Signal Processing Participants Introduction

Signal Processing Participants

Vijayakumar Bhagavatula, Carnegie Mellon University

Dave Blankenbeckler, DataPlay

Chong Tow Chong, Data Storage Institute

Dennis Howe, University of Arizona

Seiji Kobayashi, Sony

Hiroshi Koide, Ricoh

Jay Livingston, Cirrus Logic

Steve McLaughlin, Georgia Institute of Technology (Co-leader)

Kees Schep, Philips

LuPing Shi, Data Storage Institute

Terry Wong, Calimetrics (Co-leader)

Fumihiko Yokogawa, Pioneer

Introduction

In the last NSIC optical data storage roadmap [12], signal processing and multilevel recording were broken out as separate sections. However, there is a great deal of overlap between these two subject areas. In particular, multilevel recording is enabled by signal processing technology improvements in the writing and reading of an optical data storage system. Thus, it seemed logical to combine the subgroups that cover these areas. On the other hand, the technology of multilevel recording has grown substantially in its own right since the last roadmap was published in 2000. In this section, we first introduce the current state of signal processing technology. Within the discussion of the current state of the technology, we have a separate subsection that introduces the basics of multilevel recording and reviews three examples of multilevel systems. We then focus on signal processing areas that, in the future, are likely to become important for optical data storage. And finally, since signal processing in general is mostly an enabling technology and would be difficult to “roadmap”, we present a roadmap for products using multilevel recording.

Signal processing and coding have become increasingly important and powerful parts of optical data storage systems. As densities increase, more powerful methods are required to deal with decreasing signal-to-noise ratio (SNR), intersymbol interference (ISI) from adjacent symbols in the same track, intertrack interference (ITI) or crosstalk from neighboring tracks, nonlinearities in

the channel, defects, off-track performance, defocus, etc. Signal processing/coding algorithms and architectures must address the above issues, be cost effective, and operate at increasing data rates. In this section, we will briefly review the major elements of signal processing and coding for optical data storage and summarize, at a high level, the current state of the art.

Figure 1 gives a block diagram of the signal processing and coding elements in a typical data storage system. Error correction coding (ECC) is a major component in virtually all systems – it corrects random and burst errors caused by the many impairments that affect the channel. Modulation is used to represent the logical data to be stored by a physical entity (e.g. a sequence of electrical pulses) that is appropriate for transmission through the channel. It can incorporate many things – including control of the low frequency spectral content of the signal to be recorded/recovered (also known as DC control), embedding of timing information in the recorded signal, etc. The sequence of physical symbols, or channel symbols, output from the modulation process is input to a write circuit, which controls the formation of marks or other information-bearing features on the optical media. The servo systems ensure that the disk is spinning at the correct speed, the laser spot is in focus and on track, and the laser power is correct. During the read process, a physical signal is produced that represents the information recorded on the storage medium. The read side portion of Figure 1 uses a variety of signal processing methods to extract the information as reliably as possible from this signal. Note that in some systems, particularly the multilevel systems, a new block has been introduced into the chain – precoding (see block diagram in Figure 7). This block adds an additional layer of coding to improve system performance in terms of density or margins without any physical changes to the optical system or the media. In what follows, we shall highlight the state of the art in signal processing, modulation and coding in optical data storage systems.

ECC Write Modulation Encode Circuitry

Servo Media

ECC Read Demodulation Decode Circuitry

Figure 1. Block diagram of the signal processing and coding elements of a typical optical data storage system.

3.5.1 Signal Processing and Coding Technology Today

3.5.1.1 Error Correction Coding

Typically, error correction codes (ECC) are designed to convert raw bit error rates (BER) of the order of 10-3 (at the input of the ECC decoder) to a substantially lower corrected BER of the order of 10-14, or better. Reed Solomon (RS) error correction codes have been the ‘gold standard’ ECC for more than 25 years; they are used universally in systems such as CD and DVD, and will find application in next generation systems such as Blu-ray Disc and beyond. The RS ECC implementations employed in such systems use interleaving to scatter the symbols that comprise any single RS codeword widely over the storage medium’s recording surface. (Note: the symbols that are encoded, or protected by, a RS ECC are comprised of multi-user-bit words, e.g., in the CD and DVD systems, such symbols are eight-bit bytes.) Impairments, such as disc surface scratches, dust, system track-following perturbations, etc., will cause a contiguous block of recovered channel symbols to be contaminated (i.e., produce erroneous data upon demodulation); interleaving therefore causes the ECC symbols obtained from a contiguous sequence of recovered channel symbols that are corrupted by one single impairment event to become spread over several ECC codewords. Such long impairment events are called burst errors. RS ECCs also have the ability to correct short (one or two ECC symbols in duration) randomly occurring errors. The trend has been toward using longer RS ECC block sizes (codewords); this is facilitated by the steadily increasing processing power available in low-cost integrated circuits. The general trend of using RS ECCs is likely to continue, although some research in the area of turbo-like codes is beginning to show promise. Turbo-like codes include the classes of turbo codes (serial and parallel concatenation), turbo- product codes, and low-density parity check codes. While turbo-like codes by themselves are error-correcting codes, they are generally being proposed for use in configurations (described in more detail later) where they augment the primary Reed Solomon code. For example, such codes are used in conjunction with partial-response maximum-likelihood (PRML) detection architectures to reduce the absolute number of erroneously recovered ECC symbols that are sent from the demodulator to the RS ECC decoder.

3.5.1.2 Modulation

Modulation is the translation of logical digital information to a sequence of physical symbols (called channel symbols) that are appropriate for the channel. In particular, the physical representation will be designed to augment the reliable transmission of the channel symbols through the channel. For example, the spectrum of the resulting signals (that are output by the modulator) will ‘match’ the bandwidth (DC, mid-range, and high frequency transfer characteristics) of the channel.

