Genetic sequence identification from electrical characterization of single molecules has emerged as a promising alternative to traditional approaches. Since electrical data on single molecules is extremely noisy due to the limitations of even state-of-the-art approaches, achieving high detection rates is challenging, particularly when the task involves being able to distinguish a sequence from its single base-pair mismatches. To address this issue, we propose an architecture based on combining a convolutional neural network with an ensemble learning method, XGBoost. In addition, four different input feature representations are considered, 1D conductance probability distributions and 2D conductance versus distance probability distributions which can be viewed as images, with or without averaging over the experimental parameters. The with averaging case corresponds to feature matrices derived from mixed datasets. We find that 2D probability distributions are helpful with respect to classifier accuracy, but averaged conductance probability distributions are much more impactful and significantly enhance prediction accuracy. Our quantitative analysis of multiple sequences shows an impressive performance increase of approximately 10% for all sequences. While the basis of our analysis is conductance data of DNA strands and their single base-pair mismatches, our method is generally applicable to other single-molecule identification based on their conductance.

Phase change memory (PCM) is a promising candidate for next-generation memory. To lengthen the lifetime of PCM, both transient (soft) and stuck-at (hard) errors need to be corrected. Previous approaches either address only hard errors using error-correcting pointers (ECPs), or employ error-correcting codes capable of correcting six or more errors, which demand high decoding complexity and large storage overhead. Also the verify-after-write needed to generate the ECPs leads to high energy consumption and latency overhead. In this paper, by making use of the property that soft errors are rare and hard errors increase gradually with the number of writes, a novel scheme is proposed to correct both soft and hard errors through integrating 2-error-correcting BCH codes and ECPs. In our design, the ECPs come directly from BCH decoding results. Hence, the energy-consuming verify-after-write and complicated ECP generation process are eliminated. Efficient and low-latency hardware implementations are also developed for BCH en/decoding suitable for PCM applications. Synthesis and power analysis show that the proposed scheme leads to significant overall energy and latency reductions. Moreover, our design can achieve similar or better error protection than prior schemes.

We aim to provide engineers with an introduction to the nonequilibrium Green's function (NEGF) approach, which is a powerful conceptual tool and a practical analysis method to treat nanoscale electronic devices with quantum mechanical and atomistic effects. We first review the basis for the traditional, semiclassical description of carriers that has served device engineers for more than 50 years. We then describe why this traditional approach loses validity at the nanoscale. Next, we describe semiclassical ballistic transport and the Landauer–Buttiker approach to phase-coherent quantum transport. Realistic devices include interactions that break quantum mechanical phase and also cause energy relaxation. As a result, transport in nanodevices is between diffusive and phase coherent. We introduce the NEGF approach, which can be used to model devices all the way from ballistic to diffusive limits. This is followed by a summary of equations that are used to model a large class of structures such as nanotransistors, carbon nanotubes, and nanowires. Applications of the NEGF method in the ballistic and scattering limits to silicon nanotransistors are discussed.

We explore the design of quantum error-correcting codes for cases where the decoherence events of qubits are correlated. In particular, we consider the case where only spatially contiguous qubits decohere, which is analogous to the case of burst errors in classical coding theory. We present several different efficient schemes for constructing families of such codes. For example, one can find one-dimensional quantum codes of length n=13 and 15 that correct burst errors of width b<3; as a comparison, a random-error correcting quantum code that corrects t=3 errors must have length n/spl ges/17. In general, we show that it is possible to build quantum burst-correcting codes that have near optimal dimension. For example, we show that for any constant b, there exist b-burst-correcting quantum codes with length n, and dimension k=n-log n-O(1); as a comparison, the Hamming bound for the case with t (constant) random errors yields k/spl les/n-tlog n+O(1).

The past decade has seen tremendous experimental and theoretical progress in the field of mesoscopic devices and molecular self assembly techniques, leading to laboratory demonstration of many new device concepts. While these studies have been important from a fundamental physics perspective, it has been recognized by many that they may offer new insights into building a future generation of computing machines. This has recently led to a number of proposals for computing machines which use these new and novel device concepts. In this paper, we explain the physical principles behind the operation of one of these proposals, namely the ground state computing model. These computational models share some of the characteristics of the well-known systolic type processor arrays, namely spatial locality, and functional uniformity. In particular, we study the effect of metastable states on the relaxation process (and hence information propagation) in locally coupled and boundary-driven structures. We first give a general argument to show that metastable states are inevitable even in the simplest of structures, a wire. At finite temperatures, the relaxation mechanism is a thermally assisted random walk. The time required to reach the ground state and its life time are determined by the coupling parameters. These time scales are studied in a model based on an array of quantum dots.