arXiv subjects

Ali Soltani

Publications and source records attributed to Ali Soltani.

5 recordsLinked to original sources

arXiv2026

Interfacial Hole Accumulation Enables Selective Free-Surface Formation in Ge-on-Si

Electrochemically formed two-dimensional free surfaces (2DFS) decouple Ge epilayers from the misfit-dislocation network at the Ge/Si interface, but the origin of their selectivity has remained unclear. We combine finite-volume current-distribution modelling, including charge-transfer kinetics, with a one-dimensional Poisson analysis of the p-Ge/p+-Si heterojunction. With realistic conductivities, an ohmic description would drive 77% of the anodic current through Ge, whereas hole-limited charge transfer reduces this fraction to 0.07%, independently of the electrolyte conductivity. The valence-band offset accumulates holes on the Ge side up to ~1019 cm--3 within a self-limited nanometric layer, which localizes dissolution at the interface. The model yields design rules for the doping contrast (NSi/NGe ___ 60) and the mesa width (___ 3.6$λ$, with $λ$ $\approx$ 1.7 $μ$m).

cond-mat.mtrl-sci↗

arXiv2026

Current-Confined Formation of a Buried Ge/Si Free Surface for Defect Filtering

Lattice-mismatched heteroepitaxy is limited by misfit dislocations, threading defects, and residual strain generated at buried interfaces. Here, we show that current-confined electrochemistry transforms the Ge/Si heterointerface into a laterally continuous nanometric free surface while preserving the Ge epilayer. A low-doped Ge layer on a conductive p-type Si substrate redirects anodization current toward the misfit-dislocation-rich interface. Numerical modelling including charge-transfer kinetics shows that this confinement arises from hole-limited charge transfer and, together with electrochemical calibration, defines a process window that combines interfacial current confinement with controlled Si porosification. STEM reveals an approximately 15 Ang separation, while EDX shows localized interfacial oxygen enrichment. After soft annealing, geometric phase analysis and plan-view TEM show a modified strain-relaxation pathway and strongly suppressed threading-dislocation emergence. Raman spectroscopy indicates a reduction in residual tensile strain from approximately 0.10 % to 0.05 %, whereas spreading resistance profiling confirms preservation of the Ge carrier profile. Current-confined electrochemistry therefore provides a controllable post-epitaxial route for buried-interface decoupling, defect filtering, and strain control in lattice-mismatched semiconductor heterostructures.

cond-mat.mtrl-sci↗

arXiv2026

A Comparative Study of Model Selection Criteria for Symbolic Regression

Effective model selection is critical in symbolic regression (SR) to identify mathematical expressions that balance accuracy and complexity, and have low expected error on unseen data. Many modern implementations of genetic programming (GP) for SR generate a set of Pareto optimal candidate solutions, but reliable automatic selection of solutions that generalize well remains an open issue. Current literature offers various information-theoretic and Bayesian approaches, yet comprehensive comparisons of their performance across different data regimes are limited. This study presents a systematic empirical comparison of widely used selection criteria: the Akaike information criterion (AIC), the corrected AIC (AICc), the Bayesian information criterion (BIC), minimum description length (MDL), as well as Efron's bootstrap estimate for the in-sample prediction error on seven synthetic datasets with Gaussian noise. We rank candidate expressions generated by perturbing ground-truth functions to assess generalization error and selection probability of the ground-truth expression. Our findings reveal that MDL consistently identifies models with the lowest test error and the shortest length across most datasets. While no single criterion dominates all results, MDL and BIC produced the highest probability of selecting the ground-truth expressions.

cs.LG↗

arXiv2025

Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting

Financial Sentiment Analysis (FSA) traditionally relies on human-annotated sentiment labels to infer investor sentiment and forecast market movements. However, inferring the potential market impact of words based on their human-perceived intentions is inherently challenging. We hypothesize that the historical market reactions to words, offer a more reliable indicator of their potential impact on markets than subjective sentiment interpretations by human annotators. To test this hypothesis, a market-derived labeling approach is proposed to assign tweet labels based on ensuing short-term price trends, enabling the language model to capture the relationship between textual signals and market dynamics directly. A domain-specific language model was fine-tuned on these labels, achieving up to an 11% improvement in short-term trend prediction accuracy over traditional sentiment-based benchmarks. Moreover, by incorporating market and temporal context through prompt-tuning, the proposed context-aware language model demonstrated an accuracy of 89.6% on a curated dataset of 227 impactful Bitcoin-related news events with significant market impacts. Aggregating daily tweet predictions into trading signals, our method outperformed traditional fusion models (which combine sentiment-based and price-based predictions). It challenged the assumption that sentiment-based signals are inferior to price-based predictions in forecasting market movements. Backtesting these signals across three distinct market regimes yielded robust Sharpe ratios of up to 5.07 in trending markets and 3.73 in neutral markets. Our findings demonstrate that language models can serve as effective short-term market predictors. This paradigm shift underscores the untapped capabilities of language models in financial decision-making and opens new avenues for market prediction applications.

cs.CE↗

arXiv2018

Low cost Ge/Si virtual substrate through dislocation trapping by nanovoids

A low-cost method to reduce the threading disloca-tions density (TDD) in relaxed germanium (Ge) epilayers grown on silicon (Si) substrates is presented. Ge/Si sub-strate was treated with post epitaxial process to create a region with a high density of nanovoids in Ge layer which act as a barrier for threading dislocations propagation .

cond-mat.mtrl-sci↗