Run Length Limited Modulation

Run-length limited (RLL) modulation has been a mainstay in optical recording systems like CD and DVD and will continue to appear in future systems like Blu-ray Disc. The basic idea is (i) to increase the channel storage density and transmission (write/read) bit rate by representing several user data bits by each channel symbol and (ii) to introduce a constraint, namely limiting the range of lengths of the channel symbols, so that intersymbol interference can be controlled and minimized (i.e., so that the sequence of recorded channel symbols produce a write/read signal which has a bandwidth that fits nicely within the bandpass of the recorder channel). A particular RLL modulation process is defined by two parameters, d and k, which specify the minimum and maximum pulse lengths in the sequence of electrical pulses which comprise the pulse length modulated electrical waveform used to physically represent the stream of user data to be stored. The individual pulses of this waveform are comprised of an integer number of channel bits (also known as channel bit clock periods or timing intervals); the shortest RLL pulse is comprised of d+1 channel bits while the longest pulse contains k+1 channel bits. More specifically, each distinct pulse in this channel symbol waveform corresponds to a distinct run in a binary sequence called the RLL channel sequence (a channel sequence run comprised of m channel bits will contain a ‘one’ and exactly m-1 contiguous following ‘zeros’, see Figure 2). CD and DVD both use d = 2, k = 10 RLL modulation which maps user data to a pulse length modulated electrical signal with rates of 8/17 and 8/16 user bits/channel bit, respectively (we note that the DVD implementation has ~ 6% increased efficiency due to its employment of a modulation algorithm that is significantly more complex than that used in the CD system). The length of a single channel bit on a CD is ~ 0.27 µm; the channel bit length on a DVD is ~ 0.13 µm. RLL modulation also serves to control (i.e. substantially attenuate) the DC content of the channel symbol signal (i.e., the pulse length modulated write/read waveform) with the purpose of reducing interference with other signals (such as servo signals) that are embedded in the low- frequency portion of the composite readout signal that is obtained when a CD or DVD is read. The Blu-ray Disc specification calls for RLL modulation that exhibits a d = 1, k =7 RLL constraint that offers a higher rate (i.e., rate 2/3 user bits/channel bit), but also has shorter minimum length pulses in the channel symbol waveform (the channel symbol waveform produced by this modulation, called 1,7 Parity Preserving modulation, or 1,7 PP, has significantly diminished low frequency spectral content relative to the 1,7 RLL modulation employed in magnetic HDDs). The smaller d constraint can lead to greater intersymbol interference, which can be handled by partial response maximum likelihood (PRML) processing at the demodulator (discussed in the next section). In such situations, the RLL modulation and PRML demodulation processing are designed to function together.

0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0

Source: D. Howe, University of Arizona

Figure 2. A d = 2, k = 10 binary RLL channel sequence containing five completed runs of length 3, 6, 4, 3, and 11 channel bits respectively (top). The corresponding binary RLL waveform (center) and marks (bottom) recorded when the high levels of the RLL waveform are used to turn the recording laser ‘on’ are also shown.

Multilevel Run-Length Limited Modulation

Conventional RLL modulation produces a “binary” channel symbol waveform. That is, the pulses of varying length have one of only two possible amplitude levels. This is useful when the optical storage medium supports marks that have only two distinct reflectivities, e.g., as do the pits and lands employed in CD-ROM and DVD-ROM. However, if the storage medium can be recorded with marks that exhibit more than two well-defined reflectance levels (when the medium is subsequently read), as well as discretely variable lengths, then modulation symbols that have discretely variable length and amplitude can be used. This type of modulation is called multilevel run-length limited (ML-RLL) modulation. ML-RLL modulation that uses channel symbols having only three distinct amplitudes can have a rate that is 50% higher than binary RLL modulation with similar d,k constraints. ML-RLL is discussed in more detail in Section 3.5.1.5.

3.5.1.3 Equalization and Detection

Equalizers are aimed at either removing or controlling the ISI in the recovered (via readout) analog channel symbol signal without causing significant noise enhancement. Detectors try to extract discrete channel symbols (e.g., via an analog to digital conversion process) from such equalized analog signals or samples of such signals. At the read side, an equalizer would be part of the ‘read circuitry’ shown in Figure 1. In systems like CD, the minimum recorded mark size is sufficiently large that the ISI could largely be ignored (namely, treated like noise). In systems like DVD, the situation is largely the same – it is possible to treat the ISI like noise, but because of smaller marks and tighter track pitch, the ISI is greater. Note: ISI can be considered to emulate noise only if it causes randomly selected channel symbols to be occasionally demodulated into incorrect channel symbols.

Partial Response Maximum Likelihood (PRML)

The most popular and powerful type of equalizer/detector combination that handles ISI is the so- called partial response (PR) equalizer and maximum likelihood (ML) detector (together, these comprise a PRML receiver). The basic idea of PRML is that instead of equalizing the channel to completely remove the ISI, PRML equalizes the channel (through some combination of analog and digital filters) to some predefined ISI target. The most commonly used targets are of the form H(D) = (1+D)n, where n is some integer and the coefficients of the various terms of the polynomial form of H(D) give the ISI coefficients associated with contributions from neighboring symbols. For example, common ISI coefficients are (1,2,1) and (1,2,2,1). In contrast to PR equalizers, full response equalizers (such as those employed in the CD system) attempt to

shape the playback channel such that its impulse response function has essentially all of its energy confined to a time interval (or spatial dimension interval) that has length equal to one channel bit. This means that when the channel ‘reads’ the storage medium, thereby “creating” the playback analog channel symbol waveform, each contiguous portion of that waveform is formed by taking an average of an interval of recorded data track (on the storage medium) that has length equivalent to one channel bit. If, instead of considering the time axis of the analog channel symbol waveform to be continuous, we plot that waveform against a discrete time base, where the discrete time unit used is one channel bit, then we can consider the impulse response of the full response channel to be ….0,0,0,1,0,0,0,… (a shorthand notation for this is simply 0,1,0, which is taken to mean that the response is zero within all non-specified time intervals). That is, the full response channel only “sees” one single channel bit worth of storage medium at any instant. A partial response (PR) channel, on the other hand, will have a playback impulse response that is several channel bits in length. For example, a 1,2,2,1 PR channel will “see” exactly four channel bit intervals worth of storage medium at any instant, and when it produces the analog readout channel symbol waveform it will weigh the input from the center two of the channel bits of storage medium (out of the four that it “sees”) with twice the gain that it gives to the inputs from the two outer channel bit intervals. In general, longer (in terms of channel bits) PR impulse response functions (i.e., more complex PR targets) will handle higher levels of ISI (i.e., smaller marks can be read from the storage medium), but will produce recovered channel symbol waveforms that have a greater number of allowed discrete amplitude levels; such waveforms require the use of more complex detectors to recover the sequence of channel symbols during the demodulation process. And, due to the ISI of the additional amplitude levels produced by the PR equalization in the playback analog channel symbol waveform, the demodulated channel’s symbols will exhibit higher error rates, since the multi-amplitude-level analog channel symbol waveform exhibits an effectively reduced signal-to-noise. However, since PR equalization does not cause as strong an enhancement in higher frequency noise as does full response equalization, the resultant PR channel symbol signal exhibits a reduced level of baseline noise (~ 2 to 3 dB) relative to the full response channel. Once the channel has been equalized to the given PR target, it can be viewed as a finite state machine for which the Viterbi algorithm can be used to demodulate the channel symbols. If the equalized signal contains no residual (i.e., unexpected) nonlinearities, a Viterbi maximum-likelihood detector can find the channel symbol that is most likely recorded on the media. The proper choice of a PRML target is a design issue that depends on the channel transfer function and noise properties. A recent paper from NEC shows that 34 GB of capacity on a 120-mm disc is possible with a blue laser and NA = 0.85. To accomplish this, a read channel that employs PRML with target (1,2,2,2,1) and a write compensation technique are combined [1].

