Items Tagged with 'BER'

How well do statistical models predict the behavior of real-world systems?  How can we make predictions about the likeliness and severity of worst-case system behavior?  In this article, the authors explain how they used a function generator and oscilloscope to collect varying population sizes of measurement parameter results in order to investigate the accuracy of a statistical model's predictions.

Designers pushing the limits in their application can run into situations where the XO performance is inadequate for their next design due to the way it reacts to the noise and ripple in the power supply. To achieve optimal performance, they most likely will find they will need to do more than a simple datasheet evaluation to select their next XO. Read on for details on a PSNR test method to help select an optimal XO.

What are the test challenges brought on by PAM4? This article details what a BERT needs to do to meet the necessary signal analysis requirements.

While a channel may pass a test, the remaining margin and thus its resilience against geometry or material variation in production may not be observable. However, such variations are critical because they may impede the performance or cause high volume manufacturing (HVM) products to fail. This coalition of authors has developed and demonstrated a polynomial chaos expansion (PCE) flow to analyze a full-featured 100GBASE-KR4 link starting from geometry specification to Channel Operating Margin (COM) margin at the receiver. Read on to see their award winning paper on the subject.

Ken Willis provides some background on the IBIS AMI standard and looks ahead to what’s next.

We analyze the computational procedure specified for Channel Operation Margin (COM) and compare it to traditional statistical eye/BER analysis. There are a number of differences between the two approaches, ranging from how they perform channel characterization, to how they consider Tx and Rx noise and apply termination, to the differences between numerical procedures employed to convert given jitter and crosstalk responses into the vertical distribution characterizing eye diagrams and BER. We show that depending on the channel COM may potentially overestimate the effect of crosstalk and, depending on a number of factors, over- or underestimate the effect of transmit jitter, especially when the channel operates at the rate limits. We propose a modification to the COM procedure that eliminates these problems without considerable work increase.