Electrical engineering

Physics

Nanotechnology

Transistor

Characterization (materials science)

Operating system

Computer engineering

Electronic circuit

Engineering

Voltage

Embedded system

Artificial intelligence

Power (physics)

Materials science

Reliability engineering

Electronic engineering

Degradation (telecommunications)

Computer science

Quantum mechanics

Static random-access memory

Process variation

Computer hardware

Process (computing)

Reliability (semiconductor)

Telecommunications

IEEE Transactions on Computers

Transistor scaling steadily approaches fundamental limits. Sustaining circuit reliability becomes an overwhelming challenge for foundries. Therefore, early and rapid characterization of degradation effects impacting the circuits transistors becomes essential. Such degradation effects are caused by design-time variation due to manufacturing variability and/or run-time variation due to transistor aging. In this work, we are the first to employ Brain-Inspired Hyperdimensional computing (HDC) for circuit reliability. HDC is an emerging light-weight machine-learning solution. Nowadays, it is mainly applied to bio-signal processing. We bring the research of HDC to the next level by demonstrating how it can be applied to address the challenges in circuit reliability. This has far-reaching consequences due to the large savings achieved by 1) reducing the amount of training data, 2) removing the need to send the data to the Cloud for model training, and 3) significantly speeding up the characterization and classification tasks. We demonstrate the viability of HDC, using SRAM and other circuits as an example. HDC outperforms traditional machine learning methods, such as support vector machine, in accuracy and requires up to 20x fewer training samples. Our implementation and analysis are based on industrial 14 nm FinFET fully calibrated with Intel measurements.

Brain-Inspired Computing for Circuit Reliability Characterization