How MST’s Open-Source SECS/GEM Driver Is Changing Equipment Integration

Every semiconductor tool on every production line in every fab on the planet communicates using SECS/GEM — the SEMI Equipment Communications Standard and Generic Equipment Model. Developed in the 1980s and refined through dozens of SEMI standard revisions, SECS/GEM is the lingua franca that allows host systems to send recipes, receive alarms, collect process data, and control equipment state across tools from different manufacturers.

And yet, implementing SECS/GEM has traditionally been one of the most painful, expensive, and opaque tasks in semiconductor equipment engineering. Until now.

Why Is SECS/GEM Implementation So Difficult?

The SECS/GEM specification spans hundreds of pages across multiple SEMI standards: E4 (SECS-I transport), E5 (SECS-II message content), E30 (GEM behavior), E37 (HSMS transport), E40 (process management), E87 (carrier management), E90 (substrate tracking), E94 (control job management), E116 (equipment performance tracking), and more. Each standard defines message formats, state machines, and behavioral requirements that must be implemented precisely for interoperability.

Historically, equipment companies had three options. First, buy a commercial SECS/GEM SDK from vendors like Cimetrix, Brooks Automation, or PEER Group — licenses that typically cost $50,000-200,000 per product line plus annual maintenance fees. Second, implement the protocol from scratch — a project that consumes 6-18 months of senior engineering time and produces code that only the original developers can maintain. Third, cobble together incomplete implementations from forum posts and undocumented examples, resulting in equipment that passes basic connectivity tests but fails during complex production scenarios.

For large equipment makers like Applied Materials, Lam Research, or Tokyo Electron, the commercial SDK cost is negligible relative to revenue. But for the thousands of smaller equipment companies worldwide — specialty tool makers, retrofit integrators, academic labs, and startups — the cost and complexity of SECS/GEM implementation is a genuine barrier to market entry.

What Is secsgem-python and How Did It Originate?

secsgem-python is a fully open-source Python implementation of the SECS/GEM protocol stack, released under the MIT license. It provides a complete, well-documented, production-tested library for building both equipment (GEM) and host interfaces.

The project originated from MST’s internal need for a flexible SECS/GEM layer that could support the NeuroBox product line. MST’s AI systems need to communicate with semiconductor equipment to collect real-time sensor data and send control commands. Rather than licensing a commercial SDK — which would have created a dependency on third-party software at the core of MST’s product — the engineering team built a clean-room implementation in Python.

The decision to open-source it was strategic, not charitable. MST recognized that the semiconductor industry’s AI transformation depends on equipment connectivity. Every tool that cannot communicate effectively with AI systems is a tool that cannot benefit from AI. By lowering the SECS/GEM implementation barrier, MST accelerates the overall market for semiconductor AI — a market in which MST’s products compete on AI capability, not on protocol implementation.

How Does the Technical Architecture Work?

The library is structured in clean, modular layers that mirror the SECS/GEM standard hierarchy:

Transport Layer. Implements both SECS-I (RS-232 serial, for legacy equipment) and HSMS (TCP/IP, for modern equipment) with full connection state management, timeout handling, and multi-session support. The HSMS implementation handles T3, T5, T6, and T7 timer management, separate session establishment, and the control message handshake sequence that trips up many custom implementations.

Message Layer. Provides a complete SECS-II message encoder/decoder supporting all standard data types: binary, boolean, ASCII, JIS-8, signed and unsigned integers (1/2/4/8 byte), and floating point (4/8 byte). Messages are constructed using a Pythonic API that maps directly to SEMI standard nomenclature. Defining a new message type requires 5-10 lines of code rather than the hundreds of lines typical in C/C++ implementations.

GEM Layer. Implements the GEM state machine — the behavioral model that defines how compliant equipment responds to host commands. This includes communication state management (COMMUNICATING, NOT COMMUNICATING), control state management (ONLINE LOCAL, ONLINE REMOTE, OFFLINE), processing state management, and alarm/event reporting. The state machine implementation has been validated against the SEMI E30 standard using the complete set of 47 GEM compliance test scenarios.

Application Layer. Provides high-level abstractions for common equipment integration tasks: recipe management, process program upload/download, collection event configuration, dynamic report creation, and status variable access. A new equipment interface can be built in under 500 lines of Python code — a task that typically requires 5,000-15,000 lines in C or Java.

What Impact Has the Open-Source Community Had?

Since its public release, secsgem-python has grown into an active community project. Key metrics as of early 2026:

– 15,000+ total package downloads from PyPI
– 40+ contributing engineers from 12 countries
– 850+ GitHub stars
– Active deployment in production environments across 6 countries
– Support for Python 3.8 through 3.12 with comprehensive test coverage

Community contributions have expanded the library beyond MST’s original scope. Contributors have added E87 carrier management support, E40 process job management, improved documentation with interactive examples, and performance optimizations for high-frequency data collection scenarios.

The most significant community impact has been in education and training. University programs at NTU Singapore, KAIST, and several Chinese semiconductor training institutes have adopted secsgem-python as the teaching platform for equipment communication courses. Students can build a functional equipment simulator on a laptop in a single afternoon — an experience that previously required access to expensive commercial tools.

How Does Open-Source SECS/GEM Accelerate the AI Ecosystem?

The connection between an open-source protocol driver and the semiconductor AI ecosystem is direct and measurable.

Faster data pipeline development. AI models for semiconductor manufacturing require training data from equipment sensors. Every hour spent debugging SECS/GEM connectivity is an hour not spent on model development. secsgem-python reduces the equipment connectivity timeline from months to weeks, accelerating the entire AI deployment cycle.

Lower barrier for AI startups. A semiconductor AI startup can now prototype a complete data collection and model training pipeline without licensing a $100,000+ SECS/GEM SDK. This lowers the minimum viable capital required to enter the semiconductor AI market, increasing competition and innovation.

Standardized data formats. When the community converges on a common protocol library, the data formats and collection patterns become standardized. This makes it easier to share pre-trained models, benchmark results, and best practices across organizations — creating network effects that benefit the entire ecosystem.

Equipment maker adoption. Smaller equipment makers who previously could not afford SECS/GEM compliance can now build standards-compliant interfaces using secsgem-python. Each newly connected tool is a potential data source for AI systems, expanding the addressable market for semiconductor AI solutions including MST’s own products.

What Is the Roadmap for secsgem-python?

The project’s development roadmap reflects both community priorities and industry trends:

Near-term (2026): Complete E116 (equipment performance tracking) implementation, add EDA/Interface A support for advanced data collection, and release a graphical equipment simulator for training and testing.

Medium-term (2027): Integrate with the emerging SEMI OPC-UA companion specification, enabling dual-protocol support for fabs transitioning from SECS/GEM to OPC-UA. Add built-in data logging with export to common ML training data formats (Parquet, HDF5).

Long-term: Establish secsgem-python as the reference implementation that SEMI standards committees use for interoperability testing, similar to how the Linux kernel serves as the reference implementation for POSIX standards.

MST’s open-source SECS/GEM initiative embodies a strategic principle: in an ecosystem play, the company that removes friction for the entire ecosystem captures outsized value. By making equipment communication accessible to every engineer with a Python interpreter, MST is not just building a software library — it is building the infrastructure layer that the semiconductor AI revolution requires.