Bit-patterned media recording (BPMR) is a promising candidate for application in next-generation magnetic recording systems. To achieve a high areal density in BPMR systems, the bit islands are placed close together and their distance is then further reduced. Therefore, two-dimensional (2-D) interference may occur, including intersymbol interference in the down-track and intertrack interference in the across-track. The overall performance of BPMR is mainly limited by the emergence of 2-D interference in the readback signal. With the staggered-array layout, islands along the neighboring track are displaced by a half period from those on the main track. An island on the main track is primarily affected by the closest islands on the neighboring tracks. To overcome this problem, we introduce an error-correcting 5/6 modulation code that matches the structure of staggered-array bit-patterned media. The simulation results show that the system with the proposed modulation code achieves better performance.

With the development of 5G technology, programs are gradually moving to cloud services. This leads to an increasing demand for storage. In the field of high-density data storage, bit-pattern media recording (BPMR) is considered a promising approach, as it can expand the data density to 4 Tb/in². However, in high-density BPMR, bits or magnetic islands are very close to each other, leading to significant intertrack interference (ITI) from the cross-track direction and intersymbol interference (ISI) from the down-track direction. To minimize two-dimensional interference, including ITI and ISI, the serial detector method has been highly effective. However, in this method, the signal at the output of the first decoder is still a hard output. Therefore, we suggest methods to convert the output of the first detector into a soft output. Additionally, we have developed a new form of generalized partial response target to overcome the track mis-registration. The results show that our proposed methods apparently improve bit error rate performance.

Bit-patterned media recording (BPMR) is a candidate technology that extends the density to up to 4 Tb/in ² to handle large amounts of data. However, in BPMR systems, the major challenge is two-dimensional (2-D) interference, which includes intersymbol interference from the down-track direction and intertrack interference from the across-track direction. To combat 2-D interference, it is natural to consider a 2-D Viterbi detection scheme. Since the complexity of pure 2-D Viterbi detection is quite high, we propose a series of two 1-D Viterbi detectors, which is based on new 2-D partial response (PR) target modeling for the BPMR channel. The use of new 2-D PR target model and the two serial 1-D Viterbi detection scheme ensures that the complexity is greatly reduced compared to 2-D Viterbi detection and the bit error rate performance is improved.

In non-volatile random-access memory (RAM) technologies, the spin-torque transfer magnetic random-access memory (STT-MRAM) is a promising candidate. STT-MRAM has attracted attention owing to its advantages, such as a high density, high endurance, and high-speed writing/reading. Moreover, STT-MRAM is utilized to replace dynamic random-access memory (DRAM) in Internet of Things (IoT) and artificial intelligence (AI) applications. However, because the magnetic tunnel junction is used to write data, STT-MRAM must face process variation and thermal fluctuation problems. This causes errors in writing and reading processes. These errors are independent of each other. Therefore, to solve them and increase the reliability of STT-MRAM, the data are encoded before storage in STT-MRAM devices. In this study, we propose a method for designing codewords using modulation coding techniques. Our codewords increase the minimum Hamming distance and inheritance of the sparse code characteristic to exploit the asymmetric probability of the write errors. The simulation results show that our codewords can improve the bit error rate performance of STT-MRAM compared with previous works.

Central data systems require mass storage systems for big data from many fields and devices. Several technologies have been proposed to meet this demand. Holographic data storage (HDS) is at the forefront of data storage innovation and exploits the extraordinary characteristics of light to encode and retrieve two-dimensional (2D) data from holographic volume media. Nevertheless, a formidable challenge exists in the form of 2D interference that is a by-product of hologram dispersion during data retrieval and is a substantial barrier to the reliability and efficiency of HDS systems. To solve these problems, an equalizer and target are applied to HDS systems. However, in previous studies, the equalizer acted only as a linear convolution filter for the received signal. In this study, we propose a nonlinear equalizer using a convolutional neural network (CNN) for HDS systems. Using a CNN-based equalizer, the received signal can be nonlinearly converted into the desired signal with higher accuracy. In the experiments, our proposed model achieved a gain of approximately 2.5 dB in contrast to conventional models.

Ultra-high density data storage has gained high significance given the increasing amounts of data; many technologies have been proposed to achieve a high density. Among them, bit-pattern media recording (BPMR) is a promising technology. In BPMR systems, data are stored on magnetic islands. Therefore, high densities can be achieved by reducing the distance between the magnetic islands. Because of the closeness between the magnetic islands, the readback signal is distorted by two-dimensional (2D) interference, which includes the intersymbol interference according to the down-track direction and the intertrack interference according to the cross-track direction. A simple and effective serial detection algorithm was recently proposed to mitigate the 2D interference. However, serial detection utilizes the hard output in inner detection, and this degrades the serial detection performance. To resolve this problem, a subsequent study used feedback to estimate the noise and used this noise signal to create a soft output for inner detection. Following up, in this paper we propose a model that utilizes a neural network for noise prediction. The proposed neural network-based model and the model with the feedback line were compared in terms of bit error rate (BER). The results show that the proposed model achieves a gain of approximately 1 dB at a BER of 10⁻⁶.