TetraMem Announces 22nm Multi-Level RRAM Analog In-Memory Computing SoC Milestone

Photograph of the MLX200 chip with a five-cent coin for size reference Business Wire

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SAN JOSE, Calif. — TetraMem Inc., a Silicon Valley–based semiconductor company developing analog in-memory computing (IMC) solutions, today announced the successful tape-out, manufacturing, and initial silicon validation of its MLX200 platform, a 22nm multi-level RRAM-based analog IMC system-on-chip (SoC).

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The achievement marks a significant step toward the commercialization of analog computing architectures based on emerging non-volatile memory technologies, addressing the growing challenges of data movement, power consumption, and thermal constraints in modern AI systems.

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As AI workloads continue to scale, system performance is increasingly constrained by the cost of moving data between memory and compute units. Analog in-memory computing offers a fundamentally different approach by performing computation directly within memory arrays, significantly reducing data movement and improving system-level efficiency. TetraMem’s MLX200 platform integrates multi-level RRAM arrays with mixed-signal compute engines to enable high-throughput vector-matrix operations within memory, while maintaining compatibility with advanced CMOS processes.

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The multi-level RRAM technology demonstrated at the TSMC 22nm process provides key attributes required for practical deployment, including CMOS compatibility with minimal additional process complexity, low-voltage and low-current operation, strong retention and endurance characteristics, and high multi-level capability that supports improved memory and compute density. Early silicon results indicate consistent functionality across arrays, supporting the viability of this approach for both embedded non-volatile memory and compute-in-memory applications.

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This milestone builds on TetraMem’s earlier work on the MX100 platform, fabricated on the TSMC 65nm CMOS process, where the company demonstrated multi-level RRAM devices with thousands of conductance levels (“Thousands of conductance levels in memristors integrated on CMOS,” Nature, March 2023), as well as high-precision analog computing capabilities (“Programming memristor arrays with arbitrarily high precision for analog computing,” Science, February 2024). These prior results established a strong scientific and engineering foundation for scaling the technology to more advanced nodes.

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Since 2019, TetraMem has worked closely with the world leading semiconductor foundry to advance RRAM technology from early-stage research into manufacturable silicon. The progress achieved at 22nm reflects continued development in process integration, device uniformity, and system-level co-design.

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The MLX200 and MLX201 platforms are designed to support power- and latency-sensitive edge AI applications, including voice and audio processing, wearable devices, IoT systems, and always-on sensing. Evaluated sampling is expected to begin in the second half of 2026, and multi-level RRAM memory IP is available for evaluation and potential licensing.

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Dr. Glenn Ge, Co-founder and CEO of TetraMem, commented, “This milestone reflects years of close collaboration with our foundry partner TSMC and demonstrates the feasibility of bringing multi-level RRAM and analog in-memory computing from computing architecture breakthrough into advanced-node commercial silicon. We believe this approach provides a practical path to improving energy efficiency and scalability for next-generation AI systems.”

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The successful realization of the MLX200 platform highlights the viability of multi-level RRAM-based analog computing on advanced semiconductor processes. TetraMem will continue to advance this technology to support emerging AI workloads with improved energy efficiency and system scalability.

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About TetraMem

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TetraMem is a Silicon Valley–based semiconductor company pioneering analog in-memory computing using multi-level RRAM technology. Its architecture integrates memory and compute to significantly reduce data movement and improve energy efficiency for AI workloads. With a strong foundation in device, circuit, and system co-design, TetraMem is advancing scalable solutions for edge AI and future high-performance computing, working closely with leading foundries and ecosystem partners to bring fundamental science breakthrough technologies into commercial variable volume production.

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Glenn Ge

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