Adaptive Equalizers

As optical disc systems increase their recording density, the systems suffer from signal degradations due to ISI and ITI. To tackle these signal degradations, equalizing/detection technologies like PRML are used. Under optimum conditions, these equalization/detection technologies work well. However, when there is dynamically variable degradation of the receiver, such as degradation of the reading optical spot caused by disc tilt or defocus, the transfer function (i.e., the impulse response) of the system changes and the PR equalization process may systematically fail since it no longer will produce the desired target (impulse response). Conventional optical disc systems, such as CD or DVD, cope with these system degradations by reducing the recording density and providing wide system margins so that the equalizer performance is not pathologically degraded, even under the worst operating

conditions. Recently, however, adaptive signal processing technologies have been reported that enable the optical disk systems to maintain playback performance without decreasing capacity. Examples include adaptive equalizers with PRML [2,3,4], a 2-D adaptive equalizer with PRML [5,6], and adaptive control of the predicted value in PRML (i.e., the finite state machine configuration used by the maximum likelihood detector) [7,8].

Limit Equalizer

Interpolate Limiter ,c ,c ,c

-K K K -K

High-Boost Filter

Conventional IN EQ 1.5,c OUT

The Blu-ray Disc system was designed to use 1,7 PP binary RLL modulation and PRML playback equalization. However, an adaptive implementation of PMRL is required to maintain wide system margins at high storage density. The limit equalizer [9,10,11] in combination with a conventional equalizer (i.e., a full response, or nearly full response equalizer) enables the achievement of high storage density (via the reduction of ISI) and at the same time shows the same signal-to-noise ratio (SNR) improvement as PRML. Figure 3 shows the block diagram of the limit equalizer.

Source: Pioneer

Figure 3: Block diagram of the limit equalizer.

The limit equalizer is a nonlinear digital finite impulse response (FIR) filter that conditions its output in terms of the current data samples – if one of the current data samples is above or below a threshold, its value is limited (i.e., set) to some maximum or minimum value. This has the net effect of suppressing some of the equalizer inputs (e.g., the relatively high amplitude long pulses contained in the recovered analog channel symbol waveform, as well as low frequency noise), if they have too much effect on the output, while directly passing the other inputs (e.g., the short two-channel-bit-long pulses in the recovered analog 1,7 RLL waveform).

The optical disc system has a low-pass frequency response, which is derived from the modulation transfer function of the pickup head. The output from the pickup head passes through a preamplifier and automatic gain control stage before being input to the limit equalizer.

Figure 3 shows a typical limit equalizer setup - a conventional equalizer is used first and its output is processed further. The gain of the conventional equalizer is set to the optimum gain to cancel ISI. After the conventional equalizer, a transversal filter-type equalizer is used to boost high frequencies and suppress the high amplitude low frequencies. This is accomplished by placing a limiter in this side path. The limit level of the side path filter is chosen to be just above the peak level of the 2T signal (highest frequency signal). However, the lower frequencies (below a 3T frequency), which are not suppressed by the optical transfer function of the pickup head, are clipped by the limiter. Thus, the limit equalizer has high gain at 2T frequency, but its gain decreases at lower frequencies.

The limit equalizer is a kind of non-linear equalizer, but the relationship of the recovered symbol error rate to the jitter in the recovered analog channel symbol waveform is the same as that of the conventional equalizer (this is not the case with PRML, in which symbol errors increase very rapidly if system tolerances such as disc tilt and optical head defocus are exceeded (see Section 3.1.1). If we set a digital to analog (D/A) converter after the limit equalizer, then we can monitor RF signals and we can measure jitter (which cannot be directly measured at the output of a PR equalized channel). Because the limit equalizer’s operation is similar to that of a conventional full response equalizer, it is useful not only for the signal detection, but also for channel-bit timing recovery via a phase locked loop (PLL).

3.5.1.4 Servos

Recent years have seen substantial changes in the way servos are implemented in optical storage devices. Early optical storage devices used primarily analog servos that were controlled or supervised by relatively low powered microprocessors. Early optical drive servos generally included preamplifier electronics, analog circuitry to generate the focus and tracking error signals, analog circuitry to implement servo compensation, such as lead/lag or proportional, integral, derivative (PID) controllers, and analog power amplifiers to drive the mechanical actuators.

With the advances in integration and digital signal processing (DSP) technologies, current optical drive servos are moving more and more towards all-digital implementations with all the signal processing and servo compensation done in digital hardware and/or DSP firmware. A state of the art optical drive servo today consists of a simple analog preamplifier (which is often integrated on the same silicon as the photodiode detectors), an array of analog to digital converters that convert the signal from each photodiode’s preamplifier to the digital domain, a DSP processor (running at 80 MIPS or more), and power amplifiers with built in digital to analog converters that have a digital interface to the DSP. The algebra that generates the focus and tracking error signals from the individual photodiodes is implemented in DSP firmware. The servo compensators can be full state space designs, including state space estimators and very sophisticated error detection, defect detection, and error recovery routines, all implemented in DSP firmware.

The DSP firmware also includes complex routines for allowing the servos to calibrate themselves, including optimizing loop gains and offsets and minimizing effects of imperfections in the optical and mechanical systems (like tracking into focus crosstalk and mechanical resonances). These adaptive routines have been instrumental in allowing lower cost optical pickup units and lower cost mechanical actuators. On the production line, the servo firmware literally trains itself to a particular optical-mechanical assembly. In the consumers’ hands, the servos tune themselves in real time to the particular piece of media that is being played.

As levels of integration continue to increase and embedded DSPs continue to get faster and have more computational power, optical drive servos will continue to get “more digital”. The DSP firmware routines will continue to add more and more sophistication, which will enable them to handle even lower cost optical pickups and mechanical actuators, work with a larger variety of discs, and deliver higher and higher performance in terms of spin speeds and data rates.

3.5.1.5 Introduction to Multilevel Storage Recording

Multilevel recording offers the advantage of increasing linear storage density without changes to the optical and mechanical parts of the reader/writer or to the industry infrastructure for making discs. Since the last NSIC optical storage roadmap [12], multilevel recording has gone from a novel idea to a fully implemented system [13]. It has become a separate field within optical data storage with industry investment, university research effort, and many conference publications. The multilevel systems currently being pursued are Calimetrics ML, SCIPER-RPR, ML-RLL, and Ricoh systems.

What Is Multilevel Storage Recording Technology?

Multilevel storage technology offers the potential of increasing the storage capacity and data transfer rate by encoding information in the amplitude of the signal. Multilevel systems do not require any changes to the optical and mechanical components of the storage system. There are essentially three categories of physical methods to increase the data density:

• smaller marks (e.g., transition from CD to DVD to Blu-ray Disc or AOD formats)

• more bits/mark (e.g., multilevel)

• use of the third dimension (e.g., multilayer or holographic)

It should be noted that these three methods of increasing data density are complementary and can be used in combination with each other to increase the overall capacity of the system.

