ADXL05 MEMS Accelerometer Teardown: 1996 Single-Axis IC Analysis

This webinar presents five advanced failure analysis case studies demonstrating sophisticated analytical techniques for semiconductor and electronic device characterization: (1) Circuit modification using focused ion beam for reverse engineering, (2) Embedded defect analysis combining FIB, TEM, EDS, and EELS to identify oxidized nickel at layer interfaces, (3) Copper oxidation analysis employing optical microscopy, AFM, FIB, TEM, AES, XPS, and EELS to characterize oxide thickness and chemical states, (4) Surface contamination analysis using XPS survey scan and step profile to detect chromium and organic silicon contaminants, and (5) ToF-SIMS analysis for detecting surface contaminants at the parts per billion level. The presentation emphasizes that successful failure analysis requires selecting appropriate characterization techniques based on the analytical requirements, with different tools providing complementary information about elemental composition, chemical states, and structural characteristics of materials.

Scanning Electron Microscopy (SEM) uses a focused electron beam to image sample surfaces with 1-4 nm resolution, utilizing secondary electrons for topography, backscatter electrons for Z-contrast imaging, and characteristic X-rays for elemental analysis; the technique employs thermionic, field emission, or Schottky electron sources with electromagnetic lenses and various detectors including Everhart-Thornley, backscatter, EDS, WDS, EBSD, and cathodoluminescence systems, with resolution limited by chromatic aberration, spherical aberration, and diffraction effects that operators can partially correct through stigmator adjustments; sample charging in insulators is mitigated through coating, E2 point matching, or variable pressure/environmental SEM modes; the complementary Focused Ion Beam (FIB) technique enables precision milling, deposition, and TEM sample preparation through liquid metal ion sources with electrostatic lenses, allowing cross-sectional analysis and three-dimensional reconstruction of materials at nanoscale resolution.

Microelectronics reverse engineering involves systematically disassembling consumer electronics to analyze chips at multiple scales—from chip identification and disassembly to circuit reverse engineering and structural/materials analysis using advanced tools like scanning electron microscopes and ion beam mills, enabling detailed examination of chip layers as thin as one-thousandth of a human hair to reveal internal structures, dimensions, and materials used in semiconductor manufacturing.

Effective sample preparation for scanning electron microscopy (SEM) analysis of advanced semiconductor devices requires careful selection of techniques to minimize damage and preserve material integrity; mechanical polishing can cause surface damage up to three times the abrasive grit size, while focused ion beam (FIB) milling induces structural damage through atomic displacement and thermal effects, particularly problematic for thermally sensitive materials, making broad ion beam milling and automated delayering techniques essential for achieving artifact-free cross-sections and large-area layer removal with high uniformity.

Semiconductor failure analysis is a systematic process involving three main categories—electrical, physical, and material analysis—to identify root causes of device failures, utilizing various tools such as X-ray imaging, focused ion beam (FIB), and electron microscopy for defect localization and characterization.

This section presents two fundamental signal conditioning circuits for sensor interfaces. The first circuit is a current-to-voltage converter that adapts a current source (equivalent to the sensor) to the measurement chain, with output voltage VM = -R × I. The second circuit is a charge amplifier that delivers voltage proportional to charge and independent of sensor capacitance, with VM = -Qc × Cf/Cc. Both circuits use operational amplifiers with virtual ground concepts to achieve precise signal conditioning. The charge amplifier's independence from sensor capacitance makes it particularly valuable for capacitive sensors where cable capacitance variations would otherwise affect measurements.

Signal conditioning circuits are essential for converting raw sensor outputs into usable electrical signals. Three fundamental circuits serve different sensing modalities: (1) Wheatstone bridges amplify tiny resistance changes in piezoresistive sensors by creating unbalanced bridge conditions where output voltage V_out = (delta R/R) × V_in, enabling detection of micro-ohm-level resistance variations; (2) Switched capacitor circuits measure capacitive sensors by charging a known capacitor and transferring charge to the unknown capacitor, calculating capacitance from the resulting voltage; (3) Phase Lock Loops track frequency shifts in resonant sensors by comparing input frequency with a Voltage Controlled Oscillator, adjusting to maintain lock and detecting frequency changes indicative of applied forces or accelerations. These circuits form the bridge between physical phenomena and electronic processing in microsystems.

