Microelectronics Projects

Argonne is well equipped in terms of expertise and capabilities required for long-term, future microelectronics science. This ranges from Argonne’s activities in the software and architecture area, to materials, physics and devices.

Argonne projects include:

Project

AI-Driven Discovery and Development of Area-Selective Surface Chemistries for Advanced Microelectronics

A new AI-enabled approach to understand and control area-selective atomic layer deposition for advanced low-power microelectronic devices.

Argonne National Laboratory

AI-Driven Discovery and Development of Area-Selective Surface Chemistries for Advanced Microelectronics preview image

Project

AI-Guided Experimental Discovery of Ultra-Wide Bandgap Semiconductors for Operation Under Extreme Conditions

Combining in situ feedback with autonomous thin film synthesis to rapidly optimize ultra-wide bandgap oxide semiconductors for harsh-environment microelectronics.

Argonne National Laboratory

AI-Guided Experimental Discovery of Ultra-Wide Bandgap Semiconductors for Operation Under Extreme Conditions preview image

Project

Digital Twin Assisted Advanced Failure Characterization (LEADER) Platform for Resilient Heterogeneous Microelectronic Integration

By merging in situ experiments with AI-driven digital twins, this effort seeks to enable faster, more precise failure diagnostics for resilient heterogeneous integration.

Argonne National Laboratory

Digital Twin Assisted Advanced Failure Characterization (LEADER) Platform for Resilient Heterogeneous Microelectronic Integration preview image

Project

Highly-tunable, area-selective crystallization of materials by solid phase epitaxy in micro- and nanometer scale

By guiding crystallization from external template crystals, this work enables new control over thin film microstructure and crystalline surface design.

Argonne National Laboratory

Highly-tunable, area-selective crystallization of materials by solid phase epitaxy in micro- and nanometer scale preview image

Project

Imaging Memristive Behavior with Operando X-ray Microscopy

Neuromorphic computing and memristive devices may provide a means for achieving faster and more efficient computations than conventional computers.

Argonne National Laboratory

Imaging Memristive Behavior with Operando X-ray Microscopy preview image

Project

Layered transition metal dichalcogenide barriers for interconnects

We are exploring ultra-thin layered materials grown conformally using atomic layer deposition and etching as metal diffusion barriers for 3D integration in microelectronic devices.

Argonne National Laboratory

Layered transition metal dichalcogenide barriers for interconnects preview image

Project

Low-Power Printable Transistors Enabled by Novel Redox Gate Materials

We are developing redox gate and channel materials that can operate beyond the electrostatic doping limitation while the charge carriers are still electrons or holes to achieve printable transistors operating at sub-volt gate voltages.

Argonne National Laboratory

Low-Power Printable Transistors Enabled by Novel Redox Gate Materials preview image

Project

Neuromorphic Computing Based on Novel Electrochemical Transistors

This project involves the co-design of brain-inspired learning devices with organic material.

Argonne National Laboratory

Neuromorphic Computing Based on Novel Electrochemical Transistors preview image

Project

Pushing computing onto detector silicon

With the availability of advanced microelectronics technology nodes, more digital functionality from the computing domains can be integrated directly into detector silicon to increase data velocity.

Argonne National Laboratory

Pushing computing onto detector silicon preview image

Project

Recycling Heterostructures to Upcycle Microelectronics

A remarkable synthesis strategy has emerged allowing the growth of 2D single crystals on graphene that can be “stacked” atop each other to form a new pseudo-3D material.

Argonne National Laboratory

Recycling Heterostructures to Upcycle Microelectronics preview image

Project

Ultra-dense, near perfect, atomic and synaptic memory

Our goal is to develop the scientific understanding and carry out the discovery science required to build ultra-dense, fast, DNA-class memory and synaptic memory that will be used in neuromorphic engines that can approach biological energy efficiencies.

Materials Science

Ultra-dense, near perfect, atomic and synaptic memory preview image

Bia

Bia is a multidisciplinary co-design initiative that integrates advanced materials, novel device physics and state‐of‐the‐art computing architectures to revolutionize energy efficiency for next‐generation high energy physics and X‐ray science detectors.

Infographic depicting Bia applications. Bia is a multidisciplinary co-design initiative that integrates advanced materials, novel device physics and state‐of‐the‐art computing architectures to revolutionize energy efficiency for next‐generation high energy physics and X‐ray science detectors. Bia is a project within the Microelectronics Energy Efficiency Research Center for Advanced Technologies (MEERCAT), a DOE Microelectronics Science Research Center focused on advancing energy-efficient microelectronics.

Threadwork

Threadwork is an innovative co-design approach that seeks to transform the process by which we conduct microelectronics research.