Multilevel marks can be written using the same binary material processes of the standard CD and DVD systems as well as future blue laser systems. For dye-based writable and phase change rewritable discs, the sizes of the written marks are precisely controlled in a multilevel system. Because the spot of the reader is larger than the binary marks on the material, the readback signal returns an intermediate signal level proportional to the size of the mark (or the fill factor of the mark) within the reader spot. Similarly in ROM, a multilevel mark is mastered using either photoresist or non-photoresist technologies by precisely controlling the exposure times of the writing laser, which results in a mark-size/depth modulation. Again, the readback signal returns an intermediate signal level proportional to the size/depth of the mark under the reader spot. The printing of pictures using a laser printer is a common analogous system where the proportion of black (ink) to white (paper) achieves different gray levels in the response

function (Figure 4).

Source: Calimetrics

Figure 4. Multilevel response is achieved using a binary medium by mark size modulation. This is similar to gray- scale printing using a laser printer or newspaper printing.

Because a multilevel signal (Figure 5) involves signal levels with a smaller amplitude change than the binary system, it is usually assumed that the SNR of the system is decreased. In a sense this is true; the differences in signal amplitude between one level and another are smaller. However, in optical systems the SNR of the system is typically greater than the required SNR for the system to work at manufacturing margins, given better signal processing. Specifically, Calimetrics has shown that their ML system, when applied to CD, DVD, and blue- laser based systems, achieves the same margins, while at the same time substantially increasing capacity [14,15,16].

Source: Calimetrics

Figure 5. Example of a multilevel signal showing 8 different signal levels. This is an example of a type of level calibration pattern written and read on the ML-CD 2 GB system of Calimetrics.

Multilevel System - Calimetrics

In the Calimetrics multilevel system, recording is accomplished by writing variable size marks within a sequence of fixed size “data cells” that form a spiral data track on the disc. In various implementations, from 8 to 12 distinct amplitude levels are discriminated when such data cells are read. Figure 6 shows a simplified diagram of a track for a (re)writable (R and RW) and read only (ROM) multilevel disc. Calimetrics has implemented its MultiLevel (ML) technology to achieve a 2 GB ML system by combining its ML Encoder/Decoder chip and ML-specific firmware into a base drive that maintains its standard CD-R/RW capabilities and margins [13]. Additionally, the same chip can increase DVD (re)writable disc capacity to 7 GB when combined with a 0.65 NA DVD RW base drive [15]; to 20 GB if combined with a DVD-like system using a blue laser [17]; and to approximately 35 GB when combined with higher NA optics and thinner disc covers [16]. A next-generation chip or core will use Calimetrics’ new 12-level high-density multilevel (HD-ML) encoding technique [18] that, together with improved format efficiency, can increase DVD capacity to 10 GB [15]. HD-ML can also provide 25 GB per layer on a DVD- based system with the addition of a blue laser and 50 GB per layer in combination with a blue laser, higher NA optics and thinner disc covers.

© 2003 Information Storage Industry Consortium – All Rights Reserved INSIC International Optical Data Storage Roadmap Reproduction Without Permission is Prohibited August 2003 12 Conventional Optical RecordingGray-Scale Recording1234...MROM - Pit DepthR & RW - Shades of GrayROM, R, & RW - Binary

Source: Calimetrics

Figure 6. A representation of gray-scale recording marks for R and RW materials is shown above the depth profile along the center of a track of a gray-scale ROM disc with pit depth modulation.

Figure 7 shows a block diagram for the implemented multilevel system. On the encoding side, data is error-correction encoded using a Reed-Solomon Product Code (RS-PC). The RS-PC adds 5 bytes of inner and 16 bytes of outer parity to 32 KB of data. Unlike CD encoding, this ECC block is not interleaved with other blocks. Each block is stored on the disc as an independent unit. Next, the modulation encoder convolutionally encodes the data (and ECC parity bytes) by taking 5-bit groups and generating an additional bit to make a 6-bit group. These 6-bit groups are then mapped into two 8-level ML symbols. This modulation encoding provides additional error correcting capabilities that allow more effective use of the SNR of the optical data storage system. This modulation encoder is actually a combination of a precoder and a modulator (see also Section 3.5.2.3). After modulation encoding, the stream of ML symbols is placed into a complete block structure. Each block is a separate data unit because it contains its own timing, gain control, address, level calibration, and equalizer adaptation information. Therefore, this block can be written and read independently. Further, the gain control, level calibration, and equalizer adaptation subsystems enable interchange between different drives by removing effects due to mechanical and optical drive differences. Issues arising from disc defects and consumer abuse are also addressed by these subsystems. There are also subsystems to synchronize within the data block and to control the DC content of the ML signal. Last, the ML symbols are converted by the write strategy into laser pulses that write the ML marks on the disc. The write strategy is developed by a precompensation system and optimum power system that correct for non-linearities of, and writing power-level requirements for, each individual drive/disc combination, respectively.

Data

RS-PC ECC RS-PC ECC Encoder Decoder

Viterbi Modulation Decoder

MCM Modulation Encoder Deformatter

Optimum Power Calibration Adaptive Equalizer

Block Format Assembler Digital Desnaker

Precompensation Iteration Procedure A/D Converter & Timing Recovery

Write Pulse Strategy Encoder AGC

Power Control Optical Pick -Up Focus & Tracking Servos

Spindle Control Optical Disc

Source: Calimetrics

Figure 7. Data flow in the multilevel encoding and decoding system. Data is ECC-, modulation-, and format- encoded before being translated into laser pulses that write data on an ML disc using the laser in the optical pickup. For decoding ML signals, the all-sum signal (RF signal) is processed by setting the gain, converting from analog to digital, recovering the timing, adjusting for amplitude and offset (desnaking), equalizing, and finally decoding for format, modulation, and ECC. Spindle, tracking, focusing, and laser control systems are all similar to existing optical disc systems. (MCM: multilevel channel modulation.)

On the decoding side, the disc is read using the all-sum signal from a standard optical pickup unit (OPU). After this signal passes through a gain-controlled amplifier and is digitized, timing is recovered using marks in the preamble of the data block. This timing information is used to sample the all-sum signal to produce a digital stream at twice the ML symbol rate. Timing and gain/offset are maintained by using a series of marks that provide both a clean signal-edge for clock recovery as well as minimum and maximum signal levels for envelope monitoring. The digital processing of the multilevel signal begins with a fine adjustment of the gain and offset using measurements of the envelope (desnaking). The ML signal is then equalized by an 11- tap fractionally-spaced adaptive equalizer. The taps are trained at the beginning of each block using an equalizer adaptation pattern. Equalization of the signal removes the intersymbol interference caused by the interaction of the readout spot with the disc. After equalization, the deformatter removes the non-data marks and adjusts the signal according to the DC control system. The deformatted signal and level information (measured from the level calibration pattern in the preamble) is then used by the 256-state Viterbi decoder to recover the multilevel symbols. The data stream is then RS-PC decoded to produce the original 32 KB data block.

Two Dimensional Partial Response - National Project of Japan

In Japan, a national project, named Nanometer-Controlled Optical Disk System, was established in 1998, aiming for 100 Gb/in2 of ROM recording density by the end of 2003.

