Abstract:
According to an aspect of the present disclosure, a system for correcting for DC characteristics of a magnetic recording system includes: circuitry implementing at least a portion of a write channel of the magnetic recording system; and circuitry configured to process output data of the write channel circuitry in accordance with a read channel of the magnetic recording system and repeatedly trigger re-writing through the write channel circuitry using different ones of a plurality of available data scramblings until a measured baseline wander exceeds a target threshold.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a divisional application of, and claims priority to, U.S. patent application Ser. No. 13/350,684, filed on Jan. 13, 2012, entitled “Method and Apparatus to Limit DC-Level in Coded Data”, now U.S. Pat. No. 8,358,479, which is a continuation application of, and claims priority to, U.S. patent application Ser. No. 12/022,131, filed on Jan. 29, 2008, entitled “Method and Apparatus to Limit DC-Level in Coded Data”, now U.S. Pat. No. 8,098,447, which is a divisional application of U.S. patent application Ser. No. 10/752,817, filed on Jan. 6, 2004, entitled “Method and Apparatus to Limit DC-Level in Coded Data”, now U.S. Pat. No. 7,330,320, which claims priority from U.S. Provisional Patent Application No. 60/478,869, filed on Jun. 16, 2003, entitled “Method and Apparatus to Limit DC-Level in Coded Data”, and U.S. Provisional Application No. 60/485,216, filed on Jul. 7, 2003, entitled “Scrambling to Reduce DC-Content in Encoded Data”. The application herein claims the benefit of priority of all of the above listed patent applications and hereby incorporates by reference in their entirety the said patent applications. 
    
