Low-Dropout Regulators (LDOs) play an important role in enabling fine-grained supply-voltage domains for energy-efficient SoC design [1]. Digital LDOs are of particular interest due to integration and scalability advantages, but their transient response is slowed down by intrinsic limitations in sam-pled feedback systems. Design margins to ensure stability across worst-case PVT conditions further degrade transient response. Meanwhile, voltage domains continue to shrink in size, thus mandating a faster LDO response to compensate for reduced available decoupling capacitance (decap).
Recently reported non-linear control and event-driven architectures offer fast recovery times. However, non-linear approaches face the challenge of ensuring stable mode transitions under ran-dom load current (IL) conditions. Event-driven LDOs trigger logic to control MOS devices based on threshold crossings made by the regulated voltage (Vout). However, typical digital systems exhibit constant load fluctuation which can result in prohibitive switching losses. To address the impact of worst-case margining, adaptive LDO designs have also been proposed but they largely focus on suppressing Vout ripple and compensating for load current variation.
This work presents computational regulation, a technique for fast and stable transient response across PVT. This concept is demonstrated in a Digital LDO that drives a Cortex-M0 processor with an integrated linear algebra accelerator (Figure above). The key idea is to (1) derive time-domain models that are more accurate than those obtained from the traditional discrete-time transfer function and (2) evaluate the resulting state equations at runtime for rapid regulator response. We also introduce Au-tonomous Gain Tracking (AGT), a low-overhead, low-complexity technique that examines Vout statis-tics for runtime loop gain tuning to enable rapid LDO response across PVT.
In many respects, depending on context and the system in question, computational control can be viewed as a practical take on dead-beat control or as a (significantly) simplified implementation of model-predictive control.
Preliminary results on our research in this area will be presented at ISSCC 2019.