In this project, a specially designed electron beam recorder is being developed for making a master of optical ROM discs with very high resolution as well as very high pit-edge-position accuracy. For the recording method achieving the density goal by utilizing this position accuracy, a multilevel recording method called Single Carrier Independent Pit Edge Recording- Radial direction Partial Response (SCIPER-RPR) was originally proposed [19]. In this method, positions of leading and trailing edges of a pit are displaced with a stepwise function. To increase the recording density, a precoding technique PR(1,1) is applied to the across track direction. (i.e., pit-edge position is determined according to the estimated crosstalk from the adjacent track.)

This SCIPER-RPR method was further improved by Pioneer to become a 2-dimensional PRML method [20]. In this improved method, each edge of a pit records independent information in 3 discrete steps. We illustrate the configuration of the encoder (Figure 8(a)) and the decoder (Figure 8(b)) of this 2-dimensional PRML method. We also illustrate the resultant pit configuration for an areal density of 40 Gb/in2 (Figure 8(c)). The encoder is the first step (shown in Figure 8(a)), where the recording information is converted to a 3-level signal Ve(n). This 3- level signal is then converted to another 3-level signal We(n) by the modulo 3 subtraction circuit. (This modulo 3 subtraction takes care of the three pit-edge-positions which are adjacent to the recording pit-edge position.) The resultant 3-level signal is mastered to form a pit sequence with 3 discrete steps of pit-edge positions.

For readout, contributions from four edges (shown inside of a circle in Figure 8(c)) constitute a playback signal of one of nine levels. In the decoder configuration shown in Figure 8(b), the playback high frequency (HF) signal from the optical pickup is low-pass filtered and sampled by an analog to digital (A/D) converter. Then adaptive equalization and Viterbi decoding are performed. This configuration of the readout is basically the same as that used in a conventional DVD decoder. The only difference is that the Viterbi decoder is designed for a multilevel (9-level) signal input. This simple decoding was made possible by the modulo-3 subtraction performed in the encoder. Assuming a 0.85 NA objective lens, 0.1-mm thick cover layer and a blue 405-nm laser diode, this playback HF signal of 9 levels has been confirmed by an optical simulation. Experiments to confirm an actual eye-pattern are also planned in the project.

(a) 2-dimensional PR Encoder block diagram

Pit edge position by the current data Ve(n) Pit edge position actually recorded We(n)

Recording 3-level Pit edge

information modulation modulation Z-1 +

+＋ Mastering machine + Z-1 1 track delay

(FIFO) memory) (b) 2 dimensional PRML Decoder block diagram

Analog Adaptive Multilevel Viterbi Retrieved HF signal LPF ADC Equalizer decoder information

Sync signal Timing PLL detector generator

Source: Pioneer

Figure 8. A multilevel recording method (2-D PR) used in the nanometer-controlled optical disk system. The configuration of the encoder (a) and the decoder (b). The pit configuration (c) for the areal density of 40 Gb/in2.

Multilevel Run Length Limited Recording - University of Arizona Multilevel run-length-limited (ML-RLL) recording utilizes marks that (i) have several distinct lengths along the direction of the data track and (ii) produce more than two distinct signal levels when read. [Note: the discussion of the ML-RLL modulation technique found herein assumes

that the technique is used in a full-response equalized recording/playback system (see Section 3.5.1.3). It may also be possible to apply ML-RLL modulation in a PR equalized channel.] This is very similar to conventional run-length limited (RLL) recording which employs two different types of marks (e.g., pits and lands) that have several distinct lengths; such conventional RLL modulation is really two-level (or binary) RLL modulation. Conventional (binary) RLL playback signals produced by full response channels are comprised of pulses having one of two possible amplitude levels. If the recorded variable-length marks produce a full-response read signal comprised of pulses that have 3 (or 4) distinct amplitude levels, then the recording is a ternary (or 3-level) run length limited (3L-RLL) or quaternary (or four-level) run length limited (4L-RLL) scheme. ML-RLL modulation has been previously discussed in the technical literature [21-24].

First, we shall consider three-level, or ternary (as opposed to binary) RLL sequences (see the top parts of Figures 2 and 9 for examples of d = 2, k = 10 binary and ternary RLL sequences, respectively). Ternary sequences are comprised of three distinct digits, 0, 1 and 2 instead of just two (i.e., 0 and 1). Also, in a ternary RLL sequence there can be two separate and independent sets of d,k constraints; there must be at least d1 “zeros” following every “one”; at least d2 zeros following every “two”; and there are at most k1 and k2 zeros, respectively, following each “one” and “two”. In an analogous fashion, 4-level, or quaternary RLL sequences, which consist of the distinct digits 0, 1, 2 and 3, will have three sets of d,k constraints that specify the minimum and maximum number of “zeros” that may follow each “1”, “2” and “3” respectively. In the following, we shall refer to a code that transforms a binary user data sequence into a ternary or quaternary RLL channel sequence as a multilevel run-length-limited (ML-RLL) code. Ternary (3L-RLL) and quaternary (4L-RLL) codes are specific instances of ML- RLL codes. We note that multilevel RLL sequences that consist of five, or more, distinct digits exist. Here, however, we will limit our attention to ternary and quaternary ML-RLL modulation, which, in our opinion, are most easily applied to practical conventional (full response equalized) optical data storage systems. ML-RLL modulation codes have been discussed previously in the technical literature [14,18,21,25].

1 0 0 0 1 0 0 0 0 0 2 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0

Source: D. Howe, University of Arizona

Figure 9. A d1=d2= 2, k1=k2= 10 ternary RLL channel sequence containing five complete runs of length 4, 6, 4, 3 and 11 channel digits respectively (top). The corresponding pulse length modulated (PLM) ternary RLL waveform (middle) and marks recorded when the high levels of the PLM ternary RLL waveform are used to turn the recording laser ‘on’ (bottom). The amplitudes of five pulses in the PLM ternary waveform are (from left to right): a1= 1, a2=

(a1+1)mod 3 = 2, a3= (a2+2)mod 3 = 1, a4= (a3+2)mod 3 = 0 and a5= (a4+2)mod 3 = 2.

The theoretical capacities, C(d1, d2; k), and maximum efficiencies Em(d1, d2; k) of several ternary RLL sequences under the conditions d1 ≤ d2, and k1 = k2 = k are listed in Table 1. Here C(d1, d2; k) specifies the maximum possible rate of the specific 3L-RLL modulation technique (i.e., the maximum possible number of user data bits that can be represented per channel bit of the modulation symbol sequence). And, the recording efficiency Em(d1, d2; k) specifies the maximum number of user data bits that can be mapped to a minimum length channel symbol (i.e., the shortest pulse length) in the pulse length modulated waveform that is the physical realization of the channel symbol sequence produced by the corresponding 3L-RLL modulation technique. A practical 3L-RLL modulation system may exhibit rate < C and recording efficiency < Em constrained by the fact that these values must be equal to rational fractions having relatively small denominators (less than 30 or so) in order to limit any error propagation in the user sequence that is obtained via erroneous demodulation. Thus, from Table 1 we see that practical 2, 2; 10 ternary RLL modulation can exhibit a rate of R = 0.75 (= ¾ = 12/16, etc.) user bits per channel bit and recording efficiency of 3R = 2.25 user bits per minimum length recorded mark. This represents a 50% increase in the rate and recording efficiency over the 2, 10 binary RLL modulation employed in the DVD system (which exhibits rate R = 0.5 = 8/16 and 1.5 user bits per recorded 3T mark).

