Ambiq Micro, Inc. - AI-Assisted Memory Design Intern
Requirements
• Pursuing a BS in EE, CE, Microelectronics, or related field (rising Junior/Senior). • Coursework in Digital Integrated Circuits, Analog Circuit Design, VLSI Design, or Semiconductor Devices. • Coursework in Machine Learning, Optimization, Data Analytics, Computational Methods or related courses. • Basic understanding of CMOS circuit design and IC layout (DRC/LVS) concepts. • Working knowledge of Python and basic ML concepts (regression, clustering). • Comfort working with large datasets. • Strong analytical and problem-solving ability. • Exposure to layout/schematic tools (Virtuoso, Calibre) or characterization tools (SiliconSmart, PrimeTime, Liberate) • Interest in low-power IP for edge AI/IoT • Strong passion, eagerness, and curiosity to learn and explore transistor-level circuit design.
Responsibilities
• Analyze conventional memory assist techniques and explore how AI/ML-driven learning and optimization can enhance control logic, supply modulation, boosted/negative voltages, and replica/tracking schemes. • Investigate AI/ML-based techniques to analyze memory operating conditions, identify failure signatures, and predict robustness across voltage, temperature, process variation, timing, and parasitic effects. • Explore self-timed, self-adaptive, and intelligent memory techniques that learn from operating conditions and dynamically optimize assist decisions for improved robustness, power, performance, and yield. • Develop AI/ML-assisted monitoring, prediction, and adaptive control techniques for dynamically optimizing memory assist, voltage, timing, and read/write operations. • Document, benchmark, and present the proposed self-adaptive memory architecture and key design insights.
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