NANOFABRICATOR® PRO
The Physical Platform for AI-Driven Materials Discovery
Today, materials design, fabrication, characterization, and manufacturing development remain fragmented across separate tools and teams. These disconnected workflows slow innovation and risk losing the process knowledge required to reproduce and scale successful results.
NANOFABRICATOR® PRO is ATLANT 3D’s answer: the world’s first and only physical platform for AI-driven materials discovery.
Powered by ATLANT 3D’s proprietary Direct Atomic Layer Processing technology, NANOFABRICATOR® PRO connects AI-driven discovery with programmable atomic-scale fabrication, experimental validation and device prototyping.
Its industrial architecture preserves digital recipes, fabrication parameters and physical results, creating a continuous path from computational design to process transfer, integration with existing platforms and scalable manufacturing.
From AI Discovery to Physical Material
A computational prediction does not automatically provide the correct precursor chemistry, substrate, temperature, layer sequence, interface condition or process window. These parameters must be established through physical experimentation.
With more than 450 compatible material families, NANOFABRICATOR® PRO provides the physical execution layer that translates AI-generated candidates and digital process instructions into controlled experiments. Material patterns, processing locations, parameters and sequences are defined through software and executed using DALP® technology.
NANOFABRICATOR® PRO connects digital discovery with physical validation by fabricating AI-generated candidates, enabling characterization of their performance and returning physical results to improve future experiments, recipes and material designs.
More Experiments from Every Substrate
This one-substrate-one-experiment architecture limits the speed and scale of materials development. Increasing the number of tools or running chambers faster does not address the underlying constraint.
DALP® technology changes this experimental architecture. A moving microreactor confines atomic-layer chemistry to a localized area, processing materials only where specified. Multiple materials, patterns, structures and parameter variations can therefore be created on a single substrate.
Each run can generate multiple experiments and substantially more learning. Innovators can compare different process conditions, material combinations and device structures without committing an entire substrate to every variation.
This accelerates iteration while reducing the substrates, fabrication steps and resources required. It also creates richer experimental datasets that can be connected with characterization results and used to guide the next fabrication cycle.
The result is a more scalable approach to physical experimentation, enabling organizations to move from isolated experiments toward systematic, data-driven materials and process development.
As more experiments are completed, physical knowledge compounds. Better data enables better experimental decisions. Better decisions produce stronger recipes. Stronger recipes improve repeatability, process transfer and the ability to integrate validated processes into scalable manufacturing environments.
Atomic Precision, Software Defined
Digital recipes define the material, position, sequence and operating conditions, turning fabrication intent into a repeatable machine instruction. The platform supports localized deposition, selective processing, multilayers, gradients and multi-material structures on planar and complex surfaces.
Its programmable architecture combines the flexibility required for discovery with the process control required for industrial development. Software-defined recipes also support process continuity. As a material or device progresses from discovery to validation, the recipe can be refined and transferred rather than recreated in a disconnected toolchain.
This makes the process itself a valuable manufacturing asset. It contains not only the final material composition, but also the parameters, sequence and operating knowledge required to reproduce and industrialize the result.
Device-Relevant Materials by Design
While many automated materials laboratories focus on powders or bulk compounds, NANOFABRICATOR® PRO is designed for functional thin films, interfaces, multilayer stacks and localized structures fabricated directly on substrates and devices.
This device-relevant approach enables new materials to be evaluated in the forms required for semiconductor process development, advanced packaging processes, electronics, sensors, photonics, quantum technologies, energy systems and other emerging applications.
Innovators can investigate how materials behave at interfaces, within device geometries and as part of a complete multilayer structure. These are often the conditions that determine whether a promising material can become a functional and manufacturable technology.
DALP® technology has already enabled solar cells to be fabricated with nanometre precision. Multiple functional material layers, device structures and process variations were created on a single substrate using one platform, demonstrating a more direct path from materials exploration to functional devices.
The significance extends beyond a single application. The same architecture can support materials and process development wherever interface control, localized structures and integration with existing device platforms are critical.
From Process Development to Industrial Transfer & Scalable Manufacturing
Industrial adoption requires controlled process windows, repeatable recipes and results that can be reproduced across equipment, sites and fabrication environments. A process must also integrate with the tools, substrates, standards and production systems already used by manufacturers.
NANOFABRICATOR® PRO connects materials discovery, device development and manufacturing-focused validation through a recipe-centric workflow. As a process is refined, its digital recipe becomes a transferable manufacturing asset.
This preserves the knowledge required to move from a promising result into joint development activities, pilot programs, qualified process modules and production-oriented fabrication flows. Instead of rebuilding the process at every stage, organizations can carry the recipe, operating conditions and validation data forward.
The TRL 9, SEMI-compliant industrial platform is designed to integrate into semiconductor R&D environments, fabrication facilities and advanced manufacturing lines. Its modular cluster-tool architecture enables localized DALP® technology processing to operate as part of broader semiconductor and advanced packaging process flows.