Table 1. Capacity and maximum efficiency of several ternary RLL sequences.

Source: D. Howe, University of Arizona

Em(d1, d2; k) = (d1+1) d1, d2; k C(d1, d2; k) × C(d1, d2; k) 0, 0; 3 1.5726 1.5726 0, 0; 7 1.5848 1.5848 0, 1; 7 1.2707 1.2707 1, 1; 3 0.9255 1.8510 1, 1; 7 0.9962 1.9924 1, 1; 8 0.9981 1.9962 1, 2; 7 0.8714 1.7428 2, 2; 4 0.6719 2.0157 2, 2; 7 0.7470 2.2410 2, 2; 8 0.7533 2.2599 2, 2; 10 0.7589 2.2767 2, 3; 7 0.6719 2.0157 3, 3; 5 0.5298 2.1192

3, 3; 8 0.6058 2.4232

The ML-RLL channel symbol waveform is directly produced from the ML-RLL channel sequence, as shown in Figure 9. Each distinct pulse in this channel symbol waveform corresponds to a distinct run in the ML-RLL channel sequence (a channel sequence run comprised of m channel bits will contain a lead non-zero digit and exactly m-1 contiguous following zero digits). More specifically, the length of each pulse is exactly equivalent to the number of channel bits in the corresponding channel run, and the amplitude is derived from the value of the “non-zero” leading numeral of the corresponding channel run (i.e., either a “one” or a “two” in the case of ternary RLL). For example, the pulse amplitudes of the 3L-RLL channel symbol waveform are derived as follows (see Figure 9): suppose the next succeeding channel run starts with the numeral ai, where ai = 1 or 2, then relative to the amplitude of the current pulse, the next (succeeding) pulse will have amplitude ai mod 3. Thus, each pulse will have one of the discrete amplitude levels “0”, “1” or “2”, and that amplitude level is determined by a cyclic change of either “1” or “2” levels relative to the amplitude level of the current pulse. The experimental recording and playback of ternary RLL waveforms on/from phase change optical disks was reported at the Optical Data Storage 2003 conference [26]. ML-RLL modulation has several other interesting features. One of these is that detection of both the amplitude levels and pulse lengths of the playback signal are self-referencing. The latter is due to the fact that the detection channel bit clock may be derived directly from the playback signal via a phase locked loop/voltage control oscillator (VCO) (similar to what is done in conventional binary RLL). Pulse amplitude level self-referencing is possible because the ML-RLL pulse length modulated waveform is constructed by cyclically changing the amplitude of each distinct pulse relative to the amplitude of the immediately preceding pulse. For example, as shown in Figure 9, the amplitude of a new (succeeding) pulse will change by one or two levels (cyclically, relative to the current pulse amplitude level), depending on whether the multilevel channel sequence run that corresponds to the new pulse begins with a “1” or a “2”. This means that the full response equalized ML-RLL playback signal can be discriminated via a relatively simple hard decision A/D converter that uses its current state as a reference.

Due to the similarity of binary RLL and ML-RLL modulation, at a high level the signal processing employed by the two schemes in a full response system will be much the same (Figure 1 presents such a high level block diagram). At a lower level, specific elements such as the write strategy used to cause various (user data dependent) mark patterns to be optimally recorded, the equalizer used to condition the recovered channel symbol waveform and the data detector that has more than one slicing level will be different, although these differences may not be large in the case in which there are only a minimal number of amplitude levels in the recovered channel symbol waveform (e.g., 3L-RLL). For example, the complexity of a 3L-RLL demodulator may be substantially less than the demodulators employed in a simple binary RLL PRML equalized system; the equalizer would be required to limit ISI while maintaining good separation between the three allowed signal amplitude levels and the detector would be a simple state machine that only needs to discriminate via two slicing levels that are referenced to the amplitude level of the previous pulse sample (which must be held in memory).

Lastly, it may be possible to introduce metric distance into the sequence of amplitude levels of the ML-RLL channel sequence waveform by using some type of precoding (see Section 3.5.2.3; this implies that only certain sequences of pulse amplitudes may occur in the channel symbol waveform created by the modulation process). With a full response equalized channel, such a

system, when combined with a maximum likelihood (Viterbi-like) detector to demodulate the sequence of channel symbol amplitude levels, may enable additional increases in storage capacity (above that already provided by ML-RLL modulation) if an effective enhancement of the signal-to-noise ratio of the recovered channel symbol waveform can be obtained (i.e., assuming that the rate of the ML-RLL channel sequence is not appreciably reduced by the precoding process, then density would be increased because the precoding/ML detection process would enable the system to tolerate increases in ISI and ITI that would be caused by the use of smaller recorded marks and/or smaller inter-track separation).

Data cell Recorded mark

Track pitch

Multilevel System - Ricoh

Ricoh’s multilevel recording is accomplished by writing variable size marks within a sequence of fixed size “data cells” that form a spiral data track on the disc. The reproduced signals from the disc are equalized and are separated into 8 levels. Figure 10 shows electron micrographs of the marks produced in the Ricoh multilevel system.

Source: Ricoh

Figure 10. The TEM images of multilevel marks for Ricoh’s DVD system. Recording cell length was 0.40 µm and track pitch was 0.74 µm. The recording mark length varies from 0 to 0.36 µm with 8 levels (0 to 8).

Ricoh’s multilevel recording approach uses a method called Data Detection using Pattern Recognition (DDPR) and a data modulation process called Least Significant Bit Limited Modulation (LLM). DDPR detects multilevel data using correlation between pre- and post-data. LLM is based on correcting least significant bit (LSB) errors, because most errors occur in the least significant bits of multilevel data. LLM modulates 11 bits of input data into four 8-level data sets (comprising 3 bits x 4 data sets = 12 bits) using a table that converts 3 bits (out of each 11 bits of the input data) into the 4 least significant bits in the 8-level data. LLM enlarges the Hamming distance of multilevel data and generates correlation between sets. Figure 11 shows the bit assignments and coding rules for the LLM scheme.

D0 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Set of modulated data (12 bits) S0 S1 S2 S3 3 bits 8 bits D3 D5 D7 D9 MSB Conversion 4 bits D4 D6 D8 D10 table L0 L1 L2 L3 LSB

Source: Ricoh

Figure 11. The modulation process of LLM. The 11 bits of input data are modulated to 12 bits, and each set (S0, S1, S2, and S3) is recorded at each cell.

