Schedule for this course

This course is for anyone who needs to come quickly up-to-speed on data storage read channels. It focuses on detection methods and practical considerations for perpendicular recording that lead from the fundamentals of signal detection to Noise Predictive Maximum Likelihood (NPML), or Noise Predictive Viterbi (NPV), and on to Data-Dependent Noise Prediction (DDNP).

You will understand the difference between PR (partial response) and ML (maximum likelihood sequence detection) and how they work together to form the industry standard of data detection. Plus, you will gain an intuitive understanding of correlated noise, its causes, and how it affects NPML/NPV channels. You will also understand key practical considerations such as data dependencies, non-linearities, detector target optimization, recording codes, gain and timing recovery, and filter adaptation, as well as how the system works and its tradeoffs.

Additionally, many attendees from the tape, optical disc, and flash memory industries have found the course material and insights provided useful and applicable to their systems. We also briefly introduce current topics such as turbo codes, LDPC codes, iterative detection, discrete-track recording, low SNR timing recovery, and “software” channels. Some of topics are covered in greater detail in our companion course Introduction to Iterative Detection.

The course is designed for those new to recording channels, while providing industry veterans with a clear, concise review of key topics and the insight and context needed to understand and assess new developments. Suitable industry experience or previous disk drive technology training will satisfy the prerequisites.
 

Perpendicular Recording Process Overview Read and Write Process NRZ and NRZI Data Sequences Response Models Intersymbol Interference (ISI) User Density Read Channel Basics The Typical Drive Layout Data Rate Error Mechanisms White Gaussian Noise  Calculating BER Performance Maximum Likelihood Sequence Detection The Viterbi Algorithm Revealed! Path Metric Calculation Simplifications Signal Non-Linearities What is Linearity? Non-Linear Transition Shift (NLTS) Media Noise (Transition Jitter) Partial Erasure  Asymmetry Thermal Asperities Signal Processing Basics Sampling Bandlimited Signals Aliasing Transforms: D, Fourier Sinc Function Filtering and Modeling Traditional Partial Response Targets Developing Intuition Class 4 Partial Response Traditional PR4, EPR4, EEPR4 Targets 'Natural Density' Class 1 and Class 2 The ‘Ideal’ Target? Error Events in Sequence Detectors (Minimum) Euclidean Distance Calculating BER Performance What Causes an Error? Error Event Notation

Adaptive Equalization Target Spectra Designing the Equalizer Continuous-Time Filters LMS Adaptation of FIR Gain and Timing Recovery: AGC & PLL Where Does Timing Information Come From? The Effects of ISI Preliminary Target Determination Timing Function (Timing Error Detector, TED) Introduction to Interpolated Timing Recovery (ITR) Interaction of Feedback Control Loops Coding Run-Length Limited (RLL) Code Basics d=0 Codes for PRML Introduction to Distance Enhancing Codes Quantifying Error Events Maximum Transition Run (MTR) Codes Rate-1 Codes? Permuted Architectures Parity-Assist PRML, GPR, NPML, NPV, PPNP, DDNP Methods for Comparing Channels Introduction to Noise Prediction and Non-Standard    Targets Noise Whitening Filters Non-Linear Transition Jitter Noise Model SNR Definition in Media Noise Data-Dependent Noise Prediction Conclusions and Future Trends The Big Picture Introduction to LDPC, Turbo Codes,    Iterative Detection, Turbo Equalization,    and Low SNR Timing Recovery Discrete Track Media The Next dB Effects of Emerging Market Segments  New Challenges

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