This paper proposes a residual amplifier (RA) with high open-loop gain, wide output swing, and integrated auto-zeroing (AZ) functionality. To satisfy the stringent relative gain error requirements of high-precision successive-approximation-register (SAR) analog-to-digital converters (ADCs), the RA employs a three-stage architecture to achieve enhanced open-loop gain while maintaining a broad output voltage range. Stability in the multi-stage design is ensured through the strategic placement of the second and third poles using the complex-pole method. The input stage incorporates a current-reuse technique to double the effective trans-conductance, reducing thermal noise and extending bandwidth without increasing static power consumption. Additionally, an AZ technique is integrated to suppress offset voltage and mitigate low-frequency 1/f noise. Simulated in a 40nm CMOS process, the opamp achieves an open-loop gain exceeding 130dB, and a loop gain of over 101 dB when configured as 61.5× switched-capacitor (SC) RA. The design delivers a bandwidth of 30 MHz, a phase margin greater than 60°, and an input-reference total noise of 14.8 μVrms, with a power consumption of 5.6 mW. These results demonstrate the RA’s capability to meet the demands of high-resolution pipelined SAR ADCs, combining precision, dynamic performance, and power efficiency.

Besides third-party packages, the Python interpreter and system libraries are also critical dependencies of a Python program. In our empirical study, 34% programs are only compatible with specific Python interpreter versions, and 24% programs require specific system libraries. However, existing techniques mainly focus on inferring third-party package dependencies. Therefore, they can lack other necessary dependencies and violate version constraints, thus resulting in program build failures and runtime errors.

Docker is the de-facto container technology for software system deployment and delivery. A Dockerfile specifies how to containerize a system into a Docker image. However, creating a Dockerfile is not trivial since resolving the dependencies (e.g., third-party libraries) of diverse software requires comprehensive domain knowledge. In this paper, we propose DockerGen to containerize software packages automatically. DockerGen constructs a knowledge graph containing rich knowledge of building Docker images by analyzing nearly 220 thousand Dockerfiles. DockerGen exploits the knowledge graph to containerize the target software by creating a Dockerfile specifying the base image, dependencies, and the operation workflow. We evaluate DockerGen on 100 software packages of various categories. DockerGen achieves a 73% build success rate and a 59% configuration success rate. The experimental result indicates it is viable to automate software containerization based on a domain knowledge graph.