    
     BACKGROUND 
     Perpendicular magnetic recording (PMR) techniques may enable higher recording densities on magnetic storage media than conventional longitudinal magnetic recording techniques. PMR systems include heads that record bits perpendicular to the plane of the disk. PMR disks include a high permeability (“soft”) magnetic underlayer between a perpendicularly magnetized thin film data storage layer and the substrate. An image of the magnetic head pole created by the head is produced in the magnetically soft underlayer. Consequently, the storage layer is effectively in the gap of the recording head, where the magnetic recording field is larger than the fringing field produced by a longitudinal magnetic recording (LMR) head. The larger recording field makes it possible to record using smaller grain sizes and smaller bit sizes than in LMR systems. 
     In PMR, the channel response has a DC component. For a channel that is AC-coupled to the preamplifier and read channel, or that contains some other means for high-pass filtering the channel response, there may be DC-distortion. The DC-distortion may manifest itself as a data dependent baseline wander, which can severely affect the performance of a system that equalizes the channel response to a response target that is not DC-free. 
     SUMMARY 
     In an embodiment, a perpendicular magnetic recording (PMR) system may scramble an input data sequence with a first scramble seed and encode the scrambled data sequence with a modulation encoder (e.g., a run length limited (RLL) encoder). The system may then determine whether the scrambled and encoded data sequence includes one or more patterns associated with large baseline wander, using, e.g., the running digital sum (RDS) over the sequence or a low pass filter as a metric. If such a pattern is detected, the system may control the scrambler to re-scramble the data sequence with another scrambler seed, encode the re-scrambled sequence, and determine whether this scrambled and encoded sequence includes on or more patterns associated with large baseline wander. This process may be repeated until a scrambled and encoded sequence without such patterns is generated, or until all available scrambler seeds are exhausted, in which case, the scrambler seed with the least amount of baseline wander may be used. The best scrambled and decoded sequence may then be written to the magnetic recording medium. 
     In an alternative embodiment, a PMR system may scramble an input data sequence with a first scramble seed and encode the scrambled data sequence with a modulation encoder (e.g., a run length limited (RLL) encoder). A write signal may be generated and the encoded data sequence written to the magnetic recording medium. The write signal may also be fed back to a read channel in the system. The write signal may be passed through a filter to mimic the magnetic recording channel. The read channel may then determine if DC-wander in the write signal is too large to be accurately decoded. If so, the data sequence may be scrambled with another scramble seed, encoded, and used to over-write the first encoded data sequence in the magnetic recording medium. This process may be repeated until an encoded data sequence that can be accurately decoded by the read channel is generated. 
     For both of the alternative embodiments described above, the data sequence may not be scrambled in the first run through the system. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram of a read/write head and media in a perpendicular magnetic recording (PMR) system. 
         FIG. 2  is a block diagram of a write channel and a read channel in the PMR system. 
         FIG. 3  is a flowchart describing a DC-wander correction technique according to an embodiment. 
         FIG. 4  is a flowchart describing a DC-wander correction technique according to another embodiment. 
         FIG. 5  is a block diagram of an encoding portion of a write channel in a PMR system. 
         FIG. 6A  is a block diagram of a system modeling DC-offset due to high pass filters. 
         FIG. 6B  is a block diagram of an equivalent system modeling DC-offset due to high pass filters using a low pass filter. 
         FIG. 7  is a block diagram of a discrete time model of a magnetic recording system. 
         FIG. 8  is a block diagram of a discrete time model of DC-offset in a magnetic recording system. 
         FIG. 9  is block diagram of a simplified model of DC-offset in a magnetic recording system. 
         FIG. 10  is block diagram of a simplified model of DC-offset in a magnetic recording system using an adaptive DC-correction circuit. 
         FIG. 11  is a block diagram of another simplified model of DC-offset in a magnetic recording system using an adaptive DC-correction circuit. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  shows a read/write head  102  and magnetic storage disk  104  in a perpendicular magnetic recording (PMR) system. The head records bits perpendicular to the plane of the disk. PMR disks include a high permeability (“soft”) magnetic underlayer  106  between a perpendicularly magnetized thin film data storage layer  108  and the substrate  110 . An image of the magnetic head pole created by the head  102  is produced in the magnetically soft underlayer  106 . Consequently, the storage layer  108  is effectively in the gap of the recording head, where the magnetic recording field is larger than the fringing field produced by a longitudinal magnetic recording (LMR) head. 
     In PMR, the channel response has a DC component. For a channel that is AC-coupled to the preamplifier and read channel, or that contains some other means for high-pass filtering the channel response, there may be DC-distortion. The DC-distortion may manifest itself as a data dependent baseline wander, which can severely affect the performance of a system that equalizes the channel response to a response target that is not DC-free. 
       FIG. 2  shows a write channel  202  and a read channel  204  for the PMR system. In an embodiment, the data that is being written by the write channel  202  is fed back into the read channel  204 . The read channel processes the data and decides if the written sequence is likely to have very poor DC characteristics. If that is the case, the write channel changes a scrambler seed and rewrites the data using the new scrambler seed. 
