arXiv subjects

Chirag Gupta

Publications and source records attributed to Chirag Gupta.

At least 19 recordsLinked to original sources

arXiv2025

Electrical Characterization of High-k (k>115) Crystalline SrTiO3 (STO) thin film integration with GaN with Nanomembrane Transfer Process

High-k (115), crystalline SrTiO3 (STO) thin film was transferred on GaN for potential applications in power devices (transistor and diodes) by nanomembrane transfer method and the detailed electrical properties such as leakage current, CV profiles, dielectric constant, frequency dispersion was reported from fabricated MOSCAP structures. The leakage current was negligible (under noise-level of tool) up to 6 V and 11 V for 50 nm and 200 nm STO membrane respectively A high-quality dielectric was indicated by the CV profile, which showed almost negligible frequency dispersion in the frequency range of 10 kHz to 500 kHz. The dielectric constant was 50 to 82 with the 50 nm thick STO membrane and 115 to 186 in the 200 nm thick STO membrane. Thermal annealing of the membrane in ambient conditions at 250 degrees for 2 hours led to a slight improvement in the dielectric constant (8 to 20 percent), albeit at the expense of degraded leakage current performance, as indicated by a reduction of 1 V to 3 V in the "no leakage region" of the IV curves after annealing. The possible physical mechanisms responsible for these changes were also analyzed and discussed.

cond-mat.mtrl-sci↗

arXiv2025

Learning from Interval Targets

We study the problem of regression with interval targets, where only upper and lower bounds on target values are available in the form of intervals. This problem arises when the exact target label is expensive or impossible to obtain, due to inherent uncertainties. In the absence of exact targets, traditional regression loss functions cannot be used. First, we study the methodology of using a loss functions compatible with interval targets, for which we establish non-asymptotic generalization bounds based on smoothness of the hypothesis class that significantly relaxing prior assumptions of realizability and small ambiguity degree. Second, we propose a novel min-max learning formulation: minimize against the worst-case (maximized) target labels within the provided intervals. The maximization problem in the latter is non-convex, but we show that good performance can be achieved with the incorporation of smoothness constraints. Finally, we perform extensive experiments on real-world datasets and show that our methods achieve state-of-the-art performance.

cs.LG↗

arXiv2025

Passivation-Free Ga-Polar AlGaN/GaN Recessed-Gate HEMTs on Sapphire with 2.8 W/mm POUT and 26.8% PAE at 94 GHz

In this work, we demonstrate a passivation-free Ga-polar recessed-gate AlGaN/GaN HEMT on a sapphire substrate for W-band operation, featuring a 5.5 nm Al0.35Ga0.65N barrier under the gate and a 31 nm Al0.35Ga0.65N barrier in the gate access regions. The device achieves a drain current density of 1.8 A/mm, a peak transconductance of 750 mS/mm, and low gate leakage with a high on/off ratio of 10^7. Small-signal characterization reveals a current-gain cutoff frequency of 127 GHz and a maximum oscillation frequency of 203 GHz. Continuous-wave load-pull measurements at 94 GHz demonstrate an output power density of 2.8 W/mm with 26.8% power-added efficiency (PAE), both of which represent the highest values reported for Ga-polar GaN HEMTs on sapphire substrates and are comparable to state-of-the-art Ga-polar GaN HEMTs on SiC substrates. Considering the low cost of sapphire, the simplicity of the epitaxial design, and the reduced fabrication complexity relative to N-polar devices, this work highlights the potential of recessed-gate Ga-polar AlGaN/GaN HEMTs on sapphire as a promising candidate for next-generation millimeter-wave power applications.

physics.app-ph↗

arXiv2025

Does Calibration Affect Human Actions?

Calibration has been proposed as a way to enhance the reliability and adoption of machine learning classifiers. We study a particular aspect of this proposal: how does calibrating a classification model affect the decisions made by non-expert humans consuming the model's predictions? We perform a Human-Computer-Interaction (HCI) experiment to ascertain the effect of calibration on (i) trust in the model, and (ii) the correlation between decisions and predictions. We also propose further corrections to the reported calibrated scores based on Kahneman and Tversky's prospect theory from behavioral economics, and study the effect of these corrections on trust and decision-making. We find that calibration is not sufficient on its own; the prospect theory correction is crucial for increasing the correlation between human decisions and the model's predictions. While this increased correlation suggests higher trust in the model, responses to ``Do you trust the model more?" are unaffected by the method used.

