Develop complex circuit block, test key for leading-edge process node (planar, finfet, nanosheet) from schematic netlist to gds.
Develop standard cell library from architecture evaluation, PPA assessment and customized cell design with different PPA purpose and IP blocks.
Work with layout engineer for design implementation and physical verification including DRC/LVS/ERC/ANT.
Perform layout extraction, simulation, analyze simulation data, including performance, power, leakage, for layout dependent effect study.
Work with digital team and testing team for design implementation and chip level silicon data collection.
Develop processing flow for silicon data analysis, visualization, AI model regression.
Develop advanced library generation methodology and flow, including characterization, kit generation, regression, and quality assurance.
Perform timing/power/constraint/noise/LVF variation characterization for standard cell or complex circuit blocks.
Work with circuit designer and tool vendors to tackle modelling difficulties, like accuracy and runtime issues, especially for complex circuit blocks.
Responsible for new kits enablement and evaluation, like EM characterization, aging characterization.
Involve with data analysis and machine learning as well for circuit performance and power assessment.
Requirements:
BS/MS in Electrical and Electronic Engineering/Computer Engineering/Computer Science with minimum 3 years(MS) or 5 years(BS) industry experience. (We have entry level position with the same function, no prior experience is required.)
Familiar with Python, Perl, Tcl or C/C++ for flow development and data analysis, machine learning.
Basic knowledge of digital design and/or circuit design.
Solid understanding of foundation IP design, with device physics, transistor level circuit, layout dependent effect knowledge.
Experience of test chip design in FinFet/Nanosheet technologies is a plus, with understanding of DRM, layout rules, PV check, and simulation skills.
Strong communication and teamwork skills to collaborate effectively with cross-functional teams.