       FIG. 3  is a flowchart describing a DC-wander correction technique according to an embodiment. A scrambler module  206  may use a scrambler seed  208  to scramble the data  210  input to the write channel  202  (block  302 ). The data may be scrambled before it is encoded by the modulation encoder, e.g., a run length limited (RLL) encoder  212  (block  304 ). Alternatively, the scrambling may be performed after modulation encoding if the scrambler used does not destroy the constraints imposed by the modulation encoder (for example, if only bits that are left uncoded by the modulation encoder are scrambled and those bits do not affect how the encoded bits were encoded). 
     A number of different scramblers may be used. For example, in an embodiment, a pseudo-noise (PN) sequence generated using a maximum-length shift register may be added modulo-2 to each data bit to be scrambled. Regardless of the type of scrambler used, the detector must know if and how the data was scrambled to properly descramble the data. In an embodiment, the detector may know how the data is scrambled, but may not necessarily know the initial conditions or the scrambler seed that was used to scramble the data. This information (e.g., the scrambler seed or method) may be embedded in the data that is written. Alternatively, the detector may descramble the data by trial and error. For example, the detector may descramble the data following a predetermined list of scramblers/scrambler seeds until the descrambled data decodes properly by some error-control code (ECC)  213 , cyclic redundancy check (CRC) code, or some other check. In an embodiment, the scrambling is done prior to ECC encoding in the write channel, and descrambling is done after ECC decoding at the detector. 
     The write signal generated by the write channel is written to the disk (block  305 ). The write signal is also fed back into the read channel  204  (block  306 ), possibly via a filter  214  to mimic the magnetic channel. The read channel  204  processes the signal (block  308 ), and a decision block  220  determines if the DC-wander is too severe to be handled in the read channel (block  310 ). If the DC-wander is determined to be within acceptable limits at block  310 , then the next data sequence in the input data stream is scrambled (block  312 ) and encoded. However, if the DC-wander is determined to be too severe, the read channel requests that the sector be rewritten with another scrambler seed (block  314 ). The newly scrambled data sequence is then encoded (block  316 ) and re-written to the disk (block  318 ), over-writing the “bad” sequence. 
     In an embodiment, all of the functions in the read channel  204  that would be expected to be active in the actual reading of a waveform from the disk are active. An error signal generated internally in the read channel may be used to monitor how severe the DC-wander is at the detector input (at block  310 ). If the DC-wander is determined to be too large, a re-write request may be asserted by the read channel. In other embodiments, various functions of the read channel may disabled. For example, in an embodiment, the bit detector may be disabled, since the bits can be obtained directly from the write channel. 
     Several parameters may be used by the decision block  220  to determine whether the DC-wander is too large. The following are exemplary parameters for determining excessive DC-wander: 
     (a) Simple threshold: the decision block  220  considers the DC-wander to be too large if the absolute value of the error signal is larger than a given threshold at any point in the data sequence; 
     (b) The decision block  220  considers the DC-wander to be too large if the absolute value of the error signal is larger than a given threshold for a total of at least a given number of clock cycles; 
     (c) The decision block  220  considers the DC-wander to be too large if the absolute value of the error signal exceeds the given threshold for at least a given number of consecutive clock cycles. For example, in an embodiment, the threshold is 3 and the given number of consecutive cycles is three. The error sequence |e|={0, 1, 4, 5, 1, 6, 3, 4, 6, 7, 2, 3, 7} has seven numbers greater than 3. However, only the three consecutive occurrences of numbers greater than 3 (i.e., the sub-sequence {4, 6, 7}) are counted. 
     The parameters described above may be used separately or combined. For example, the “simple threshold” (a) and “consecutive clock cycle” (c) parameters can be combined. Then the threshold for (a) should be larger than the threshold for (c). 
     In an embodiment, an encoded data sequence may be inspected for patterns that might cause large baseline wander before being written to disk, i.e., in the write channel. The data sequence may be repeatedly scrambled and encoded until an acceptable level of estimated DC-wander has been achieved. The data sequence may then be written to disk. 
       FIG. 4  is a flowchart describing a DC-wander correction technique according to an embodiment. As shown in  FIG. 5 , a scrambler module  502  may use a scrambler seed  504  to scramble data sequences in an input data stream  506  (block  402 ). Information about the scrambler (e.g., the scrambler seed or method) may be embedded in the data so that the data can be readily decoded by the detector. The scrambled data may then be encoded by an RLL encoder  508  (block  404 ). 
     After the RLL encoder, the encoded data may be output to an output buffer  510  and a DC-wander estimation module  512  (block  406 ). The DC-wander estimation module may screen for patterns that might cause large baseline wander (i.e., “bad” patterns) (block  408 ). If no such pattern is found, the output buffer may output the encoded data to the write channel for further processing and writing to the disk (block  410 ). Otherwise, the data is scrambled using another scrambler seed (block  412 ) and then encoded by the RLL encoder  508  (block  404 ). The newly encoded data sequence is then screened for patterns that may cause large baseline wander (block  408 ). This process may continue until the encoded data sequence is determined to contain no bad patterns. The encoded data is then output to the write channel. In the case that all scrambler seeds are exhausted (block  414 ), the data may be scrambled using the scrambler seed that yielded the least baseline wander (block  416 ). 
     Several metrics may be used by the DC-wander estimation module  512  to measure baseline wander. In an embodiment, the maximum absolute value of the running digital sum (RDS max ) over the entire sequence may be used. 
     The running digital sum of a binary sequence x={x 0 , x 1 , . . . , }, where x i =±1 is defined as 
     
