Jie Min

Bio: Jie Min is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Subthreshold slope & Subthreshold conduction. The author has an hindex of 9, co-authored 16 publications receiving 309 citations. Previous affiliations of Jie Min include The Chinese University of Hong Kong.

Short-Channel Effects in Tunnel FETs

Abstract: This paper investigates short-channel effects (SCEs) in double-gate tunnel FETs (TFETs) using an analytic model that includes depletion in the source. It is shown that the drain bias has a significant effect on the potential profile at the source when the channel length is reduced to below twice the scale length. The OFF-state current becomes a strong function of channel length. The subthreshold current slope is also degraded in short-channel TFETs to the extent that there is no region of <60 mV/decade below a minimum channel length. The SCE also manifests itself in the finite-output conductance in the saturation region—a Drain-Induced Barrier Lowering-like effect in conventional MOSFETs.

Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research.

Abstract: Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional group(s) on chemical shifts combine to hinder their effectiveness. Here, we leveraged Non-Uniform Sampling (NUS) 2D NMR techniques and deep Convolutional Neural Networks (CNNs) to create a tool, SMART, that can assist in natural products discovery efforts. First, an NUS heteronuclear single quantum coherence (HSQC) NMR pulse sequence was adapted to a state-of-the-art nuclear magnetic resonance (NMR) instrument, and data reconstruction methods were optimized, and second, a deep CNN with contrastive loss was trained on a database containing over 2,054 HSQC spectra as the training set. To demonstrate the utility of SMART, several newly isolated compounds were automatically located with their known analogues in the embedded clustering space, thereby streamlining the discovery pipeline for new natural products.

A Short-Channel $I$ – $V$ Model for 2-D MOSFETs

Abstract: This paper presents an analytic $I$ – $V$ model for short-channel MOSFETs made of 2-D semiconductor material. First, a subthreshold current model is formulated based on the solutions to 2-D Poisson’s equation with negligible mobile charge. Next, a velocity saturation model is developed under the framework of a drift and diffusion long-channel model. These two models are then unified into an all region, short-channel $I$ – $V$ model with both drain induced barrier lowering and velocity saturation effects. Ballistic currents, including the intraband tunneling and the above-the-barrier transport, have been examined and compared with the thermionic currents.

An Analytic Model for Heterojunction Tunnel FETs With Exponential Barrier

Abstract: This paper presents an analytic model for double-gate tunnel FETs with an exponential barrier. By carrying out the Wentzel-Kramer–Brillouin integral in closed form, an $I$ – $V$ model is formulated in terms of a single integral of a continuous function with respect to energy. The model shows that source degeneracy helps the linear region $I_{\mathbf {ds}}$ – $V_{\mathbf {ds}}$ characteristics, but degrades the saturation current. Also investigated is the role of the effective density of states on the debiasing of $V_{\mathbf {gs}}$ due to channel inversion charge at low $V_{\mathbf {ds}}$ . A high effective density of states is shown to lead to superlinear $I_{\mathbf {ds}}$ – $V_{\mathbf {ds}}$ characteristics.

Analysis of Source Doping Effect in Tunnel FETs With Staggered Bandgap

Abstract: The effect of source doping on tunnel FET (TFET) currents is investigated analytically for the case of an exponential barrier Source depletion is coupled to the channel potential profile through the continuity of field at the junction edge Closed form WKB (Wentzel-Kramers-Brillouin) integral are carried out by considering mixed electron and hole tunneling in heterojunction TFETs with a staggered bandgap It is shown that there is an optimum source doping that maximizes the TFET current A certain degree of source depletion actually helps because it lets low-barrier hole tunneling in the source to make up part of the tunneling path