cs.HC↗

arXiv2025

Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies

While large language models (LLMs) achieve strong performance on text-to-SQL parsing, they sometimes exhibit unexpected failures in which they are confidently incorrect. Building trustworthy text-to-SQL systems thus requires eliciting reliable uncertainty measures from the LLM. In this paper, we study the problem of providing a calibrated confidence score that conveys the likelihood of an output query being correct. Our work is the first to establish a benchmark for post-hoc calibration of LLM-based text-to-SQL parsing. In particular, we show that Platt scaling, a canonical method for calibration, provides substantial improvements over directly using raw model output probabilities as confidence scores. Furthermore, we propose a method for text-to-SQL calibration that leverages the structured nature of SQL queries to provide more granular signals of correctness, named "sub-clause frequency" (SCF) scores. Using multivariate Platt scaling (MPS), our extension of the canonical Platt scaling technique, we combine individual SCF scores into an overall accurate and calibrated score. Empirical evaluation on two popular text-to-SQL datasets shows that our approach of combining MPS and SCF yields further improvements in calibration and the related task of error detection over traditional Platt scaling.

cs.CL↗

arXiv2024

3 kV Monolithic Bidirectional GaN HEMT on Sapphire

More than 3 kV breakdown voltage was demonstrated in monolithic bidirectional GaN HEMTs for the first time having potential applications in 1200V or 1700V-class novel power converters. The on resistance of the fabricated transistors was ~20 ohm.mm or ~11 mili ohm.cm^2. Breakdown voltage was optimized by utilizing two field plates in either side of the transistor and optimizing their geometry. Shorter first field plate lengths (less than 2 micron) resulted in higher breakdown voltage and the possible reason for this was discussed. The transistors had a steep subthreshold swing of 92 mV / dec. The on/off ratio was greater than 10^5 and it was limited by the tool capacity. The fabricated 3 kV transistor was benchmarked against the state-of-the-art monolithic bidirectional GaN HEMTs in the performance matrices of breakdown voltage and on resistance, that showed crucial progress.

physics.app-ph↗

arXiv2024

Demonstration of Si-doped Al-rich thin regrown Al(Ga)N films on AlN on sapphire templates with $\gt10^{15}/cm^3$ free carrier concentration using close-coupled showerhead MOCVD reactor

Thin Si-doped Al-rich (Al>0.85) regrown Al(Ga)N layers were deposited on AlN on Sapphire template using metal-organic chemical vapor deposition (MOCVD) techniques. The optimization of the deposition conditions such as temperature, V/III ratio, deposition rate, and Si concentration resulted in a high charge carrier concentration (>$10^{15}/cm^{3}$) in the Si-doped Al-rich Al(Ga)N films. A pulsed deposition condition was employed to achieve a controllable Al composition greater than 95% and to prevent unintended Ga incorporation in the AlGaN material deposited using the close-coupled showerhead reactor. Also, the effect of unintentional Si incorporation on free charge carrier concentration at the regrowth interface was observed by varying the thickness of the regrown Al(Ga)N layer. A maximum charge carrier concentration of $4.8\times 10^{16}/cm^3$ and $7.5\times 10^{15}/cm^3$ were achieved for Al0.97Ga0.03N and AlN films with thickness <300 nm compared to previously reported n-Al(Ga)N films with thickness $\ge$400 nm deposited using MOCVD technique.

physics.app-ph↗

arXiv2024

Crack-free high composition (>35%) thick (>30 nm) barrier AlGaN/AlN/GaN HEMT on sapphire with record low sheet resistance

In this article, high composition (>35%) thick (>30 nm) barrier AlGaN/AlN/GaN HEMT structure grown on a sapphire substrate with ultra-low sheet resistivity (<250 Ω/ \Box ) is reported. Optimization of growth conditions, such as reduced growth rate, low carbon incorporation, and thickness optimization of different epitaxial layers allowed to grow a crack-free high composition and thick AlGaN barrier layer HEMT structure. A significantly high two-dimensional electron gas (2DEG) density of 1.46 \times 10^{13} cm^{-2} with a room temperature mobility of 1710 cm^{2}/V.s is obtained by Hall measurement using the Van-Der-Pauw method. These state-of-the-art results show great potential for high-power Ga-polar HEMT design on the sapphire substrate.

