice in effectiveness of the attack.

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Are proximity attacks a threat to the security of split manufacturing of integrated circuits?

Magaña, J., Shi, D., Melchert, J. and Davoodi, A.

2017

Split manufacturing is a technique that allows manufacturing the transistor-level and lower metal layers of an integrated circuit (IC) at a high-end, untrusted foundry, while manufacturing only the higher metal layers at a smaller, trusted foundry. Using split manufacturing is only viable if the untrusted foundry cannot reverse engineer the higher metal layer connections (and thus the overall IC design) from the lower layers. This paper studies the effectiveness of proximity attack as a key step to reverse engineer a design at the untrusted foundry. We propose and study different proximity attacks based on how a set of candidates are defined for each broken connection. The attacks use both placement and routing information along with factors which capture the router's behavior such as per-layer routing congestion. Our studies are based on designs having millions of nets routed across nine metal layers and significant layer-by-layer wire size variation. Our results show that a common, Hamming distance-based proximity attack seldom achieves a match rate over 5%. But our proposed attack yields a relatively small list of candidates which often contains the correct match. Finally, we propose a procedure to artificially insert routing blockages in a design at a desired split level, without causing any area overhead, in order to trick the router to make proximity-based reverse engineering significantly more challenging.

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