Graph Attention Networks to Identify the Impact of Transistor Degradation on Circuit Reliability

Tarek Mohamed, Victor M. Van Santen, Lilas Alrahis, Ozgur Sinanoglu, Hussam Amrouch

Research output: Contribution to journalArticlepeer-review


Reliability is one of the key concerns in circuit design. The circuit must be able to tolerate transistor degradation to sustain reliability against timing failure. Whether a transistor is degraded due to noise, aging, or poor manufacturing, a circuit must uphold a timing error-free functionality over its entire projected lifetime. Transistors are hardened (designed stronger than necessary) to tolerate these degradations. However, hardening (e.g., widening the transistors) comes at the cost of additional area and power. Hence, it is necessary to identify and selectively harden specific transistors within a circuit. In this work, transistors that prolong a circuit's delay when they are degraded are termed 'susceptible', and thus, are to be hardened. Identifying the susceptible transistors within a circuit is a complex task, for example, Monte Carlo circuit simulations require days to identify susceptible transistors in a single circuit. Consequently, current solutions are costly in terms of time and limited in their application. Instead, machine learning (ML) can offer a fast (inference in seconds) and universal (applicable to unseen circuits) alternative. However, traditional ML techniques struggle with inference on topology-based problems, while recent graph neural networks (GNNs) excel in these applications. Therefore, this work presents the first ML to classify susceptible transistors with GNNs. We use GNNs, specifically Graph Attention Networks (GAT), because the topology of the cell strongly affects how each transistor degradation affects performance. For instance, series-connected transistors amplify their impact, while parallel-connected ones can offset each other's influence. Our GAT-based approach employs a heterogeneous graph in combination with GAT's attention mechanism to capture the circuit's topology and its impact on the analysis. Our evaluation demonstrates the capability of our approach to classifying transistors according to their impact within the ASAP7 standard cell library's standard cells in mere 0.04 s (compared to days of Monte Carlo simulation time) while achieving 80.4% accuracy on unseen circuits.

Original languageEnglish (US)
Pages (from-to)3269-3281
Number of pages13
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number7
StatePublished - Jul 1 2024


  • Reliability
  • aging
  • circuits
  • degradation
  • graph attention
  • graph neural network
  • transistors

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering


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