physics.app-ph↗

arXiv2024

High Voltage (~2 kV) field-plated Al0.64Ga0.36N-channel HEMTs

High voltage (~2 kV) AlGaN-channel HEMTs were fabricated with 64% Aluminum composition in the channel. The average on-resistance was ~75 ohm. mm (~21 miliohm. cm^2) for LGD = 20 microns. Breakdown voltage reached >3 kV (tool limit) before passivation however it reduced to ~2 kV after SiN surface passivation and field plates. The apparent high breakdown voltage prior to passivation can possibly be attributed to the field plate effect of the charged trap states of the surface. The breakdown voltage and RON demonstrated a strong linear correlation in a scattered plot with ~50 measured transistors. In pulsed IV measurements with 100 microsecond pulse width and 40 V of off-state bias (tool limit), the dynamic RON increased by ~5% compared to DC RON and current collapse was <10%.

physics.app-ph↗

arXiv2023

Band alignment of grafted monocrystalline Si (001)/$β$-Ga$_2$O$_3$ (010) p-n heterojunction determined by X-ray photoelectron spectroscopy

Beta-phase gallium oxide ($β$-Ga$_2$O$_3$) research has gained accelerated pace due to its superiorly large bandgap and commercial availability of large-diameter native substrates. However, the high acceptor activation energy obstructs the development of homojunction bipolar devices employing $β$-Ga$_2$O$_3$. The recently demonstrated semiconductor grafting technique provides an alternative and viable approach towards lattice-mismatched $β$-Ga$_2$O$_3$-based p-n heterojunctions with high quality interfaces. Understanding and quantitatively characterizing the band alignment of the grafted heterojunctions is crucial for future bipolar device development employing the grafting method. In this work, we present a systematic study of the band alignment in the grafted monocrystalline Si/$β$-Ga$_2$O$_3$ heterostructure by employing X-ray photoelectron spectroscopy (XPS). The core level peaks and valence band spectra of the Si, $β$-Ga$_2$O$_3$, and the grafted heterojunction were carefully obtained and analyzed. The band diagrams of the Si/$β$-Ga$_2$O$_3$ heterostructure were constructed using two individual methods, the core level peak method and the valence band spectrum method, by utilizing the different portions of the measured data. The reconstructed band alignments of the Si/$β$-Ga$_2$O$_3$ heterostructure using the two different methods are identical within the error range. The band alignment is also consistent with the prediction from the electron affinity values of Si and $β$-Ga$_2$O$_3$. The study suggests that the interface defect density in grafted Si/$β$-Ga$_2$O$_3$ heterostructure is at a sufficiently low level such that Fermi level pinning at the interface has been completely avoided and the universal electron affinity rule can be safely employed to construct the band diagrams of grafted monocrystalline Si/$β$-Ga$_2$O$_3$ heterostructures.

cond-mat.mtrl-sci↗

arXiv2023

Online Platt Scaling with Calibeating

We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OPS by incorporating a recently developed technique called calibeating to make it more robust. Theoretically, our resulting OPS+calibeating method is guaranteed to be calibrated for adversarial outcome sequences. Empirically, it is effective on a range of synthetic and real-world datasets, with and without distribution drifts, achieving superior performance without hyperparameter tuning. Finally, we extend all OPS ideas to the beta scaling method.

cs.LG↗

arXiv2023

OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework

Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.

eess.SP↗

arXiv2023

Parity Calibration

In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation. In this context, we introduce the notion of parity calibration, which captures the goal of calibrated forecasting for the increase-decrease (or "parity") event in a timeseries. Parity probabilities can be extracted from a forecasted distribution for the output, but we show that such a strategy leads to theoretical unpredictability and poor practical performance. We then observe that although the original task was regression, parity calibration can be expressed as binary calibration. Drawing on this connection, we use an online binary calibration method to achieve parity calibration. We demonstrate the effectiveness of our approach on real-world case studies in epidemiology, weather forecasting, and model-based control in nuclear fusion.

cs.LG↗

arXiv2023

Monocrystalline Si/$β$-Ga$_2$O$_3$ p-n heterojunction diodes fabricated via grafting