Figure 12 shows the signal processing flow of data detection, coupling LLM with DDPR. The reproduced signal read from an optical disk is converted into a digital signal using an analog to digital (A/D) converter. The digital signal is equalized to remove intersymbol interference using a 7-tap equalizer. After equalization, DDPR and LL demodulation are processed concurrently. The LSBs of output data in a set from DDPR are checked to see whether they obey the LLM conversion rules. If they match the rules, the result of DDPR is output as detected data. If they do not match, the result of LL demodulation is output as detected data. The LSBs in a set can be used as error detection code. LL demodulation detects multilevel data according to conversion rules in LLM and the correlation between sets. Even if symbol errors propagate along several sets, DDPR is able to terminate the error propagation. DDPR and LLM compensate for each other’s faults. Further discussion can be found in the technical literature [27].

Check LSBs DDPR Playback A/D Equalizer signal converter Detected LL Selector converter data Demod- ulation

Source: Ricoh

Figure 12. Signal processing of data detection coupling LLM with DDPR.

The performance of this multilevel recording approach was evaluated by using a DVD optical pickup unit (wavelength 650-nm and numerical aperture 0.65) and a DVD+RW disc. The length of each cell was 3T of DVD (0.4 µm). The capacity advantage of multilevel recording was about 2X compared to the conventional DVD system.

3.5.2 Signal Processing and Coding Technology of the Future

In the following sections, we describe some future technologies in signal processing and coding: 1) the prospect of improved performance by applying turbo code-like rules to the decoding strategies employed by Reed Solomon ECC decoders, 2) the use of guided scrambling modulation to improve recording efficiency (i.e., the number of user bits per channel bit), and 3) the employment of precoding (i.e, adding an additional layer of coding) to improve system performance. We also include a discussion of 2-dimensional and 3-dimensional equalization and detection.

3.5.2.1 Error Correction Coding: Enhanced Reed Solomon Decoding

Turbo coding concepts have influenced the design of many ECCs. In this section, we discuss ideas that may lead to turbo-like decoding of Reed Solomon codes. A turbo coding system is comprised of two catenated convolutional error correcting codes (ECCs) that cause each encoded information symbol (hereinafter referred to as a bit) to be a constituent of two different convolutional codewords. Suppose that the two convolutional codes produce codewords that have lengths of n and m bits and which have Hamming distances of dn and dm bits, respectively. [Note: a code having Hamming distance dh can correct a maximum of t erroneous bits and x erased bits, where n, t and x satisfy the inequality 2t < (dh – 1 – x).] Such coding systems exhibit the property that their Hamming distance is equal to the product of the Hamming distances of the constituent codes (i.e., in this case, dh= dndm). However, in order to practically realize this maximum error correction performance (i.e., distance dh = dndm bits in a block of nm bits), the turbo code decoders (there are two of these; one for each of the two constituent convolutional codes) must decode each constituent codeword several times - and the decoders must exchange information about the reliability of the decoding action(s) that they individually performed (such exchanged information is referred to as soft decision information). The beauty of turbo codes is that they provide a systematic set of rules (also known as a decoding strategy, which specifies the maximum number of erroneous bits and erased bits that the decoder should attempt to correct in a received codeword) that should be applied to a specific received codeword, taking into consideration the soft decision decoding reliability information provided by previous decoding processes. We note that each constituent bit of a codeword that is received by the decoder will have appended flags that specify a statistical estimate of its current reliability, after it has been subjected to one or more decoding processes.

Reed Solomon (RS) error correction codes are distinguished by the facts that (i) the symbols that they encode are comprised of a multiple number of bits (rather than a single bit, e.g., the symbols encoded by the RS ECCs used in CD and DVD systems are eight-bit bytes), and (ii) the number of parity symbols (overhead) required to achieve a specified Hamming distance is less than, or equal to, that required by any type of known ECC. This makes RS ECCs ideal for application in optical storage systems, which exhibit storage media that have large defects that

contaminate long sequences of data, i.e., such defects produce long error bursts. If these error bursts are recognized and flagged by the playback electronics, they become erasure bursts. Two RS ECCs can also be configured as a catenated coding system. In fact, due to VLSI circuitry costs, such catenated (or product) RS ECC systems are preferred for optical data storage applications (e.g., both the CD and DVD systems employ catenated RS ECCs). Like the turbo codes mentioned above, each constituent codeword of these product RS ECCs must be decoded several times (and the decoders must exchange soft decision information) in order for the product RS ECC to realize its maximum error/erasure correction performance, i.e., to produce the actual error/erasure correction capability specified by the product of the Hamming distances of the two constituent RS ECCs. However, unlike the turbo coding systems, there is no systematic rule set that defines the decoding strategies that the decoders of a catenated RS ECC system must apply in order to practically realize the theoretical Hamming distance of the catenated RS ECC. The decoding strategies used in the CD and DVD systems are ad hoc strategies that are (presumably) devised to handle “worst case” error/erasure environments using only a single decoding cycle, i.e., each of the two constituent RS ECCs is decoded only once. This implies that the full power of the catenated RS ECC is not used or, equivalently, that the actual overhead of the implemented ECC system is higher than it could be. A more optimum situation would provide that the decoding strategies applied to a specific storage medium would be tailored to the “expected” error environment that affects a specific volume of medium. This would impart “turbo code-like” performance to catenated RS ECC systems. By performing research and development that leads to the design of decoding strategies that are optimized for a specific error environment and coupling this with means of adaptively estimating the error environment presented by a specific volume of storage medium, future catenated RS ECC decoders can effectively (i) extend the life of lower density optical storage media, and (ii) enable higher optical storage density on new storage media.

3.5.2.2 Modulation: Guided Scrambling

There is a constant push for more efficient methods to better control parameters such as DC content or channel spectrum. Guided scrambling is a technology that was invented in the field of optical fiber communication [28]. In guided scrambling, the source word x is converted to a selection set whose number of elements is L=2r, where r represents the number of redundant bits. Since this conversion is designed to be an invertible mapping, one can choose an element in the sets and transmit it to convey the same information as the source word x. By applying a rule for choosing the transmitting element, one can expect virtually any characteristic for the signal that is transmitted over the channel. Recently, K.A. Immink has introduced this guided scrambling technique to data storage [29]. This results in two innovations in the coding of optical data storage systems: 1) a new modulation code GS913, which theoretically achieves a 4.5% overall density increase compared with the same d constraint 1,7 PP code [30], and 2) improved lifetime of phase change media by using the guided scrambling to randomize the recording pattern written onto the media [31]. Because guided scrambling can be used to provide any characteristics to the recorded signal, the technology could be used in various ways in the future. For example, guided scrambling technology could be used to control the DC characteristics of a multilevel recording signal.

3.5.2.3 Precoding

Figure 13 shows the signal processing system components that have started to appear in systems (particularly multilevel systems, see for example the convolutional precoder in Section 3.5.1.5). As density increases and mark sizes decrease, this places increased burden on the signal processing to compensate for lower signal-to-noise ratios and higher nonlinear content in the read-back signal.

