惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

有赞技术团队
有赞技术团队
Apple Machine Learning Research
Apple Machine Learning Research
IT之家
IT之家
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
B
Blog RSS Feed
酷 壳 – CoolShell
酷 壳 – CoolShell
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
博客园_首页
V
V2EX
aimingoo的专栏
aimingoo的专栏
爱范儿
爱范儿
博客园 - 聂微东
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Stack Overflow Blog
Stack Overflow Blog
罗磊的独立博客
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
MongoDB | Blog
MongoDB | Blog
Jina AI
Jina AI
T
The Blog of Author Tim Ferriss
月光博客
月光博客
云风的 BLOG
云风的 BLOG

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
0.6-V, uW-Power 4-Stage OTA with Minimal Components and 1...
M. Privitera, A. D. Grasso, A. Ballo, M. Alioto · 2025-08-07 · via eess.SP updates on arXiv.org

A four-stage operational transconductance amplifier (OTA) for ultra-low-power applications is introduced in this paper. The proposed circuit inclusive of frequency compensation requires minimal transistor count and passives, overcoming the traditionally difficult compensation of 4-stage OTAs and bringing it back to the simplicity of 3-stage OTAs. At the same time, the proposed circuit achieves high power efficiency, as evidenced by the >3.7X (>11.3X) improvement in the large-signal (small-signal) power efficiency figure of merit FOML (FOMS), compared to prior 4-stage OTAs (sub-1 V multi-stage OTAs). Thanks to the lower sensitivity of the phase margin to the load capacitance, the proposed OTA remains stable under a wide range of loads (double-sided as in any 3-4-stage OTA), achieving a max/min ratio of the load capacitance of >100X.