Break the Black Box.
Don't buy a closed-source million-euro tool. Let us turn your existing vacuum chambers and legacy deposition hardware into a programmable, autonomous laboratory.
The Proprietary Trap
Legacy hardware OEMs build exceptional vacuum components but trap you in rigid, proprietary PLC (Programmable Logic Controller) architectures. They are designed for human operators to run static recipes, not for dynamic AI orchestration. If you cannot programmatically command your mass flow controllers or read real-time metrology via an open API, you cannot run active learning loops.
The Open-Architecture Imperative
Solbion champions an open-hardware ecosystem. We bypass proprietary software limitations by retrofitting your existing PVD, ALD, or Spark Ablation tools with custom mechatronics. We expose every valve, sensor, and motor to a unified, Python-based middleware layer, bridging the physical world with modern computational materials science.
Building the Digital Twin
1. Physical Hardware
LAYER_01The raw synthesis and characterization layer. We utilize commoditized or existing facility hardware to minimize CapEx.
2. Edge Control & Mechatronics
LAYER_02We replace or parallel-wire proprietary PLCs with high-frequency, open-source edge controllers that act as translators for physical signals.
3. The Solbion Middleware
LAYER_03A centralized daemon that creates a 'Digital Twin' of the hardware. It synchronizes substrate movement with precise gas dosing and logs every environmental variable.
4. Active Learning Engine
LAYER_04The 'Brain'. It reads the metrology data from Layer 3, predicts the optimal next experiment, and commands the Middleware to execute the run autonomously.
How We Upgrade Your Lab
Phase 1: Hardware & API Audit
We assess your existing tools (or spec used equipment for procurement). We map out the required I/O interfaces, UHV stepper motor integration for combinatorial masks, and metrology feedback loops.
Phase 2: Mechatronic Retrofit
Our engineering team installs the edge control layer, bypassing rigid PLCs and bringing all mass flow controllers, power supplies, and sensors into a unified digital network.
Phase 3: Middleware Deployment
We deploy the Solbion Python orchestrator locally. Your machine is now a node on a network, addressable via simple code commands.
Phase 4: Handoff & ML Integration
We hand over the API documentation. Your computational scientists can immediately begin running continuous, closed-loop discovery campaigns using their preferred ML frameworks.
Take command of your hardware.
Stop adapting your R&D to the limitations of proprietary software. Let us build the middleware that sets your discovery loops free.
Request an Integration Audit