ECC Write Modulation encode Precode Circuitry

Media

ECC Read Un-precode Demodulation decode Circuitry

Figure 13. Block diagram for potential future systems. Notice the presence of an additional layer of coding, precode, and shared information among all the read side blocks.

The primary difference between current and future signal processing systems is the inclusion of soft processing, and feedforward and feedback connections between system components. The basic premise, motivated by the recent advances in turbo and “turbo-like” coding is that sharing reliability information across system components in an iterative fashion can result in remarkable improvements in system performance (like bit error rate). In the following, we describe each of the system components in more detail.

The read circuitry includes tasks such as automatic gain control (AGC), timing recovery, front end and digital equalizers. Those systems are likely to be very similar to current technology, with the possible inclusion of feedforward and feedback paths from other parts of the system. The demodulator incorporates algorithms such as a Viterbi detector matched to the partial response ISI target. In general, this box computes and passes aposteriori probabilities (APP) that are used by the channel decoder, which decodes the received words of an “inner” error control code such as a turbo code. This inner decoder also computes and passes these APPs to other parts of the decoder chain. Finally, an ECC decoder, much like that discussed in Section 3.5.2.1, uses hard and soft decisions.

In Figure 14, we provide an example of a future system that uses both PRML and turbo coding. Here, the channel precoder/modulation system is comprised of two interleavers (random permuters), a turbo-like encoder and precoder (we note that logical information, in the form of

digital data, is input to this composite sub-system while a physical representation of this input, in the form of a sequence of channel symbols, is output by it). By turbo-like code we mean one of several types of codes including turbo codes, turbo product codes and low density parity check codes. All of these codes share three things in common: 1) soft processing (based on shared APPs), 2) long blocklengths/diversity (where diversity means information is spread out over a very long period, usually by some random interleaving) and 3) iterative decoding. These are the basic tenets of turbo-like coding and virtually any system that has all three performs well. One or more of the interleavers can be eliminated depending on the code that is used. There is a feedback path from the turbo-like decoder to the PRML demodulator indicating the iterative nature of the symbol detection/decoding process. This basic structure can improve SNR by about 3- to 5-dB which, in one study, translates to about 30-40% increase in density.

ECC !1 Turbo-like !2 Precode & Write encode encoder Modulate Circuitry

Media

Read ECC !1 Turbo-like !2 PRML Circuitry decode decoder ‘decoder’

Figure 14. Block diagram showing an example of a future system containing a precoder and PRML decoder.

Before any new signal processing or coding techniques can be incorporated into a system, they must be cost effective; namely, the incremental cost of inclusion into LSI should be small (a few dollars at most). All of the ECC and modulation algorithms described above are well within the cost constraints of most systems (with data rates below about 100 Mb per second).

3.5.2.4 Other: Two- and Three-Dimensional Technologies

There are other signal processing issues and opportunities that we must consider. Some of these are unique to the particular systems. In the following, we discuss two developing technology arenas: 1) dealing with 2-dimensional signal processing and coding on a disc surface, and 2) dealing with 3-dimensional signal processing for holographic applications.

Two Dimensional Optical Storage (TwoDOS): European Project

The European Project “TwoDOS” (with 3 industry and 3 university participants) increases storage capacity on a disc surface by arranging bits on a two-dimensional lattice, and by applying joint detection [36]. TwoDOS stores information in a hexagonally packed stripe (or meta-spiral), which is mastered using e-beam technology and read with a multi-spot pickup head (Figure 15). Advanced signal processing is used to exploit the effects of the increased interference from neighboring bit cells, including two-dimensional coding, adaptive equalization, timing recovery, and bit detection. An essential difference from 1-D signal processing that uses cross-talk cancellation is that TwoDOS applies 2-D “joint detection” of all the signal energy-per- bit. TwoDOS expects to increase the density of the base system (Blu-ray Disc) by a factor of two to achieve 50 GB on a 120-mm disc. A multilevel version is expected to double that capacity to 100 GB.

Source: Philips [37]

Figure 15. The TwoDOS system. Extra capacity is achieved on a disc surface by closely packing the bits on a 2-D lattice. Information is stored on a wide stripe and readback by a multiple spot reading head.

Volume Holography For holographic storage, several unique coding and signal processing challenges arise. There are similar constraints on the page-oriented arrangement of bits: ISI, degraded SNR, coding for RLL-like constraints, spectral constraints. For a description of holographic recording, see Section 3.4.1. Signal processing challenges arise from issues such as i) changes in the readout condition, ii) misalignment in the detector, iii) light scattering resulting in noise at the detector, and iv) unbalanced DC content across the page. Efforts to address these issues include ISI

cancellation techniques [32,33]. Constrained coding has also been used to enforce a more balanced DC content [34]. For further review, an excellent reference for the signal processing used in holography has been recently published [35]. 3.5.3 Multilevel Recording Technology Roadmap

Because multilevel technology is complementary to other capacity enhancing technologies, the potential for multilevel technology to be incorporated into products should be very high. Some projected future products include those proposed by Calimetrics using their 12-level Multilevel technology to achieve 10 GB per layer on a 120-mm DVD-based disc, 25 GB on a 120-mm “DVD Blue” disc, and 50 GB on a High-NA blue system. Ultimately, this system could be applied to a dual-layer Blu-ray Disc to achieve 100 GB on a 120-mm disc (~59 Gb/in2). In addition, forward-looking efforts, like the Japanese national project named Nanometer- controlled Optical Disc System, are including gray-scale in their efforts to achieve 100 Gb/in2 with a target date of the end of 2003. Towards that goal, at the end of 2002, 40 Gb/in2 density disc recordings were reported using a two-dimensional version of the radial partial response SCIPER-RPR method [36]. Also, the European TwoDOS effort is targeting a system with 100 GB on a 120-mm disc with a 600 Mb per second transfer rate by adding multilevel modulation to double the capacity of their original system. Current TwoDOS densities have been reported at 1.4 times Blu-ray Disc densities (~16-19 Gb/in2) [37].

Multilevel Roadmap

1000 ) 2 n i

/ 100 b G (

y t i s

n 10 e D

e g a r

o 1 t S

0.1 1994 1996 1998 2000 2002 2004 2006 2008 Year

ML-TwoDOS target

Figure 16. Multilevel roadmap. Filled circles represent productized technology in optical data storage. The open circles forecast binary and multilevel products. The filled triangles show reported lab densities for multilevel technology and the open triangles are target densities for multilevel research projects.

Most current multilevel recording methods can be viewed as one-dimensional multilevel recording. Expanding one-dimensional multilevel recording to multidimensional multilevel recording can further increase the density. The idea of 2-D multilevel recording is to create another group of multilevel signals. This idea can be further expanded to multidimensional multilevel recording. For example, multidimensional multilevel recording can be realized by using multilevel signal on phase, amplitude, polarization, wavelength, time, and so on.

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