       
         
           
             
               
                 
                   
                     RDS 
                     ⁡ 
                     
                       ( 
                       n 
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         0 
                       
                       n 
                     
                     ⁢ 
                     
                       x 
                       i 
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     The maximum absolute value of RDS over a sequence of length N is 
     
       
         
           
             
               
                 
                   
                     RDS 
                     max 
                   
                   = 
                   
                     
                       max 
                       
                         0 
                         ≤ 
                         n 
                         ≤ 
                         
                           N 
                           - 
                           1 
                         
                       
                     
                     ⁢ 
                     
                       
                          
                         
                           RDS 
                           ⁡ 
                           
                             ( 
                             n 
                             ) 
                           
                         
                          
                       
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     A first method to determine if a sequence has large baseline wander uses equation (2) for the entire sequence. If RDS max  is above a certain threshold, the sequence is considered to have a large baseline wander. In the case that all available sequences have a large baseline wander, the sequence with the smallest RDS max  may be selected. 
     A second method splits the sequence into two or more subsequences, and uses the first method for each sub-sequence. In the case that all available sequences have one or more sub-sequences with large baseline wander, the sequence with the smallest number of sub-sequences with large baseline wander may be selected. A tie among those can, for example, be broken by selecting the sequence with the smallest RDS max . 
     For a third method, the number of bit periods for which the absolute value of the RDS is greater than a threshold is counted. 
     
       
         
           
             
               
                 
                   
                     
                       RDS 
                       count 
                     
                     = 
                     
                       
                         ∑ 
                         
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                           = 
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                       ⁢ 
                       
                         I 
                         i 
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     where I i  is an indicator function 
     
       
         
           
             
               
                 
                   
                     I 
                     i 
                   
                   = 
                   
                     { 
                     
                       
                         
                           
                             
                               1 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               if 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               
                                  
                                 
                                   RDS 
                                   ⁡ 
                                   
                                     ( 
                                     i 
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                                  
                               
                             
                             &gt; 
                             
                               t 
                               h 
                             
                           
                         
                       
                       
                         
                           
                             
                               0 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
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                                   RDS 
                                   ⁡ 
                                   
                                     ( 
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                               t 
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                   ( 
                   4 
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     If RDS count  is greater than a certain value, the sequence is determined to have a large baseline wander. If all available sequences have large baseline wander, then the sequence with the smallest RDS count  is selected. 
     A fourth method is similar to the third method, but only the first instance when several consecutive values of the RDS is greater than a threshold is counted. 
     
       
         
           
             
               
                 
                   
                     
                       RDS 
                       nbr 
                     
                     = 
                     
                       
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                           = 
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                           - 
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                       ⁢ 
                       
                         I 
                         i 
                         b 
                       
                     
                   
                   , 
                   
                     
 
                   
                   ⁢ 
                   where 
                 
               
               
                 
                   ( 
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                     I 
                     i 
                     b 
                   
                   = 
                   
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                               ⁢ 
                               
                                   
                               
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                               ⁢ 
                               
                                   
                               
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                                   RDS 
                                   ⁡ 
                                   
                                     ( 
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                                 t 
                                 h 
                               
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                               ⁢ 
                               
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                                   RDS 
                                   ⁡ 
                                   
                                     ( 
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                               t 
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                             otherwise 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
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                   ) 
                 
               
             
           
         
       
     
     For example, if the RDS for a sequence of length 20 is given by {1, 0, −1, −2, −3, −4, −5, −4, −3, −4, −3, −2, −1, 1, 2, 1, 2, 3, 2}, and the threshold t h =3. Then RDS coount =4 using equation (3), and RDS nbr =2 using equation (5). 
     In a fifth method, the mean of the absolute value of the RDS is used. 
     
