mpw-005 / DNM Deep N-Well

the DNM shapes of all of mpw-005

40 of 40 dies rendered.

20,309 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (4.7 MB in total, largest 120.5 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_DNM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_DNM_reticle.png
the stitched reticle, full resolution
3.7 MB ce9f70b47ffaacc9812642a56806183b51efe2e04476d8136402ea8ee1d0b36354d87299 55a72e7ab361388898f59d4abe4cfbebedefa88c594f31b52d5df619b24bbf61
mpw-005_DNM_reticle_overview.png
the same image scaled to fit 4096 px
189.4 kB e2f7572c4cd6603e333e9f436efe07c5bf498e619087c183232e37f6c5ac9e7c9bfa348c 67259eca48ac8532ee07fade4017ab10e106fcfc56add9e7614500e230473bc6
mpw-005_DNM_tiles.json
where every tile belongs, and its checksum
13.5 kB e7e23cda24156049b46d48c8114438ebfeadad82c1bc31549497a6d35fc54b75685d780c 24b6bccc3790e4a57f3067105447368b8476d52f5c47dc08389ecd1ae2844cc4
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 111.4 kB3c81020a08d7f97f9e6e6f7c562ca13bfe8778aa6fe164fcf608f98fb2e16053
A2 mpw5_cache 7576×107767576, 0 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 112.4 kBae569d09c0e590b2c652b8a2d2c47840dd32def78c6a4ee6871b8f3374f45fc1
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 112.4 kB6bb1572d8a3353ad808cdf9dca0099791489e659146ef74de83823c0805b2eae
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 112.4 kBae569d09c0e590b2c652b8a2d2c47840dd32def78c6a4ee6871b8f3374f45fc1
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
B2 microwatt_mpw5 7576×107767576, 10776 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
B3 armleo_gpio_mpw5 7576×1077615152, 10776 112.6 kB48277260073683b6c377f036b1bfdd5de4be0e987f4e12f5851daaef7d1dafa2
B4 fabulous_efpga 7576×1077622728, 10776 112.6 kB58c614c70ec3285b4e03d196f3f080ebb096903e1bb4efdad733ff4242517d52
B5 i2c_controller 7576×1077630304, 10776 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
B6 lbist-mbist 7576×1077637880, 10776 112.6 kB10d17d66772b67c54999fb11dc44c19def85665d1d5b8681abf5602003e4ad72
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 116.5 kB3febd342bf9144720d6c929dda178ed28d5bad89c0299e5cd12ab1ef73e0275b
C1 riscduino-qcore__q0_ 7576×107760, 21552 112.5 kB5c0d63a3c5102bbafece95b1243e88a67556acbcc58b0592383266adbce6f891
C2 high_speed_vco 7576×107767576, 21552 111.4 kB3c81020a08d7f97f9e6e6f7c562ca13bfe8778aa6fe164fcf608f98fb2e16053
C3 openfasoc-cryo-gen 7576×1077615152, 21552 111.4 kB3c81020a08d7f97f9e6e6f7c562ca13bfe8778aa6fe164fcf608f98fb2e16053
C4 microwave_signal_generator 7576×1077622728, 21552 111.4 kB3c81020a08d7f97f9e6e6f7c562ca13bfe8778aa6fe164fcf608f98fb2e16053
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 115.5 kB71ae02506fa89ffc07ee2b0de743242b3de41913c3eb35bb872753beb891cb76
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 111.4 kB3c81020a08d7f97f9e6e6f7c562ca13bfe8778aa6fe164fcf608f98fb2e16053
D2 azadi_iii 7576×107767576, 32328 112.6 kB82877af85efef0a6da4919b3ecf562b5b1a6dfb3a9b42abc8e4c1d1bbc640be8
D3 tia_for_physic_experiments 7576×1077615152, 32328 111.8 kB44ea9707795da228986efc97c36286cf5243381878c7a501b8dc4abad3ded5b6
D4 qf105 7576×1077622728, 32328 112.4 kB7a6bc2c7a10011851b5c3e989ad3bfc3a7304b613af4eed99bd53111812cb867
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
D6 junga_soc_mpw5 7576×1077637880, 32328 112.5 kB47c63dc7c05b6461280b0c3eeead25db685048ad7571a3725087b947fb165c86
D7 nand_flash_mpw-5 7976×1077645456, 32328 116.5 kBfad61cacbe6a6a4c5ea762bc028ff9f89dace30a834e26ed826826eeab5f1c9a
E1 kasirga-c0-mpw-5 7576×107760, 43104 112.5 kB82ebcf4de771fb8ba4abce411f2e526c8ffec5fbb672fc4e0af228ba8b624ecb
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
E3 wirelessenergyharvesting 7576×1077615152, 43104 111.5 kBbcdd9ab7728446ec532ac2dafcca3cd6f2865ab90971ad069026e8714681d4eb
E4 caravel_raster_engine 7576×1077622728, 43104 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 111.4 kB3c81020a08d7f97f9e6e6f7c562ca13bfe8778aa6fe164fcf608f98fb2e16053
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 112.4 kB0540585d37360774d9814ee292a9c55bf357f00c52efc4686925e5ce5248c770
E7 alu 7976×1077645456, 43104 116.5 kBd7504f9e4894d532a654c61ce4f3525335cf5348e32102b4201e9b6006f30e65
F1 4ft4 7576×111760, 53880 116.2 kB9159a80b5f32d11628b9bfe19e7c8acb7a26788f02e837f5619c9a1a2ac869a3
F2 rng_chaos 7576×111767576, 53880 116.2 kB9159a80b5f32d11628b9bfe19e7c8acb7a26788f02e837f5619c9a1a2ac869a3
F3 uetrv-ecore 7576×1117615152, 53880 116.3 kB5f589f6e94a5ffe29a433b839d7489d4d1690971785ed6bec91df780a4f53a14
F4 systolic_array 7576×1117622728, 53880 116.2 kB9159a80b5f32d11628b9bfe19e7c8acb7a26788f02e837f5619c9a1a2ac869a3
F5 reram_test 7576×1117630304, 53880 115.2 kB0b508a56e895c92506815c8d7418308ffad833a9db70a26620b40b061b96ded4
F6 secure_memory 7576×1117637880, 53880 116.2 kB9159a80b5f32d11628b9bfe19e7c8acb7a26788f02e837f5619c9a1a2ac869a3
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 120.5 kB3629b8962a68c60a48765ff75aefc9dc12fa59b9f24e5fef55c13391e9f88b87

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About DNM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask DNM

Mask layer
DNM — Deep N-Well
Process step
7 — Deep N-well mask
Drawn layer
64:18
Layer on the mask-layer GDS
48:0
Detail
DNM = dnwell sized by cdnm.3
Used in SKY130
yes

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.