Organic semiconductor crystals

Abstract: Organic semiconductors have attracted a lot of attention since the discovery of highly doped conductive polymers, due to the potential application in field-effect transistors (OFETs), light-emitting diodes (OLEDs) and photovoltaic cells (OPVs). Single crystals of organic semiconductors are particularly intriguing because they are free of grain boundaries and have long-range periodic order as well as minimal traps and defects. Hence, organic semiconductor crystals provide a powerful tool for revealing the intrinsic properties, examining the structure–property relationships, demonstrating the important factors for high performance devices and uncovering fundamental physics in organic semiconductors. This review provides a comprehensive overview of the molecular packing, morphology and charge transport features of organic semiconductor crystals, the control of crystallization for achieving high quality crystals and the device physics in the three main applications. We hope that this comprehensive summary can give a clear picture of the state-of-art status and guide future work in this area.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes-6.

Abstract: Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes is a series of Editorials, which are published on a biannual basis by the Editorial Board of the Medicinal Chemistry section of the journal Molecules [...].

Two-Dimensional Transition Metal Dichalcogenides and Their Charge Carrier Mobilities in Field-Effect Transistors

Abstract: Two-dimensional (2D) materials have attracted extensive interest due to their excellent electrical, thermal, mechanical, and optical properties. Graphene has been one of the most explored 2D materials. However, its zero band gap has limited its applications in electronic devices. Transition metal dichalcogenide (TMDC), another kind of 2D material, has a nonzero direct band gap (same charge carrier momentum in valence and conduction band) at monolayer state, promising for the efficient switching devices (e.g., field-effect transistors). This review mainly focuses on the recent advances in charge carrier mobility and the challenges to achieve high mobility in the electronic devices based on 2D-TMDC materials and also includes an introduction of 2D materials along with the synthesis techniques. Finally, this review describes the possible methodology and future prospective to enhance the charge carrier mobility for electronic devices.

Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography-High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics.

Abstract: The rapid innovations in metabolite profiling, bioassays and chemometrics have led to a paradigm shift in natural product (NP) research. Indeed, having partial or full structure information about possibly \"all\" specialized metabolites and an estimation of their levels in plants or microorganisms provides a way to perform pharmacognostic or chemical ecology investigations from a new and holistic perspective. The increasing amount of accurate metabolome data that can be acquired on massive sample sets, notably through data-dependent LC-HRMS/MS and NMR profiling, allows the mapping of natural extracts at an unprecedented level of precision. Most progress made recently in accelerating metabolite identification has been pushed by the need for metabolomics to have tools that provide a confident annotation of the biomarkers highlighted as the results of data mining through multivariate analysis, often on important datasets of complex samples. Historically, NP chemists have been involved in the unambiguous full de novo identification of unknown compounds from complex natural biological matrices. This process is classically performed by the tedious isolation of pure bioactive NPs through comprehensive bioactivity-guided isolation workflows involving orthogonal chromatographic steps at the preparative level. Increasingly advanced metabolomics metabolite profiling methods are of strategic importance in dereplication workflows in NP research as well as for the full metabolome composition assignment of relevant organisms from both drug discovery and chemical ecology perspectives. In this review, we describe the latest developments in metabolite profiling by both LC-MS and NMR-based methods and related databases from a natural product chemist perspective. We assess the current possibilities and limits of such methods and the workflows for manual and automated NP annotations by equally treating the MS and NMR approaches that are both key for the \"as confident as possible\" NP annotation in crude natural extracts. We also propose future lines of development in the field that are important for NP research but are also generally needed for metabolite annotation in metabolomics because NPs represent perfect candidate compounds for identification due to their intrinsic structural complexity and chemodiversity across organisms. This review does not aim to provide a comprehensive survey of all metabolite profiling applications made in NP research to date. Typical case studies are discussed, and an update of a selection of the latest advanced original studies and numerous specialized reviews is made with links to tools and DBs regarded as useful for their current or future usage in NP research. Evaluations of what can be readily implemented and what is still required for confident NP structural elucidation are made, especially concerning access to generic structural and spectral DBs as well as the use of orthogonal detection methods for improved confidence in metabolite annotation.

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

Abstract: This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.