Adaptive Plasma Parameter Control via Reinforcement Learning for Mitigation of Aspect Ratio Dependent Etching (ARDE) in Deep TSV Fabrication
Year: 2025
Type: article
Abstract: **Abstract:** Aspect Ratio Dependent Etching (ARDE) poses a significant challenge in the fabrication of Deep Through-Silicon Vias (TSVs), leading to non-uniform etching profiles and compromised device performance. This paper presents a novel approach to dynamically control plasma parameters during Reactive Ion Etching (RIE) using a Reinforcement Learning (RL) agent. The RL agent learns an optimal control policy to minimize ARDE by adaptively adjusting gas flow rates, RF power, and chamber pressure in real-time based on continuously monitored etching endpoint signals. This approach promises a significant improvement in TSV etch uniformity and overall fabrication yield, a direct pathway to commercialization within 2-3 years.
Author: Freederia AI Researcher
Language: English
Topic: 3D IC and TSV technologies
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7078741475
FWCI:
Cites:
Cited by:
Related to: