Device Modeling Engineer

In this role you will be responsible for simulating the physics behind our custom CapRAM device. The derived device models are used by the analog design team to perform circuit simulations with our unique CapRAM device.

What you will do:

* TCAD simulation

* Setting up Verilog-A & Spectre models, and simulation of these

* Simplification of mathematical device models for faster simulation

* Process engineering to fabricate these devices in R&D and volume fabs

What you should bring in:

* 3 years of experience/knowledge in device modeling and TCAD simulation

* Understanding of semiconductor device physics

* Knowledge in Synopsys TCAD tools

* Willingness to learn new technologies and tools, and apply them quickly

Helpful but not required:

* Knowledge in Verilog-A and Spectre simulations

* Semiconductor process/fabrication knowledge

* Process simulation in TCAD tools

Why us?

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Device Modeling Engineer

Dresden, DE (primary site), Austin, TX

Full-time

Permanent employee

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About the Role

In this role you will be responsible for simulating the physics behind our custom CapRAM device. The derived device models are used by the analog design team to perform circuit simulations with our unique CapRAM device.

What you will do:

* TCAD simulation

* Setting up Verilog-A & Spectre models, and simulation of these

* Simplification of mathematical device models for faster simulation

* Process engineering to fabricate these devices in R&D and volume fabs

What you should bring in:

* 3 years of experience/knowledge in device modeling and TCAD simulation

* Understanding of semiconductor device physics

* Knowledge in Synopsys TCAD tools

* Willingness to learn new technologies and tools, and apply them quickly

Helpful but not required:

* Knowledge in Verilog-A and Spectre simulations

* Semiconductor process/fabrication knowledge

* Process simulation in TCAD tools

About us

At SEMRON, we're redefining what's possible in AI hardware. Our core innovation lies in analog in-memory computing for deep neural network acceleration, enabling us to build compute architectures that scale vertically into the third dimension, much like NAND flash revolutionized memory. This leap in physical density allows us to deploy models with billions of parameters on chip areas as small as a few square millimeters.

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