The boost in switching speed and the perpetual reduction of device size
are two of the major driving forces of the semiconductor industry. As a
side effect, reliability problems have become more and more important,
due to the shortening of the channel length, which raises the electric
field in the device. Reliability issues like Hot Carrier Stress (HCS)
and Negative Bias Temperature Instability (NBTI) become major topics.
Although theoretical models have been developed from the very beginning,
they still do not satisfy all needs.
Up to now it is not quite certain, judging from the contradicting
reports, what kinds of defects are to blame for the NBTI phenomenon.
Therefore it is vital to collect measurement data in a systematic
manner. Empirical research and data analyses should finally lead to an
applicable model. One of the most established models, the
reaction-diffusion-model, assumes that some hydrogen species is first
released from previously passivated interface defects and then diffuses
into the oxide. Unfortunately this model is not capable of explaining the
relaxation behavior correctly.
A growing number of recent publications attribute at least a part of the
degradation to hole traps. Eventually a combination of these two
mechanisms may lead to a better description of the phenomenon.
Although most applications operate in AC mode, due to its simplicity
DC-stress has become the common test routine for reliability. Here, one
drawback is the missing consideration of the relaxation of the threshold
voltage during and after stressing of the device. This is of
considerable importance for accurate lifetime prediction.
Factors like the stress voltage (high electric field) and high
temperature, as well as time and duty cycle of the bias stress, have an
enormous impact on the recovery behavior. An intriguing feature of
relaxation is its universality, which is shown in the picture. The
exact physical mechanisms remain unresolved at the moment. One of the main
tasks is thus the development of a model which is able to anticipate
degradation precisely, in particular the long logarithmic tails covering
more than 10 decades of time.
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