Gradient Boost Dynamic Control for High-Voltage Power Supplies in Ion Implantation

Ion implantation technology, as a core process in semiconductor manufacturing, requires precision that directly impacts device performance and yield. As semiconductor devices shrink to nanoscale, traditional static high-voltage power supplies struggle to meet advanced process demands such as shallow junction implantation and multi-energy doping. Gradient boost dynamic control technology enables precise regulation of implantation depth and concentration by dynamically adjusting output voltage waveforms in real time, representing a breakthrough in process capabilities.
Technical Requirements for Gradient Boosting
The ion implantation process demands high-voltage power supplies to switch between multiple voltage levels within microseconds while maintaining stability (ripple <0.1%) and accuracy (±0.05% error). For example, ultra-shallow junction formation requires voltage escalation from 5kV to 30kV within milliseconds, while avoiding channeling effects necessitates non-linear voltage ramping profiles. Key challenges during dynamic operation include: 
• Load transient disturbances: Beam current fluctuations causing voltage oscillation 
• EMI issues: High-frequency switching-induced electromagnetic interference 
• Thermal stress accumulation: Instantaneous overloads in power devices during voltage transitions 
Core Implementation of Dynamic Control
1. Multi-phase Topology Architecture 
   An interleaved boost converter employs 4-8 parallel power modules with 180° phase-shifted operation, reducing input/output ripple current by 40% while extending power capacity to 60kV/500mA. The critical innovation lies in a voltage doubler-SEPIC hybrid structure: the doubler provides high-voltage gain, and the Single-Ended Primary Inductor Converter (SEPIC) ensures non-inverting output during voltage transitions. 
2. Dynamic Interface Technology 
   An SPI-based digital control architecture allows real-time voltage configuration via a 12-bit shift register. The controller dynamically adjusts PWM duty cycles according to preset gradient curves (e.g., exponential or S-shaped), achieving 10μs response time and supporting arbitrary waveform generation from 9V to 60V. Concurrently, Spread Spectrum Frequency Modulation (SSFM) expands switching frequencies up to 2MHz, significantly reducing EMI peaks at specific frequencies. 
3. Closed-Loop Feedback Mechanism 
   A triple-loop control system ensures stability: 
   • Voltage loop: Samples output via divider network and generates error signals 
   • Current loop: Compensates load variations using Hall-effect beam current sensors 
   • Dose loop: Adjusts implantation time based on Faraday cup dose integration 
   This multi-loop coordination achieves 99.5% output stability and ±0.8% dose accuracy. 
Advanced Control Algorithms
• Model Predictive Control (MPC): Utilizes power-load transfer function models to predict system states 5 cycles ahead, optimizing PWM parameters preemptively. MPC suppresses voltage overshoot below 0.3%. 
• Intelligent Compensation: Neural network-based disturbance observers identify nonlinearities using historical data (beam current, temperature, voltage). When lattice scattering intensifies, the system autonomously reduces boost slew rates to minimize crystal damage. 
• Adaptive PID: Employs high proportional gain (Kp=5.2) for rapid response during startup, switching to integral dominance (Ki=0.8) at steady state to eliminate offset. 
Advantages and Future Trends
This technology delivers three breakthroughs: 
1. Shallow junction precision: Doping concentration variation in 3nm junctions improves from ±15% to ±4.5%; 
2. Energy efficiency: Multi-phase topology reduces switching losses, achieving 92% system efficiency; 
3. Process integration: Single-step implantation accomplishes multi-energy profiles, reducing masking steps. 
Future developments will integrate wafer-level control systems. By analyzing lattice images from real-time TEM (Transmission Electron Microscopy), voltage gradient profiles will be dynamically reconstructed, ultimately enabling self-sensing, self-optimizing intelligent implantation systems.