MEMPHIS Electronic GmbH
Basler Str. 5
61352 Bad Homburg
Germany
Phone: +49 6172 90350
Email: info@memphis.de
MEMPHIS Electronic GmbH
Basler Str. 5
61352 Bad Homburg
Germany
Phone: +49 6172 90350
Email: info@memphis.de
Air pollution, particularly in the form of fine particulate matter (with a diameter below ≤2.5 µm, PM2.5), is associated with an increased risk of respiratory, cardiovascular, and neurological disorders. Smart cities require pervasive, real-time environmental intelligence to mitigate these risks. Sensor miniaturization has advanced to a point where accurate PM detection can be embedded into street fixtures, consumer devices, and wearable platforms. However, sensing alone is insufficient.
To transition from raw data acquisition to actionable insights, sensor systems must evolve into edge-intelligent nodes—capable of local inference, robust storage, and intermittent communication. Conventional memory technologies (e.g., Flash or DRAM) are ill-suited due to volatility, limited endurance, and high energy demands. This compels the use of non-volatile, high-endurance, and fast-access memories.
Embedded AI enables event-based sensing, data compression through local classification, and contextual behavior like adaptive sampling. TinyML models using TensorFlow Lite or Syntiant frameworks can be trained offline and deployed on neural accelerators or MCU-integrated AI engines with <1 mW power.
This architecture supports synchronous and asynchronous operation, enabling efficiency and resilience.
To ensure successful deployment and long-term operability, several Critical-to-Quality (CTQ) parameters must be established. These include:
Component selection must balance performance, energy efficiency, and integration flexibility.
Traditional Flash memory suffers from slow write cycles, limited write endurance, and high power requirements. DRAM, while fast, is volatile and requires constant refresh cycles, making it impractical for IoT edge deployments.
MRAM utilizes magnetic tunnel junctions offering virtually infinite write endurance (>10¹⁵ cycles), non-volatility, fast access (~35 ns), and radiation resistance. These make MRAM ideal for real-time logs, model updates, and event storage.
FeRAM operates by switching the polarization of a ferroelectric capacitor, offering low write energy, fast access, and moderate endurance (10⁷–10⁹ cycles), ideal for periodic logs such as calibration or maintenance records.
STT-MRAM ArchitectureTraditional Flash memory suffers from slow write cycles, limited write endurance, and high power requirements. DRAM, while fast, is volatile and requires constant refresh cycles, making it impractical for IoT edge deployments.
MRAM utilizes magnetic tunnel junctions offering virtually infinite write endurance (>10¹⁵ cycles), non-volatility, fast access (~35 ns), and radiation resistance. These make MRAM ideal for real-time logs, model updates, and event storage.
FeRAM operates by switching the polarization of a ferroelectric capacitor, offering low write energy, fast access, and moderate endurance (10⁷–10⁹ cycles), ideal for periodic logs such as calibration or maintenance records.
STT-MRAM Architecture
Netsol provides high-endurance STT-MRAM modules optimized for edge AI devices with industrial-grade retention, small form factor, and extended temperature support.
Ramxeed offers FeRAM-based ASICs and embedded memory platforms suitable for ultra-low-power applications such as wearables, medical devices, and specialized IoT tags. Their modules are often integrated into systems requiring microsecond-level write latency and minimal energy footprint.
Together, these vendors form the backbone of memory innovation within the AIoT ecosystem.
The convergence of optical sensing, embedded AI, and non-volatile memory forms a resilient, scalable platform for air quality monitoring. MRAM and FeRAM provide not only endurance and data safety but also empower intelligent inference under constrained energy budgets.
By designing architectures around these technologies, smart cities can deploy autonomous sensor grids that adapt, learn, and persist, ensuring long-term environmental intelligence
Contact our technical sales team to request engineering samples, integration support, or design-in consultations.
The proposed architecture is modular and comprises:
This configuration ensures autonomous functioning, secure data preservation, and rapid inferencing even when disconnected from the network or power supply.
System architecture outlineThe proposed architecture is modular and comprises:
This configuration ensures autonomous functioning, secure data preservation, and rapid inferencing even when disconnected from the network or power supply.
System architecture outline