This lecture by Prof. S Sen from IIT Kharagpur's Department of Electrical Engineering covers essential signal conditioning techniques for capacitive sensors, including push-pull configurations in low-input impedance amplifiers, high-input impedance capacitance amplifiers, feedback-type capacitive amplifiers, and methods to mitigate parasitic capacitance effects in measurement systems.

Microsensor signals exhibit defining characteristics requiring specialized interface electronics: extremely low amplitude in microvolt range, part-per-million level parameter changes, analog nature requiring ADC conversion, cross-parameter sensitivities where secondary effects (temperature) influence output, individual device output isolation needs, offset and linearity errors, and time-dependent output drift due to aging. Amplifiers serve as the primary signal conditioning block for microsensors producing DC signals in the microvolt range. Differential amplifier configurations are preferred for their ability to amplify DC signals while rejecting common-mode interference and power supply noise. However, offset voltage occurs due to component mismatch between matched pairs, with CMOS implementations showing 10-100 times worse performance than bipolar devices. Drift arises from temperature variations, aging, and mechanical stress during packaging. 1/f noise dominates at low frequencies in MOSFETs and is inversely related to frequency, making it particularly problematic for DC-coupled sensor interfaces.

Sensor outputs are often very tiny electrical signals requiring internal amplifiers to produce usable outputs. This amplification is one form of signal conditioning. Common sensor output formats include voltage signals (0-5V, 0-10V, -10V to +10V) and current signals (4-20mA). Signal conditioners may be external or integrated into sensors/systems.

MEMS sensors evolved from simple accelerometers enabling smartphone portrait/landscape functionality to sophisticated multi-sensor systems. Early 4mm x 4mm form factors have decreased while accuracy improved, requiring careful accuracy-power trade-offs. MEMS technology converged across personal electronics, automotive (airbag deployment, electronic stability), and industrial sectors. Traditional bulky, expensive technologies were replaced by scalable MEMS solutions. Applications include Pokemon Go navigation, autonomous vehicle safe stopping requiring 20-centimeter accuracy, and biotechnology drug delivery. The technology enables capabilities previously impossible while maintaining affordable costs.

Micro-Electro-Mechanical Systems (MEMS) are microscopic devices that integrate mechanical elements, sensors, actuators, and electronics on a common silicon substrate, enabling revolutionary applications across diverse fields including consumer electronics (smartphones, gaming controllers), automotive systems (airbags, tire pressure monitoring), healthcare (pacemakers, diagnostic devices), environmental monitoring, and military/aerospace systems, where their miniaturization, low power consumption, and batch fabrication capabilities provide significant advantages over traditional macro-scale solutions.

MEMS applications span automotive (airbags, suspensions), consumer electronics (smartphones), biomedical (lab-on-chip, organ-on-chip), aerospace (gyroscopes for navigation), industrial (shock testing), and sports performance monitoring. Common MEMS devices include accelerometers, gyroscopes, pressure sensors, micro mirrors, and grippers. Silicon-based devices are common, while PDMS is preferred for soft microfluidic applications.

Smartphones contain multiple MEMS devices including mirrors for autofocus, display enhancement components, switches, tuners, oscillators, speakers, temperature sensors, imagers, haptic interfaces, humidity sensors, and pressure sensors for environmental sensing. In automotive applications, airbags (legally required), microphones for noise cancellation, distance detection systems for collision avoidance, airflow sensors, brake pressure sensors, tire pressure monitoring systems (TPMS), and exhaust sensors are essential. Autonomous driving requires even more sophisticated sensor suites.

MEMS (Microelectromechanical Systems) combines electronics with microscopic mechanical structures like membranes, levers, and mirrors on a single chip. Manufacturing uses semiconductor techniques including thin film growth, lithography, and etching, but creates three-dimensional structures. The technology translates mechanical changes into electrical signals and vice versa. Originating from 1960s piezoresistive sensors, MEMS gained mass production in the 1990s with HP's inkjet cartridges, then exploded in smartphones and cars. Key applications span consumer electronics (motion sensors, microphones, speakers, VR displays), automotive systems (airbags, tire pressure monitoring, stabilization), and healthcare (wearable sensors for heart rate, temperature, breathing). Piezoelectric materials like PZT and Scandium-doped aluminum trioxide now enable more efficient sensors and actuators.