📌 TOPINDIATOURS Breaking ai: US firm’s advanced pressure sensor for high-purity in

A North Carolina-based company has introduced a new type of advanced pressure sensor that can help in semiconductor manufacturing. Developed by Honeywell, the pressure sensor designed for cleanroom environments.

13MM Pressure Sensor supports manufacturing in high-purity industries, such as semiconductor production, where minimizing contaminants and defects and maximizing yield are essential.

Integrating sensing innovations can help enable precise measurement

“Quality is everything in the semiconductor space. As the industry continues to grow and advance, there is an increased need for wafer fabrication technologies that can operate effectively without introducing impurities into the process,” said Carmen Becker, president of Honeywell Sensing Solutions.

“By integrating sensing innovations like the 13MM Pressure Sensor, we can help enable precise measurement and more reliable manufacturing for the semiconductor sector.”

Unlike other pressure sensors that can “drift” in vacuum environments, which can lead to less accurate readings over time, Honeywell’s 13MM Pressure Sensor can perform accurately under high-pressure and high-temperature conditions with minimal offset drift.

Sensor can help equipment operate with precision

Honeywell highlighted that the sensor—used for various gases that are common in semiconductor manufacturing—can help equipment operate with precision and create wafer features that adhere to precise specifications.

The sensor is designed to comply with strict metal composition and surface roughness requirements for the semiconductor industry (SEMI F20), helping to ensure that minimal impurities are introduced from the sensor into the manufacturing process. It also helps reduce defects and improve overall yield, resulting in less wafer scraps during processing, according to a press release.

In addition to semiconductor processing, the 13MM Pressure Sensor can also be used in other industries requiring ultra-high purity environments such as solar panel manufacturing, display manufacturing, biopharmaceutical production, food and beverage production, advanced optics and medical equipment manufacturing.

The latest sensor developed by Honeywell helps businesses with high purity standards maintain the integrity of their manufacturing processes and reliably create high-quality end products.

Honeywell 13V Series stainless steel pressure sensors are designed for Ultra High Purity (UHP) applications that involve measurement of gases and liquid flow in harsh environments.

The company revealed that the rugged, media-isolated package pressure sensor uses the Honeywell proven piezoresistive semiconductor sensor chip in an oil-isolated housing.

This design has proven to be highly reliable, stable and accurate. Designed with a SEMI F20 compatible ring and diaphragm for Ultra High Purity (UHP) applications, this sensor exhibits exceptional corrosion resistance and specifically designed to withstand the aggressive nature of halogenated gases commonly encountered in semiconductor manufacturing processes, according to Honeywell.

Its robust construction ensures protection against environmental factors while maintaining accurate pressure measurements. The company highlighted that the sensor is designed to minimize the offset drift under prolonged vacuum conditions, making it an ideal choice for critical industrial applications where measurement accuracy and material integrity are crucial.

🔗 Sumber: interestingengineering.com

📌 TOPINDIATOURS Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Syste

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 2M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. 

Meet the author
Institutions: Penn State University, Duke University, Google DeepMind, University of Washington, Meta, Nanyang Technological University, and Oregon State University. The co-first authors are Shaokun Zhang of Penn State University and Ming Yin of Duke University.

In recent years, LLM Multi-Agent systems have garnered widespread attention for their collaborative approach to solving complex problems. However, it’s a common scenario for these systems to fail at a task despite a flurry of activity. This leaves developers with a critical question: which agent, at what point, was responsible for the failure? Sifting through vast interaction logs to pinpoint the root cause feels like finding a needle in a haystack—a time-consuming and labor-intensive effort.
 
This is a familiar frustration for developers. In increasingly complex Multi-Agent systems, failures are not only common but also incredibly difficult to diagnose due to the autonomous nature of agent collaboration and long information chains. Without a way to quickly identify the source of a failure, system iteration and optimization grind to a halt.
 
