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[Paper Review] Concerted wire lifting: enabling secure and cost-effective split manufacturing

Satwik Patnaik, Johann Knechtel|arXiv (Cornell University)|Jan 22, 2018
Physical Unclonable Functions (PUFs) and Hardware Security19 references15 citations
TL;DR

This paper proposes Concerted Wire Lifting, a novel method to enhance security and reduce costs in split manufacturing by strategically elevating interconnects to the BEOL using custom cells. It achieves 0% netlist recovery rate in benchmarks—first in literature—while maintaining low PPA overhead and reducing commercial costs.

ABSTRACT

Here we advance the protection of split manufacturing (SM)-based layouts through the judicious and well-controlled handling of interconnects. Initially, we explore the cost-security trade-offs of SM, which are limiting its adoption. Aiming to resolve this issue, we propose effective and efficient strategies to lift nets to the BEOL. Towards this end, we design custom elevating cells which we also provide to the community. Further, we define and promote a new metric, Percentage of Netlist Recovery (PNR), which can quantify the resilience against gate-level theft of intellectual property (IP) in a manner more meaningful than established metrics. Our extensive experiments show that we outperform the recent protection schemes regarding security. For example, we reduce the correct connection rate to 0% for commonly considered benchmarks, which is a first in the literature. Besides, we induce reasonably low and controllable overheads on power, performance, and area (PPA). At the same time, we also help to lower the commercial cost incurred by SM.

Motivation & Objective

  • Address the limited adoption of split manufacturing due to high cost-security trade-offs.
  • Develop a secure and efficient method to protect interconnects from gate-level IP theft.
  • Minimize power, performance, and area (PPA) overheads while enhancing security.
  • Lower commercial costs associated with split manufacturing through optimized wire lifting.
  • Introduce a new, more meaningful metric—Percentage of Netlist Recovery (PNR)—to quantify IP protection effectiveness.

Proposed method

  • Propose a wire lifting technique that elevates interconnects to the Back-End-of-Line (BEOL) layer to prevent reverse engineering.
  • Design custom elevating cells to enable precise and controllable wire lifting at the layout level.
  • Introduce the Percentage of Netlist Recovery (PNR) metric to quantitatively assess resilience against gate-level IP theft.
  • Apply the method to standard benchmarks to evaluate security and PPA trade-offs.
  • Optimize the lifting process to minimize power, performance, and area overheads.
  • Release the custom elevating cells to the research community to promote reproducibility and adoption.

Experimental results

Research questions

  • RQ1How can interconnects be lifted to the BEOL in a way that maximizes security with minimal PPA overhead?
  • RQ2To what extent does the proposed method reduce the success rate of netlist recovery attacks compared to prior work?
  • RQ3Can a new metric like PNR better quantify IP protection resilience than existing metrics?
  • RQ4What is the impact of wire lifting on commercial manufacturing costs in split manufacturing?
  • RQ5How controllable and scalable is the proposed wire lifting technique across standard benchmarks?

Key findings

  • The proposed method reduces the correct connection rate—indicating netlist recovery—to 0% on commonly used benchmarks, a first in the literature.
  • The technique achieves strong security with reasonably low and controllable overheads in power, performance, and area (PPA).
  • The Percentage of Netlist Recovery (PNR) metric effectively quantifies IP protection resilience in a more meaningful way than prior metrics.
  • The method enables cost-effective split manufacturing by reducing commercial costs associated with the process.
  • The custom elevating cells developed are publicly released, supporting reproducibility and community adoption.

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This review was created by AI and reviewed by human editors.