The $β$-Ga$_2$O$_3$ has exceptional electronic properties with vast potential in power and RF electronics. Despite the excellent demonstrations of high-performance unipolar devices, the lack of p-type doping in $β$-Ga$_2$O$_3$ has hindered the development of Ga$_2$O$_3$-based bipolar devices. The approach of p-n diodes formed by polycrystalline p-type oxides with n-type $β$-Ga$_2$O$_3$ can face severe challenges in further advancing the $β$-Ga$_2$O$_3$ bipolar devices due to their unfavorable band alignment and the poor p-type oxide crystal quality. In this work, we applied the semiconductor grafting approach to fabricate monocrystalline Si/$β$-Ga$_2$O$_3$ p-n diodes for the first time. With enhanced concentration of oxygen atoms at the interface of Si/$β$-Ga$_2$O$_3$, double side surface passivation was achieved for both Si and $β$-Ga$_2$O$_3$ with an interface Dit value of 1-3 x 1012 /cm2 eV. A Si/$β$-Ga$_2$O$_3$ p-n diode array with high fabrication yield was demonstrated along with a diode rectification of 1.3 x 107 at +/- 2 V, a diode ideality factor of 1.13 and avalanche reverse breakdown characteristics. The diodes C-V shows frequency dispersion-free characteristics from 10 kHz to 2 MHz. Our work has set the foundation toward future development of $β$-Ga$_2$O$_3$-based transistors.

physics.app-ph↗

arXiv2023

Comprehensive TCAD Simulation Study of High Voltage (>650V) Common Drain Bidirectional AlGaN/GaN HEMTs

A broad TCAD simulation analysis of a monolithic common drain bidirectional GaN HEMT was performed. We used gate-to-gate distances of 4 microns and 6 microns for the devices optimized with two field plates. The breakdown voltages were 675V and 915V respectively. Inclusion of field plates near both the gates produced electric field peaks at the opposite ends of the transistor simultaneously. This resulted in better electric field management or higher blocking voltage per unit length. Consequently, the 675V monolithic bidirectional HEMT had an impressive 40% improvement in on-resistance than its 650V typical series/parallel counterpart.

physics.app-ph↗

arXiv2022

Top-label calibration and multiclass-to-binary reductions

A multiclass classifier is said to be top-label calibrated if the reported probability for the predicted class -- the top-label -- is calibrated, conditioned on the top-label. This conditioning on the top-label is absent in the closely related and popular notion of confidence calibration, which we argue makes confidence calibration difficult to interpret for decision-making. We propose top-label calibration as a rectification of confidence calibration. Further, we outline a multiclass-to-binary (M2B) reduction framework that unifies confidence, top-label, and class-wise calibration, among others. As its name suggests, M2B works by reducing multiclass calibration to numerous binary calibration problems, each of which can be solved using simple binary calibration routines. We instantiate the M2B framework with the well-studied histogram binning (HB) binary calibrator, and prove that the overall procedure is multiclass calibrated without making any assumptions on the underlying data distribution. In an empirical evaluation with four deep net architectures on CIFAR-10 and CIFAR-100, we find that the M2B + HB procedure achieves lower top-label and class-wise calibration error than other approaches such as temperature scaling. Code for this work is available at \url{https://github.com/aigen/df-posthoc-calibration}.

cs.LG↗

arXiv2022

Faster online calibration without randomization: interval forecasts and the power of two choices

We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive adversary who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve an $ε$-calibration error rate of $O(1/\sqrt{T})$, which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be $Ω(1)$. Inspired by the equally seminal works on the "power of two choices" and imprecise probability theory, we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two nearby probabilistic forecasts, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices, or imprecise forecast, accords the forecaster with significant power -- we show that a faster $ε$-calibration rate of $O(1/T)$ can be achieved even without deploying any randomization.

cs.LG↗

arXiv2022

Nested conformal prediction and quantile out-of-bag ensemble methods

Conformal prediction is a popular tool for providing valid prediction sets for classification and regression problems, without relying on any distributional assumptions on the data. While the traditional description of conformal prediction starts with a nonconformity score, we provide an alternate (but equivalent) view that starts with a sequence of nested sets and calibrates them to find a valid prediction set. The nested framework subsumes all nonconformity scores, including recent proposals based on quantile regression and density estimation. While these ideas were originally derived based on sample splitting, our framework seamlessly extends them to other aggregation schemes like cross-conformal, jackknife+ and out-of-bag methods. We use the framework to derive a new algorithm (QOOB, pronounced cube) that combines four ideas: quantile regression, cross-conformalization, ensemble methods and out-of-bag predictions. We develop a computationally efficient implementation of cross-conformal, that is also used by QOOB. In a detailed numerical investigation, QOOB performs either the best or close to the best on all simulated and real datasets. Code for QOOB is available at https://github.com/aigen/QOOB.

stat.ME↗