       
         
           
             
               
                 
                   
                     RDS 
                     mean 
                   
                   = 
                   
                     
                       1 
                       N 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           0 
                         
                         
                           N 
                           - 
                           1 
                         
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         
                            
                           
                             RDS 
                             ⁡ 
                             
                               ( 
                               i 
                               ) 
                             
                           
                            
                         
                         . 
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     If RDS mean  is greater than a threshold, the baseline wander may be considered to be large. 
     In an alternative embodiment, a low pass filtered version of the sequence is used as a metric rather than the RDS of the sequence. The main source of baseline wander in many systems is AC coupling or other high pass filtering circuits. The amount of baseline wander caused by a code sequence can be estimated by passing the sequence through a model of the high pass filter  602  (which mimics AC-coupling), and subtracting the output of the filter from the input sequence, as shown in  FIG. 6A . Equivalently, the amount of baseline wander caused by a code sequence can be estimated by passing the code sequence through a low pass filter  604  with a transfer function that complements the high pass filter, as shown in  FIG. 6B . For example, if the high pass filter model has the transfer function H(z), then the low pass filter should have the transfer function F(z)=1−H(z). 
     Other, usually more complex, filters can be designed to mimic not only the impact of high pass filters, but also the impact of the write and read process and the signal shaping in the read channel.  FIG. 7  shows a simple block diagram of a discrete time model of a magnetic recording channel. This model may be used as a basis for the DC-offset model shown in  FIG. 8 . Since the correct bit decisions are know, they may be used instead of a Viterbi detector  702 . The DC-offset may be estimated by passing the encoded sequence through an ideal target response  802  and then subtracting the finite impulse response (FIR) equalizer output  704  from the target response output. This model can be simplified as shown in  FIG. 9 , where the filter  900  is given by H(D)=t(D)−h(D)g1(D)g2(D)f(D). 
     Although several examples have been given, any filter that generates an estimate of the DC-offset based on the encoded data sequence as input can be used for this purpose. 
     The following are exemplary parameters that may be used to determine if a sequence has a large DC-wander in systems using low pass filtered sequences as a metric: 
     If the largest DC-offset of the sequence is larger than a threshold; 
     If the largest DC-offsets of the sub-sequences are larger than a threshold; 
     If the DC-offset is larger than a threshold for more than another threshold number of bit-cycles; 
     If the DC-offset is larger than a threshold for more than another threshold number of times; 
     If the mean of the absolute value of the DC-offset is greater than a threshold. 
     In another embodiment, a low pass filtered sequence with DC correction is used as a metric. Instead of just estimating the DC-offset based on filtering the encoded sequence, this method assumes that there is a DC-correction circuit built into the system. Different types of DC-correction circuits can be used. In  FIG. 10 , a block diagram of the filter is shown with an adaptive DC-correction circuit  1000 . In most cases, the DC-correction circuit can be modeled as a low pass filter  900 . Another block diagram of the DC-offset estimation filter with DC-correction circuit  1100  is shown in  FIG. 11 . Here, the DC-correction circuit uses the DC-corrected channel estimation filter output  1102  as its input. 
     In an alternative embodiment, several seeds may be used to scramble the encoded sequence in parallel. The scrambled sequences may then be encoded and evaluated in parallel. The seed that provides the best response may then be selected. 
     In other alternative embodiments, the first trial may not be scrambled. For example, block  302  and  402  in  FIGS. 3 and 4 , respectively, may be skipped when the sequence is first input to the write channel. 
     A number of embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. For example, blocks in the flowcharts may be skipped or performed out of order and still produce desirable results. Accordingly, other embodiments are within the scope of the following claims.