NANOFABRICATOR® PRO is designed to integrate with existing platforms rather than create another isolated fabrication environment. It can form part of a connected workflow that includes AI materials-design systems, characterization and metrology equipment, device-prototyping capabilities, additional processing technologies and established manufacturing infrastructure.
This ability to connect with existing systems is essential for industrial transfer. New materials must move beyond the development tool and become qualified steps within a complete manufacturing process.
NANOFABRICATOR® PRO does not replace established volume-manufacturing technologies. It transforms the development, validation and process-transfer work that precedes them. It enables teams to develop production-relevant recipes, demonstrate repeatability and prepare processes for integration with scalable manufacturing platforms.
The result is a more direct path from industrial R&D to process qualification, fab integration and scalable deployment.
AI-to-Matter Infrastructure for Physical AI: From Digital Discovery to Physical Validation
It provides the physical execution layer required to connect AI-driven materials discovery with programmable fabrication, experimental validation, device prototyping and industrial process development.
Digital material concepts can be translated into physical experiments, fabricated with atomic-scale precision, measured and refined within a connected innovation workflow. Successful results can then be preserved as digital recipes and prepared for transfer into broader manufacturing systems.
NANOFABRICATOR® PRO is designed to connect with existing AI models, metrology and characterization systems, device-prototyping platforms and additional processing technologies. This enables organizations to build AI-to-Matter workflows around their existing infrastructure.
The platform also provides the foundation for the next generation of self-driven materials systems. By integrating metrology, automated handling, additional processing capabilities and orchestration around NANOFABRICATOR® PRO, future platforms will be able to fabricate, measure and refine experiments with increasing levels of autonomy.
The objective is to create a connected physical infrastructure in which digital discovery, physical experimentation, validation and industrial transfer can operate as parts of one continuous process.
NANOFABRICATOR® PRO is where digital discovery becomes physical validation, and where AI-designed materials begin their journey into real-world technologies.
Closing the Loop: Enabling Recursive Innovation
NANOFABRICATOR® PRO provides the physical engine for a recursive innovation loop that connects digital design, programmable fabrication, integrated metrology, device testing and continuous learning.
Every authorized experiment can contribute valuable, structured data. Successful results, failed experiments, process boundaries and device-performance outcomes can all improve the next cycle.
The loop follows six connected stages:
- AI designs. AI models and researchers propose candidate materials, device architectures, process variations and performance targets.
- Digital objectives become executable recipes. Designs, materials and target properties are translated into software-defined DALP® fabrication instructions.
- NANOFABRICATOR® PRO builds. DALP® executes the process with localized, atomic-scale control, turning digital instructions into physical materials, structures and devices.
- Metrology and testing generate evidence. Integrated metrology, characterization and device testing measure material properties, interfaces, process performance and functional results.
- The platform learns. Experimental results are connected with the original recipe, process parameters and machine conditions to create a structured record of each cycle.
- The next cycle improves. AI models and development teams use the physical evidence to narrow the search, refine the design and accelerate the next experiment.
Within ATLANT 3D’s Physical AI architecture, A-Compiler is designed to translate digital designs and objectives into executable DALP® recipes. A-Cloud is designed to structure the resulting physical data so that models and development teams can learn from every authorized cycle.
This recursive innovation model operates across multiple levels:
Material Loop:
Material composition, synthesis conditions and measured properties inform the next experiment. Each cycle improves the understanding of how a material should be fabricated and where its useful process window exists.
Device Loop:
Material stacks and device architectures are fabricated, tested and refined. Device-performance results inform updates to layer sequences, interfaces, geometries and process conditions.
Factory Loop:
Validated processes can be evaluated against manufacturing requirements such as repeatability, yield, throughput and cost. The results support process optimization, control-policy updates and transfer into scalable manufacturing environments.
Network Loop:
Through A-Hub, ATLANT 3D’s autonomous materials foundry, NANOFABRICATOR® platforms can connect physical experimentation with global partner networks. Governed data sharing enables approved insights to improve development across equipment and locations while protecting customer data, confidentiality and intellectual property.
NANOFABRICATOR® PRO is designed to integrate with existing AI models, metrology systems, characterization tools, processing equipment and manufacturing platforms. This allows organizations to build recursive innovation workflows around their existing infrastructure.
As integrated metrology, automated handling and orchestration capabilities are added, these workflows can operate with increasing levels of autonomy. This creates the foundation for next-generation self-driven platforms that can design, build, qualify, learn and improve through every cycle.
Recursive innovation therefore connects materials, devices, manufacturing processes and distributed infrastructure within one continuously improving system.
The objective is not simply to run more experiments. It is to make every experiment improve the next decision.
Every cycle generates physical evidence.
Every result strengthens the learning system.
Every validated recipe creates a clearer path to scalable manufacturing.
AI-to-Matter
Infrastructure
NANOFABRICATOR® PRO connects AI-driven discovery, atomic-scale fabrication, validation and process transfer within one industrial platform. Its integration with existing systems and metrology creates the foundation for recursive learning and next-generation self-driven materials platforms.
AI can imagine the materials of the future.
NANOFABRICATOR® PRO makes them real.