To address this challenge, researchers from Penn State University and Duke University, in collaboration with institutions including Google DeepMind, have introduced the novel research problem of “Automated Failure Attribution.” They have constructed the first benchmark dataset for this task, Who&When, and have developed and evaluated several automated attribution methods. This work not only highlights the complexity of the task but also paves a new path toward enhancing the reliability of LLM Multi-Agent systems.
The paper has been accepted as a Spotlight presentation at the top-tier machine learning conference, ICML 2025, and the code and dataset are now fully open-source.

Paper：https://arxiv.org/pdf/2505.00212
Code：https://github.com/mingyin1/Agents_Failure_Attribution
Dataset：https://huggingface.co/datasets/Kevin355/Who_and_When
 
 
Research Background and Challenges
LLM-driven Multi-Agent systems have demonstrated immense potential across many domains. However, these systems are fragile; errors by a single agent, misunderstandings between agents, or mistakes in information transmission can lead to the failure of the entire task.

Currently, when a system fails, developers are often left with manual and inefficient methods for debugging:
Manual Log Archaeology : Developers must manually review lengthy interaction logs to find the source of the problem.
Reliance on Expertise : The debugging process is highly dependent on the developer’s deep understanding of the system and the task at hand.
 
This “needle in a haystack” approach to debugging is not only inefficient but also severely hinders rapid system iteration and the improvement of system reliability. There is an urgent need for an automated, systematic method to pinpoint the cause of failures, effectively bridging the gap between “evaluation results” and “system improvement.”

Core Contributions
This paper makes several groundbreaking contributions to address the challenges above:
1. Defining a New Problem: The paper is the first to formalize “automated failure attribution” as a specific research task. This task is defined by identifying the

2. failure-responsible agent and the decisive error step that led to the task’s failure.

Constructing the First Benchmark Dataset: Who&When : This dataset includes a wide range of failure logs collected from 127 LLM Multi-Agent systems, which were either algorithmically generated or hand-crafted by experts to ensure realism and diversity. Each failure log is accompanied by fine-grained human annotations for:
Who: The agent responsible for the failure.
When: The specific interaction step where the decisive error occurred.
Why: A natural language explanation of the cause of the failure.

3. Exploring Initial “Automated Attribution” Methods : Using the Who&When dataset, the paper designs and assesses three distinct methods for automated failure attribution:
All-at-Once: This method provides the LLM with the user query and the complete failure log, asking it to identify the responsible agent and the decisive error step in a single pass. While cost-effective, it may struggle to pinpoint precise errors in long contexts.
Step-by-Step: This approach mimics manual debugging by having the LLM review the interaction log sequentially, making a judgment at each step until the error is found. It is more precise at locating the error step but incurs higher costs and risks accumulating errors.
Binary Search: A compromise between the first two methods, this strategy repeatedly divides the log in half, using the LLM to determine which segment contains the error. It then recursively searches the identified segment, offering a balance of cost and performance.
 
Experimental Results and Key Findings

Experiments were conducted in two settings: one where the LLM knows the ground truth answer to the problem the Multi-Agent system is trying to solve (With Ground Truth) and one where it does not (Without Ground Truth). The primary model used was GPT-4o, though other models were also tested. The systematic evaluation of these methods on the Who&When dataset yielded several important insights:

• A Long Way to Go: Current methods are far from perfect. Even the best-performing single method achieved an accuracy of only about 53.5% in identifying the responsible agent and a mere 14.2% in pinpointing the exact error step. Some methods performed even worse than random guessing, underscoring the difficulty of the task.
• No “All-in-One” Solution: Different methods excel at different aspects of the problem. The All-at-Once method is better at identifying “Who,” while the Step-by-Step method is more effective at determining “When.” The Binary Search method provides a middle-ground performance.

• Hybrid Approaches Show Promise but at a High Cost: The researchers found that combining different methods, such as using the All-at-Once approach to identify a potential agent and then applying the Step-by-Step method to find the error, can improve overall performance. However, this comes with a significant increase in computational cost.

• State-of-the-Art Models Struggle: Surprisingly, even the most advanced reasoning m…

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  🔗 Sumber: syncedreview.com

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