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Summary: A high frequency resolution digitally controlled oscillator (DCO) is proposed for wireless transceivers, in which the scheme adopts an optimized cascaded differential switched‐capacitor (SC) ladder and a low‐coupled eight‐shaped transformer. The frequency resolution is further improved by the low electromagnetic coupling effect between the primary and secondary coils of the on‐chip transformer, thus reducing quantization noise and phase noise. An eight‐shaped transformer designed using two coils cross‐connected each other is adopted to reduce the magnetic coupling and frequency pulling. The proposed DCO is implemented in a TSMC 180‐nm CMOS process, and a tuning range of 36.7% from 3.83 to 5.55 GHz with a frequency resolution of 17 kHz is achieved. The phase noise is −120.29 dBc/Hz at 1 MHz offset from the 3.83 GHz carrier frequency. The DCO consumes 9.26 mA from a 1.8 V supply, while the figure of merit with tuning range is −191 dBc/Hz. The chip size is 0.294 mm2. [ABSTRACT FROM AUTHOR]

Analog arithmetic operations are the most fundamental mathematical operations used in image and signal processing as well as artificial intelligence (AI). In‐memory computing (IMC) offers a high performance and energy‐efficient computing paradigm. To date, in‐memory analog arithmetic operations with emerging nonvolatile devices are usually implemented using discrete components, which limits the scalability and blocks large scale integration. Here, a prototypical implementation of in‐memory analog arithmetic operations (summation, subtraction and multiplication) is experimentally demonstrated, based on in‐memory electrical current sensing units using spin‐orbit torque (SOT) devices. The proposed structures for analog arithmetic operations are smaller than the state‐of‐the‐art complementary metal oxide semiconductor (CMOS) counterparts by several orders of magnitude. Moreover, data to be processed and computing results can be locally stored, or the analog computing can be done in the nonvolatile SOT devices, which are exploited to experimentally implement the image edge detection and signal amplitude modulation with a simple structure. Furthermore, an artificial neural network (ANN) with SOT devices based synapses is constructed to realize pattern recognition with high accuracy of ≈95%. [ABSTRACT FROM AUTHOR]