AMS CAD Lead

Tata Electronics; Bengaluru; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Tata Electronics in Bengaluru seeks an experienced AMS CAD Engineer to optimize CAD tools and design flows for Analog Mixed-Signal circuit design and verification. You will enhance environments, automate tasks, and support AMS teams across front-end and back-end flows.

The ideal candidate has 5+ years of hands-on experience with Cadence Virtuoso, Spectre, Synopsys Custom Compiler, Eldo, and related EDA tools, plus strong scripting skills and AMS methodologies.

Qualifications

• Experience in AMS CAD tool management and design flows.
• Experience with DRC/LVS and parasitic extraction.
• Strong scripting for workflow automation.
• Knowledge of AMS modeling and SPICE-based tools.

Responsibilities

• Develop, maintain, and optimize CAD tools and AMS flows.
• Provide tool support to AMS design teams and troubleshoot issues.
• Automate workflows with custom scripts to boost productivity.
• Collaborate with EDA vendors to implement updates.
• Ensure integration of AMS tools across front-end/back-end.

Skills

Analog/mixed-signal design

Python scripting

Perl

TCL/Shell scripting

Debugging

Communication

Education

B.Tech/M.Tech in Electronics or Microelectronics

Tools

Cadence Virtuoso

Spectre

Synopsys Custom Compiler

Mentor Graphics Pyxis

Eldo

Job description

Job Description

We are seeking a skilled and motivated AMS CAD Engineer to support and optimize CAD tools and design flows for Analog Mixed-Signal (AMS) circuit design and verification. This role involves developing robust design environments, ensuring efficient tool utilization, and collaborating with design teams to enable the successful development of AMS circuits. The ideal candidate will have a strong technical background in EDA tools and AMS design methodologies.

Responsibilities

• Develop, maintain, and optimize CAD tools and flows for AMS design and verification processes.
• Provide technical support to AMS design teams, troubleshooting issues related to tools, workflows, and methodologies.
• Automate design flows and verification tasks by developing custom scripts to improve productivity and efficiency.
• Collaborate with EDA vendors to resolve tool issues, implement updates, and optimize tools for AMS-specific needs.
• Ensure seamless integration of AMS tools and workflows with front-end and back-end design environments.
• Manage tool configurations and licenses, ensuring optimal resource utilization.
• Work closely with design teams to define and enhance design flows, methodologies, and verification processes.
• Maintain and enforce design guidelines, best practices, and documentation for AMS workflows.
• Support AMS simulation environments, including Spectre, HSPICE, and Eldo, and assist in debugging simulation-related issues.
• Evaluate new AMS modeling, simulation tools, layout and recommend improvements to workflows.Stay updated on advancements in EDA technologies and AMS design methodologies to drive innovation.

Qualifications

• Education: B.Tech/M.Tech in Electronics, Electrical Engineering, Microelectronics, or a related field.
• Experience:
  • 5+ years of experience in managing AMS CAD tools and design flows.
  • Hands-on experience with EDA tools such as Cadence Virtuoso, Spectre, Synopsys Custom Compiler, or Mentor Graphics Pyxis, Eldo.
• Skills:
  • Strong knowledge of analog and mixed-signal circuit design methodologies.
  • Proficiency in scripting languages such as Python, Perl, TCL, or Shell for workflow automation.
  • Understanding of layout design principles, physical verification flows (DRC, LVS), and parasitic extraction.
  • Familiarity with AMS simulation tools and SPICE-based modeling.
  • Excellent problem-solving and analytical skills for debugging complex tool-related issues.
  • Strong communication and collaboration abilities to support cross-functional teams.

Preferred Qualifications

• Experience with technology nodes ranging from 180nm to 28nm.
• Familiarity with RF and high-speed AMS designs.
• Knowledge of design for manufacturability (DFM) and yield optimization techniques.
• Exposure to machine learning-based approaches in AMS workflows.