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Bias Temperature Instability Modelling and Lifetime Prediction on Nano-scale MOSFETs

Gao, R (2018) Bias Temperature Instability Modelling and Lifetime Prediction on Nano-scale MOSFETs. Doctoral thesis, Liverpool John Moores University.

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Abstract

Bias Temperature Instability (BTI) is one of the most important reliability concerns for Metal Oxide Semiconductor Field Effect Transistors (MOSFET), the basic unit in integrated circuits. As the development MOSFET manufacturing technology, circuit designers need to consider device reliability during design optimization. An accurate BTI lifetime prediction methodology becomes a prerequisite. Typical BTI lifetime standard is ten years, accelerated BTI tests under high stress voltages are mandatory. BTI modelling is needed to project BTI lifetime from high voltages (accelerated condition) to operating voltage. The existing two mainstream BTI models: 1). The Reaction-Diffusion (R-D) framework and 2). The Two-Stage model cannot provide accurate lifetime prediction. Quite a few fitting parameters and unjustifiable empirical equations are needed in the R-D framework to predict the lifetime, questioning its predicting capability. The Two-stage model cannot project device lifetime from high voltages to operating voltage. Moreover, the scaling down of MOSFET feature size brings new challenges to nano-scale device lifetime prediction: 1). Nano-scale devices’ current is fluctuating due to the impact of a single charge is increasing as MOSFET scaling down, repetitive tests need to be done to achieve meaningful averaged results; 2). Nano-scale devices have significant Device-to-Device variability, making the lifetime a distribution instead of a single value. In this work a comprehensive As-grown Generation (A-G) framework based on the A-G model and defect centric theory is proposed and successfully predicts the Time Dependent Variability and lifetime on nano-scale devices. The predicting capability is validated by the good agreement between the test data and predicted values. It is speculated that the good predicting capability is due to the correct understanding of different types of defects. In the A-G framework, Time Dependent Variability is experimentally separated into Within-Device Fluctuation and the averaged degradation. Within-Device Fluctuation can be directly measured and the averaged degradation can be modelled using the A-G model. The averaged degradation in the A-G model contains: Generated Defects, As-grown Traps and Energy Alternating Defects. These defects have different kinetics against stress time thus need separate modelling. Various patterns such as Stress-Discharge-Recharge, multi-Discharging-based Multiple Pulses are designed to experimentally separate these defects based on their different charging/discharging properties. Fast-Voltage Step Stress technique is developed to reduce the testing time by 90% for the A-G framework parameter extraction, making the framework practical for potential use in industry.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Bias Temperature Instability (BTI); Time Dependent Variability; Nano-scale MOSFETs; As-grown Generation (A-G) model; Lifetime Prediction.
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Electronics and Electrical Engineering
Date Deposited: 26 Jul 2018 13:04
Last Modified: 26 Jul 2018 13:04
Supervisors: Ji, Z
URI: http://researchonline.ljmu.ac.uk/id/eprint/8989

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