arXiv subjects

Mingu Kang

Publications and source records attributed to Mingu Kang.

59 records · Page 4Linked to original sources

arXiv2017

Universal Mechanism of Band-Gap Engineering in Transition-Metal Dichalcogenides

Two-dimensional (2D) van-der-Waals semiconductors have emerged as a class of materials with promising device characteristics owing to the intrinsic bandgap. For realistic applications, the ideal is to modify the bandgap in a controlled manner by a mechanism that can be generally applied to this class of materials. Here, we report the observation of a universally tunable bandgap in the family of bulk 2H transition metal dichalcogenides (TMDs) by in situ surface doping of Rb atoms. A series of angle-resolved photoemission spectra unexceptionally shows that the bandgap of TMDs at the zone corners is modulated in the range of 0.8 ~ 2.0 eV, which covers a wide spectral range from visible to near infrared, with a tendency from indirect to direct bandgap. A key clue to understand the mechanism of this bandgap engineering is provided by the spectroscopic signature of symmetry breaking and resultant spin splitting, which can be explained by the formation of 2D electric dipole layers within the surface bilayer of TMDs. Our results establish the surface Stark effect as a universal mechanism of bandgap engineering based on the strong 2D nature of van-der-Waals semiconductors.

cond-mat.mtrl-sci↗

arXiv2017

Generalized Water-filling for Source-aware Energy-efficient SRAMs

Conventional low-power static random access memories (SRAMs) reduce read energy by decreasing the bit-line voltage swings uniformly across the bit-line columns. This is because the read energy is proportional to the bit-line swings. On the other hand, bit-line swings are limited by the need to avoid decision errors especially in the most significant bits. We propose an information-theoretic approach to determine optimal non-uniform bit-line swings by formulating convex optimization problems. For a given constraint on mean squared error of retrieved words, we consider criteria to minimize energy (for low-power SRAMs), maximize speed (for high-speed SRAMs), and minimize energy-delay product. These optimization problems can be interpreted as classical water-filling, ground-flattening and water-filling, and sand-pouring and water-filling, respectively. By leveraging these interpretations, we also propose greedy algorithms to obtain optimized discrete swings. Numerical results show that energy-optimal swing assignment reduces energy consumption by half at a peak signal-to-noise ratio of 30dB for an 8-bit accessed word. The energy savings increase to four times for a 16-bit accessed word.

cs.IT↗

arXiv2017

Massive Dirac fermions in a ferromagnetic kagome metal

The kagome lattice is a two-dimensional network of corner-sharing triangles known as a platform for exotic quantum magnetic states. Theoretical work has predicted that the kagome lattice may also host Dirac electronic states that could lead to topological and Chern insulating phases, but these have evaded experimental detection to date. Here we study the d-electron kagome metal Fe$_3$Sn$_2$ designed to support bulk massive Dirac fermions in the presence of ferromagnetic order. We observe a temperature independent intrinsic anomalous Hall conductivity persisting above room temperature suggestive of prominent Berry curvature from the time-reversal breaking electronic bands of the kagome plane. Using angle-resolved photoemission, we discover a pair of quasi-2D Dirac cones near the Fermi level with a 30 meV mass gap that accounts for the Berry curvature-induced Hall conductivity. We show this behavior is a consequence of the underlying symmetry properties of the bilayer kagome lattice in the ferromagnetic state with atomic spin-orbit coupling. This report provides the first evidence for a ferromagnetic kagome metal and an example of emergent topological electronic properties in a correlated electron system. This offers insight into recent discoveries of exotic electronic behavior in kagome lattice antiferromagnets and may provide a stepping stone toward lattice model realizations of fractional topological quantum states.

cond-mat.mtrl-sci↗

arXiv2016

A 481pJ/decision 3.4M decision/s Multifunctional Deep In-memory Inference Processor using Standard 6T SRAM Array

This paper describes a multi-functional deep in-memory processor for inference applications. Deep in-memory processing is achieved by embedding pitch-matched low-SNR analog processing into a standard 6T 16KB SRAM array in 65 nm CMOS. Four applications are demonstrated. The prototype achieves up to 5.6X (9.7X estimated for multi-bank scenario) energy savings with negligible (<1%) accuracy degradation in all four applications as compared to the conventional architecture.

cs.AR↗

arXiv2016

Reducing the Energy Cost of Inference via In-sensor Information Processing

There is much interest in incorporating inference capabilities into sensor-rich embedded platforms such as autonomous vehicles, wearables, and others. A central problem in the design of such systems is the need to extract information locally from sensed data on a severely limited energy budget. This necessitates the design of energy-efficient sensory embedded system. A typical sensory embedded system enforces a physical separation between sensing and computational subsystems - a separation mandated by the differing requirements of the sensing and computational functions. As a consequence, the energy consumption in such systems tends to be dominated by the energy consumed in transferring data over the sensor-processor interface (communication energy) and the energy consumed in processing the data in digital processor (computational energy). In this article, we propose an in-sensor computing architecture which (mostly) eliminates the sensor-processor interface by embedding inference computations in the noisy sensor fabric in analog and retraining the hyperparameters in order to compensate for non-ideal computations. The resulting architecture referred to as the Compute Sensor - a sensor that computes in addition to sensing - represents a radical departure from the conventional. We show that a Compute Sensor for image data can be designed by embedding both feature extraction and classification functions in the analog domain in close proximity to the CMOS active pixel sensor (APS) array. Significant gains in energy-efficiency are demonstrated using behavioral and energy models in a commercial semiconductor process technology. In the process, the Compute Sensor creates a unique opportunity to develop machine learning algorithms for information extraction from data on a noisy underlying computational fabric.

cs.AR↗